diff --git "a/docs/assets/resources.json" "b/docs/assets/resources.json"
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+++ "b/docs/assets/resources.json"
@@ -1 +1 @@
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Resource-specific angle: Self-referential agents that fold task-solving and self-modification into editable programs, extending the Darwin Godel Machine toward open-ended self-improvement, the loop where an agent rewrites its own improvement mechanism across runs.","impact":"Gives readers a concrete source in Hyperagents when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2603.19461; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jenny Zhang; Bingchen Zhao; Wannan Yang; Jakob Foerster; Jeff Clune; Minqi Jiang; Sam Devlin; Tatiana Shavrina","publication_date":"2026-03-19","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code at https://github.com/facebookresearch/Hyperagents","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.19461","date_added":""},{"row_id":"ale-0115","title":"PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks","url":"https://arxiv.org/abs/2512.03549","canonical_url":"https://arxiv.org/abs/2512.03549","annotation":"Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.","key_contribution":"Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.","novelty":"The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Hierarchical plan-execute-assess loops that detect and correct strategic errors during multi-hour autonomous runs.","impact":"Gives readers a concrete source in PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2512.03549; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yuki Orimo; Iori Kurata; Hodaka Mori; Ryuhei Okuno; Ryohto Sawada; Daisuke Okanohara","publication_date":"2025-12-03","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2512.03549","date_added":""},{"row_id":"ale-0116","title":"When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents","url":"https://arxiv.org/abs/2603.17104","canonical_url":"https://arxiv.org/abs/2603.17104","annotation":"Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","key_contribution":"Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","novelty":"The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Measures how agents drift from intent when specifications arrive incrementally across a long loop, and proposes a mitigation that recovers most of the loss.","impact":"Gives readers a concrete source in When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2603.17104; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Lu Yan; Xuan Chen; Xiangyu Zhang","publication_date":"2026-03-17","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.17104","date_added":""},{"row_id":"ale-0117","title":"Reflexion code","url":"https://github.com/noahshinn/reflexion","canonical_url":"https://github.com/noahshinn/reflexion","annotation":"Reference implementation and experiments for verbal reinforcement loops.","key_contribution":"Reference implementation and experiments for verbal reinforcement loops.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Reference implementation and experiments for verbal reinforcement loops.","impact":"Gives readers a concrete source in Reflexion code when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Inspectable GitHub source (3,205 stars; 312 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2023-03-22","publication_year":"2023","publication_venue":"noahshinn/reflexion","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"noahshinn/reflexion","github_stars":"3205","arxiv_id":"","date_added":""},{"row_id":"ale-0118","title":"Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting","url":"https://arxiv.org/abs/2607.00038","canonical_url":"https://arxiv.org/abs/2607.00038","annotation":"Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","key_contribution":"Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Position paper that formalizes the loop specification (trigger, goal, verification step, stopping rule, memory) as a reusable artifact handed to an agent harness, with a taxonomy, a five-level verification ladder, and a hand-coded analysis of fifty real-world loops.","impact":"Gives readers a concrete source in Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.00038; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"objective;trigger;context;verification;exit","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Sandeco Macedo","publication_date":"2026-06-28","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00038","date_added":""},{"row_id":"ale-0119","title":"From Question Answering to Task Completion: A Survey on Agent System and Harness Design","url":"https://arxiv.org/abs/2606.20683","canonical_url":"https://arxiv.org/abs/2606.20683","annotation":"Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","key_contribution":"Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Survey that decomposes the agent execution harness into six runtime responsibilities (observation, context, control, action, state, verification) and argues task performance emerges from the interaction of model, runtime, task structure, and evaluation rather than the model alone.","impact":"Gives readers a concrete source in From Question Answering to Task Completion: A Survey on Agent System and Harness Design when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2606.20683; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"context;verification;state;exit","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jianyuan Guo; Zhiwei Hao; Chengcheng Wang; Cheng Fan; Tingzhang Luo; Hongguang Li; Ying Gao; Hefei Mei; Jiankun Peng; Rongjian Xu; Minjing Dong; Han Wu; Mengyu Zheng; Kai Han; Shiqi Wang; Chang Xu; Yunhe Wang","publication_date":"2026-06-14","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.20683","date_added":""},{"row_id":"ale-0120","title":"MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems","url":"https://arxiv.org/abs/2605.22794","canonical_url":"https://arxiv.org/abs/2605.22794","annotation":"Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","key_contribution":"Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Self-evolution loop where the agent rewrites its own source code, with each change anchored to a production failure and accepted only after deterministic replay verification with rollback, lifting a four-task mean grader score from 0.25 to 0.61 without human intervention.","impact":"Gives readers a concrete source in MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2605.22794; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification;state;escalation","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Qianshu Cai; Yonggang Zhang; Xianzhang Jia; Huajiang Zheng; Wei Xue; Jun Song; Xinmei Tian; Yike Guo","publication_date":"2026-05-21","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"12 pages, 3 figures, 2 tables. Preprint. Code: https://github.com/hkgai-official/Moss","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.22794","date_added":""},{"row_id":"ale-0121","title":"METR Time Horizon 1.1","url":"https://metr.org/blog/2026-1-29-time-horizon-1-1/","canonical_url":"https://metr.org/blog/2026-1-29-time-horizon-1-1/","annotation":"Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","key_contribution":"Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Update to METR's time-horizon methodology, expanding the task suite to 228 tasks (31 at 8+ hours), migrating to the open-source Inspect framework, and revising the post-2023 capability doubling time to roughly 131 days.","impact":"Gives readers a concrete source in METR Time Horizon 1.1 when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Contextual source from metr.org; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-01-29","publication_year":"2026","publication_venue":"METR Blog","publisher":"metr.org","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0122","title":"MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution","url":"https://arxiv.org/abs/2607.05297","canonical_url":"https://arxiv.org/abs/2607.05297","annotation":"Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","key_contribution":"Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Two-timescale recursive self-improvement where a fast loop rewrites task skills from execution traces while a slow loop evolves the meta-skill governing improvement itself, gaining up to 23.5 points on OfficeQA, SealQA, and ALFWorld.","impact":"Gives readers a concrete source in MetaSkill-Evolve: Recursive Self-Improvement via Two-Timescale Meta-Skill Evolution when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.05297; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Zefeng Wang; Minxi Yan; Jinhe Bi; Sikuan Yan; Volker Tresp; Yunpu Ma","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05297","date_added":""},{"row_id":"ale-0123","title":"SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe","url":"https://arxiv.org/abs/2607.03451","canonical_url":"https://arxiv.org/abs/2607.03451","annotation":"Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","key_contribution":"Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Formalizes agent skill self-evolution as zeroth-order optimization and distills it into a minimal pipeline of file-system trajectory exploration, consensus attribute mining, and independent validation gating, letting a smaller model surpass larger ones on LiveMath and SpreadsheetBench.","impact":"Gives readers a concrete source in SkillOpt-Lite: Better and Faster Agent Self-Evolution via One Line of Vibe when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.03451; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yifei Shen; Bo Li; Xinjie Zhang","publication_date":"2026-07-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.03451","date_added":""},{"row_id":"ale-0124","title":"Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops","url":"https://arxiv.org/abs/2607.07663","canonical_url":"https://arxiv.org/abs/2607.07663","annotation":"Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","key_contribution":"Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Survey of 1,250 arXiv papers from 2024-2026 organized along two axes, what a self-improvement loop improves and its degree of loop closure, separating bounded evaluable self-refinement from open-ended recursive self-improvement.","impact":"Gives readers a concrete source in Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07663; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"whole-loop","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Mingguang Chen; Licheng Wang; Bo Qu","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"42 pages, 6 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07663","date_added":""},{"row_id":"ale-0125","title":"From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents","url":"https://arxiv.org/abs/2607.07321","canonical_url":"https://arxiv.org/abs/2607.07321","annotation":"EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","key_contribution":"EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: EvoSOP has agents distill recurring execution trajectories into reusable standard operating procedures and iteratively optimize the toolset through a construction, merging, evaluation, and pruning lifecycle.","impact":"Gives readers a concrete source in From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07321; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Haipeng Ding; Yuexiang Xie; Zhewei Wei; Yaliang Li; Bolin Ding","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07321","date_added":""},{"row_id":"ale-0126","title":"TTHE: Test-Time Harness Evolution","url":"https://arxiv.org/abs/2607.08124","canonical_url":"https://arxiv.org/abs/2607.08124","annotation":"Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","key_contribution":"Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","novelty":"Connects Loop Engineering to prior agent-loop and feedback-loop research. Resource-specific angle: Adapts LLM agents at test time by evolving a population of candidate harnesses (the executable control program around the model) from execution traces, using a label-free agentic proposer and judge to sustain improvements on text-to-SQL and competitive programming while flagging execution-derived proxy reliability as the key open challenge.","impact":"Gives readers a concrete source in TTHE: Test-Time Harness Evolution when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.08124; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jun Nie; Yonggang Zhang; Jun Song; Qianshu Cai; Dahai Yu; Yike Guo; Xinmei Tian; Bo Han","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"15 pages, 5 figures","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08124","date_added":""},{"row_id":"ale-0127","title":"DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment","url":"https://arxiv.org/abs/2607.07820","canonical_url":"https://arxiv.org/abs/2607.07820","annotation":"Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","key_contribution":"Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Introduces DeepSearch-Evolve, where a deep search agent improves by self-distilling its own trajectories inside a deterministic 420K-task verifiable environment, progress verification, grounded reflection, and failure recovery replace teacher trajectories and sparse RL reward, lifting a 9B model to 31.2% BrowseComp and 61.5% GAIA.","impact":"Gives readers a concrete source in DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07820; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xinyu Geng; Xuanhua He; Sixiang Chen; Yanjing Xiao; Fan Zhang; Shijue Huang; Haitao Mi; Zhenwen Liang; Tianqing Fang; Yi R. 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Resource-specific angle: Statistical analysis of 433 issues plus controlled multi-model experiments showing LLM repair agents succeed more when bug reports carry reproduction scripts, fix suggestions, and fault-localization cues, while longer natural-language reports correlate with lower success, directly informing how a loop's work-discovery step should specify tasks before dispatching agents.","impact":"Gives readers a concrete source in What Makes a Good Bug Report for an AI Agent? when they need to understand the evidence, vocabulary, and lineage behind recurring agent systems.","signal":"Research source arXiv:2607.07593; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Learn","user_goal":"Understand the field and its boundaries.","section":"Research Foundations","section_slug":"research-foundations","lifecycle_stages":"intake","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Lara Khatib; Noble Saji Mathews; Meiyappan Nagappan; Pengyu Nie; Thomas Zimmermann","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07593","date_added":""},{"row_id":"ale-0129","title":"AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution","url":"https://arxiv.org/abs/2607.08252","canonical_url":"https://arxiv.org/abs/2607.08252","annotation":"Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","key_contribution":"Names self-locking as a runtime failure mode of continuing agent loops, where accumulated state and history pull generation toward stale repetition (over 95% rolling action-repetition across an eight-model 40-day stress test), and proposes a multi-timescale loop that admits divergent material only through evidence-governed absorption, cutting macro-theme repetition from 61.8% to 36.3%.","novelty":"State persistence is explicit enough for repeated runs and handoff. 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Resource-specific angle: CLI that runs a planner, an implementer, and independent validators in isolated environments, looping until a change is verified or rejected with reproducible failures.","impact":"Gives readers a concrete source in zeroshot when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,646 stars; 140 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-12-25","publication_year":"2025","publication_venue":"the-open-engine/zeroshot","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"the-open-engine/zeroshot","github_stars":"1646","arxiv_id":"","date_added":""},{"row_id":"ale-0194","title":"Loki Mode","url":"https://github.com/asklokesh/loki-mode","canonical_url":"https://github.com/asklokesh/loki-mode","annotation":"Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","key_contribution":"Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Autonomous spec-to-app loop that runs Reason-Act-Reflect-Verify cycles behind quality gates, with completion gated by a blind three-reviewer council and a deterministic evidence receipt that rejects empty diffs and failing tests.","impact":"Gives readers a concrete source in Loki Mode when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,018 stars; 200 forks; NOASSERTION license; updated 2026-07-15); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification;state;exit","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-12-26","publication_year":"2025","publication_venue":"asklokesh/loki-mode","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"asklokesh/loki-mode","github_stars":"1018","arxiv_id":"","date_added":""},{"row_id":"ale-0195","title":"Looper","url":"https://github.com/ksimback/looper","canonical_url":"https://github.com/ksimback/looper","annotation":"Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","key_contribution":"Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Claude Code skill for designing review-gated agent loops before running them, coaching the user into a portable loop.yaml spec with explicit goals, typed verification, iteration caps, and budget limits, then emitting artifacts runnable in-session or via an external Python runner.","impact":"Gives readers a concrete source in Looper when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (684 stars; 61 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"objective;verification;budget","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-18","publication_year":"2026","publication_venue":"ksimback/looper","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"ksimback/looper","github_stars":"684","arxiv_id":"","date_added":""},{"row_id":"ale-0196","title":"Agent Apprenticeship","url":"https://github.com/Forsy-AI/agent-apprenticeship","canonical_url":"https://github.com/Forsy-AI/agent-apprenticeship","annotation":"Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","key_contribution":"Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","novelty":"The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Multi-backend ecosystem where apprentice agents complete tasks through workflow loops, mentors or humans verify results, and execution traces are compiled into a published dataset that feeds future agent improvement.","impact":"Gives readers a concrete source in Agent Apprenticeship when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (1,316 stars; 56 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-19","publication_year":"2026","publication_venue":"Forsy-AI/agent-apprenticeship","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Forsy-AI/agent-apprenticeship","github_stars":"1316","arxiv_id":"","date_added":""},{"row_id":"ale-0197","title":"Scholar Loop","url":"https://github.com/renee-jia/scholar-loop","canonical_url":"https://github.com/renee-jia/scholar-loop","annotation":"Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","key_contribution":"Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Autonomous multi-agent research loop from literature to hypothesis to real ML experiments to write-up, scoring every checkable agent claim against frozen ground-truth metrics and shipping an adversarial cheater engine that probes the loop for reward-hacking gaps.","impact":"Gives readers a concrete source in Scholar Loop when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (461 stars; 35 forks; MIT license; updated 2026-07-08); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-15","publication_year":"2026","publication_venue":"renee-jia/scholar-loop","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"renee-jia/scholar-loop","github_stars":"461","arxiv_id":"","date_added":""},{"row_id":"ale-0198","title":"loop-engineering (Cobus Greyling)","url":"https://github.com/cobusgreyling/loop-engineering","canonical_url":"https://github.com/cobusgreyling/loop-engineering","annotation":"Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's \"Loop Ready\" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","key_contribution":"Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's \"Loop Ready\" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Patterns-and-tooling repo shipping seven npm CLIs (loop-init, loop-audit, loop-cost, loop-sync, loop-context, loop-mcp-server, loop-worktree), starter kits, and production loop patterns; scaffolds skills/state/budget files, scores a repo's \"Loop Ready\" readiness, detects state drift, and estimates token spend per cadence for Claude Code, Codex, OpenCode, and Grok loops.","impact":"Gives readers a concrete source in loop-engineering (Cobus Greyling) when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (8,117 stars; 1,063 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"trigger;workspace;context;state;budget","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-09","publication_year":"2026","publication_venue":"cobusgreyling/loop-engineering","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"cobusgreyling/loop-engineering","github_stars":"8117","arxiv_id":"","date_added":""},{"row_id":"ale-0199","title":"AutoCVE","url":"https://github.com/larlarua/AutoCVE","canonical_url":"https://github.com/larlarua/AutoCVE","annotation":"Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","key_contribution":"Open-source agent-driven CVE discovery platform whose orchestrator coordinates Recon, Scan, Triage, Finding, and Verification agents through ReAct loops with correction nudges and structured FinalizeFinding termination, running the full discover, source-audit, dynamic-verify, dedup, and report loop on a self-hosted FastAPI/React/PostgreSQL stack with agent-tree observability.","novelty":"Verification is promoted from a final check to a loop-control signal. 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Resource-specific angle: Multi-agent offensive-security meta-harness that turns an existing coding agent into an autonomous vulnerability-research loop, with a verify-claims receipt step that separates confirmed findings from speculation.","impact":"Gives readers a concrete source in T3MP3ST when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (4,839 stars; 1,013 forks; AGPL-3.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"elder-plinius/T3MP3ST","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"elder-plinius/T3MP3ST","github_stars":"4839","arxiv_id":"","date_added":""},{"row_id":"ale-0205","title":"Loom","url":"https://github.com/valkor-ai/loom","canonical_url":"https://github.com/valkor-ai/loom","annotation":"Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","key_contribution":"Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Open-source delivery harness for existing coding agents that treats delivery as a durable loop: route, execute, verify, record evidence, repair, and continue from saved state.","impact":"Gives readers a concrete source in Loom when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (577 stars; 60 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-09","publication_year":"2026","publication_venue":"valkor-ai/loom","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"valkor-ai/loom","github_stars":"577","arxiv_id":"","date_added":""},{"row_id":"ale-0206","title":"Inferoa","url":"https://github.com/agentic-in/inferoa","canonical_url":"https://github.com/agentic-in/inferoa","annotation":"Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","key_contribution":"Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Inference-native agent harness for loop engineering that treats every loop as an inference workload, shaping each turn to preserve cacheable prefixes and bound stale evidence.","impact":"Gives readers a concrete source in Inferoa when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (486 stars; 83 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-08","publication_year":"2026","publication_venue":"agentic-in/inferoa","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"agentic-in/inferoa","github_stars":"486","arxiv_id":"","date_added":""},{"row_id":"ale-0207","title":"PlanWeave","url":"https://github.com/GaosCode/PlanWeave","canonical_url":"https://github.com/GaosCode/PlanWeave","annotation":"File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.","key_contribution":"File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: File-backed loop-engineering system for long-running coding agents that turns fuzzy plans into a claimable task graph of nodes and block documents routed through implementation and review.","impact":"Gives readers a concrete source in PlanWeave when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (222 stars; 12 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"context","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-05-24","publication_year":"2026","publication_venue":"GaosCode/PlanWeave","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"GaosCode/PlanWeave","github_stars":"222","arxiv_id":"","date_added":""},{"row_id":"ale-0208","title":"loop.js","url":"https://github.com/loop-js/loop.js","canonical_url":"https://github.com/loop-js/loop.js","annotation":"TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","key_contribution":"TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: TypeScript loop-engineering framework that runs an agent in rounds against a stated goal until a skeptical, read-only verifier agent accepts the result or a budget is exhausted.","impact":"Gives readers a concrete source in loop.js when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (121 stars; 0 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"objective;budget","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"loop-js/loop.js","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"loop-js/loop.js","github_stars":"121","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0209","title":"ai-trains-ai","url":"https://github.com/Danau5tin/ai-trains-ai","canonical_url":"https://github.com/Danau5tin/ai-trains-ai","annotation":"Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","key_contribution":"Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Recursive training loop where a trainer agent autonomously writes complete reinforcement-learning jobs (environments, rewards, configs), runs them, and iterates on the results.","impact":"Gives readers a concrete source in ai-trains-ai when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (177 stars; 14 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"Danau5tin/ai-trains-ai","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Danau5tin/ai-trains-ai","github_stars":"177","arxiv_id":"","date_added":"2026-07-15"},{"row_id":"ale-0210","title":"Factory 2.0: From Coding Agents to Software Factories","url":"https://factory.ai/news/software-factory","canonical_url":"https://factory.ai/news/software-factory","annotation":"Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","key_contribution":"Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: Factory's software-factory pattern, where Automations coordinate recurring workflows with shared objectives and memory, Missions run multi-agent execution over hours or days, and Droid Computers give agents persistent remote execution across the SDLC.","impact":"Gives readers a concrete source in Factory 2.0: From Coding Agents to Software Factories when they need to choose an implementation surface for repeatable agent work.","signal":"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"objective;context;delegation;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Factory","publication_date":"2026-06-15","publication_year":"2026","publication_venue":"","publisher":"Factory","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0211","title":"Superpowers 6","url":"https://blog.fsck.com/2026/06/15/Superpowers-6/","canonical_url":"https://blog.fsck.com/2026/06/15/Superpowers-6/","annotation":"Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","key_contribution":"Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Release notes doubling as a case study of an unattended overnight autoresearch loop that ran 25 harness experiments against the project's own eval suite, roughly halving orchestration runtime and cutting token spend about 60%.","impact":"Gives readers a concrete source in Superpowers 6 when they need to choose an implementation surface for repeatable agent work.","signal":"Contextual source from blog.fsck.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"delegation;verification;budget","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"Massively Parallel Procrastination","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0212","title":"Introducing Devin Security Swarm","url":"https://cognition.com/blog/introducing-devin-security-swarm","canonical_url":"https://cognition.com/blog/introducing-devin-security-swarm","annotation":"Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","key_contribution":"Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","novelty":"The trigger or cadence is explicit, making the workflow recurring rather than one-off. Resource-specific angle: Cognition's agent swarm runs a continuous discover-verify-fix security loop: parallel agents hunt vulnerabilities, reproduce each in an isolated sandbox to confirm exploitability before reporting, and open remediation PRs, re-running on a schedule after the backlog clears.","impact":"Gives readers a concrete source in Introducing Devin Security Swarm when they need to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"trigger;intake;workspace;verification","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"","publisher":"cognition.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0213","title":"Towards Self-Driving Codebases","url":"https://cursor.com/blog/self-driving-codebases","canonical_url":"https://cursor.com/blog/self-driving-codebases","annotation":"Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","key_contribution":"Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","novelty":"Uses real automated software-engineering systems as evidence for practical loop architectures. Resource-specific angle: Cursor research on running thousands of coding agents as a recursive planner-subplanner-worker hierarchy sustaining roughly 1,000 commits per hour, finding that tolerating small error rates that peer agents later fix beats enforcing per-step correctness.","impact":"Gives readers a concrete source in Towards Self-Driving Codebases when they need to choose an implementation surface for repeatable agent work.","signal":"Contextual source from cursor.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"workspace;delegation;verification;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Wilson Lin","publication_date":"","publication_year":"","publication_venue":"","publisher":"Cursor","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0214","title":"Factory: Incident Response Automation","url":"https://factory.ai/news/incident-response","canonical_url":"https://factory.ai/news/incident-response","annotation":"Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","key_contribution":"Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","novelty":"State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Factory's July 10, 2026 launch where a Droid triggered by Slack alerts (Sentry, Datadog, Rootly, Axiom) autonomously investigates each incident on a dedicated computer, triages, prepares fixes, and reports back in the thread, recording what it learns in a persistent runbook that Factory says improves its incident response over time.","impact":"Gives readers a concrete source in Factory: Incident Response Automation when they need to choose an implementation surface for repeatable agent work.","signal":"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Coding-Agent Loop Systems","section_slug":"coding-agent-loop-systems","lifecycle_stages":"trigger;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Factory","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"","publisher":"Factory","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0215","title":"A Week-Long Autonomous Voxel Manhattan Build","url":"https://x.com/mattshumer_/status/2075268746315268138","canonical_url":"https://x.com/mattshumer_/status/2075268746315268138","annotation":"Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.","key_contribution":"Matt Shumer's demonstration of a single-prompt run in which a frontier model worked autonomously for almost a week with subagent fan-out to build a navigable voxel Manhattan.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. 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Give It Backpressure.","url":"https://generativeprogrammer.com/p/stop-babysitting-your-coding-agent","canonical_url":"https://generativeprogrammer.com/p/stop-babysitting-your-coding-agent","annotation":"Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","key_contribution":"Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Explains how to turn tests, linters, builds, traces, and other signals into feedback loops for coding agents.","impact":"Gives readers a concrete source in Stop Babysitting Your Coding Agent. Give It Backpressure. when they need to measure progress and gate completion with repeatable evidence.","signal":"Operational pattern or playbook; signal comes from reusable loop structure and practical transferability.","resource_type":"Pattern","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;exit","audience":"builder;evaluator","evidence_class":"operational-pattern","signal_strength":"medium","source_status":"ok","authors":"Bilgin Ibryam","publication_date":"","publication_year":"","publication_venue":"","publisher":"generativeprogrammer.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0218","title":"How to Build a Self-Verification Loop in Claude Code","url":"https://dev.to/shipwithaiio/how-to-build-a-self-verification-loop-in-claude-code-3-layers-20-minutes-m1p","canonical_url":"https://dev.to/shipwithaiio/how-to-build-a-self-verification-loop-in-claude-code-3-layers-20-minutes-m1p","annotation":"Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","key_contribution":"Uses hooks to enforce syntax, intent, and regression checks before an agent can finish.","novelty":"The agent workflow includes explicit self-checking or gated completion. 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Resource-specific angle: Addy Osmani argues that review, not code generation, is the bottleneck in agentic workflows, proposing risk-tiered verification depth, heterogeneous AI reviewers, and hard CI gates while warning against closed loops of models with correlated blind spots.","impact":"Gives readers a concrete source in Agentic Code Review when they need to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Addy Osmani","publication_date":"","publication_year":"","publication_venue":"","publisher":"addyosmani.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0220","title":"Using DSPy to Evaluate and Improve Datasette Agent's SQL System Prompts","url":"https://simonwillison.net/2026/Jul/2/dspy-datasette-agent-prompts/","canonical_url":"https://simonwillison.net/2026/Jul/2/dspy-datasette-agent-prompts/","annotation":"Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","key_contribution":"Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Simon Willison wires a DSPy evaluation harness to a live Datasette instance with real tool calls and gold-standard metrics, then uses the eval traces to find and fix weaknesses in the agent's SQL system prompt.","impact":"Gives readers a concrete source in Using DSPy to Evaluate and Improve Datasette Agent's SQL System Prompts when they need to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Simon Willison","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"Simon Willison’s Weblog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0221","title":"Agentic coding notes","url":"https://danluu.com/ai-coding/","canonical_url":"https://danluu.com/ai-coding/","annotation":"Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","key_contribution":"Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Dan Luu's first-hand benchmarks and workflows arguing that systematic test infrastructure such as fuzzing and randomized testing, not human review, is what lets agent-generated code ship, and documenting why a self-contained agentic quality loop has so far eluded him.","impact":"Gives readers a concrete source in Agentic coding notes when they need to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from danluu.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"danluu.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0222","title":"Understanding Is the New Bottleneck","url":"https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck.html","canonical_url":"https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck.html","annotation":"Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","key_contribution":"Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Geoffrey Litt argues that human understanding, not verification, is the real bottleneck in agent loops, warning that cognitive debt accrues when iterations outpace comprehension and proposing literate diffs, quizzes, and interactive micro-worlds as speed regulators.","impact":"Gives readers a concrete source in Understanding Is the New Bottleneck when they need to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.geoffreylitt.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"2026","publication_venue":"","publisher":"geoffreylitt.com","doi":"","publication_note":"","primary_category":"","metadata_source":"url-date","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0223","title":"Verifying Agentic Development at Scale","url":"https://cognition.com/blog/testing-development","canonical_url":"https://cognition.com/blog/testing-development","annotation":"Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","key_contribution":"Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Cognition details the verification stack behind Devin sessions going majority-async: source-grounded test plans, deterministic reusable testing skills, and annotated video artifacts with pass/fail assertions so unattended runs return merge-ready results.","impact":"Gives readers a concrete source in Verifying Agentic Development at Scale when they need to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from cognition.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-05-29","publication_year":"2026","publication_venue":"","publisher":"cognition.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0224","title":"Loop Engineering Without Verification Is Just Automation","url":"https://www.sonarsource.com/blog/loop-engineering-without-verification-is-just-automation/","canonical_url":"https://www.sonarsource.com/blog/loop-engineering-without-verification-is-just-automation/","annotation":"Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","key_contribution":"Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Sonar formalizes a two-tier verification gate for agent loops, pairing a probabilistic LLM verifier sub-agent for intent with a deterministic analysis gate as the hard halt, arguing that LLM-only verification amounts to two optimists agreeing.","impact":"Gives readers a concrete source in Loop Engineering Without Verification Is Just Automation when they need to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.sonarsource.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"sonarsource.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0225","title":"Closing the Verification Loop: Observability-Driven Harnesses","url":"https://www.datadoghq.com/blog/ai/harness-first-agents/","canonical_url":"https://www.datadoghq.com/blog/ai/harness-first-agents/","annotation":"Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","key_contribution":"Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Datadog engineers' case for harness-first engineering once agents write code faster than humans can review, using deterministic simulation testing across millions of seeds as the verification gate.","impact":"Gives readers a concrete source in Closing the Verification Loop: Observability-Driven Harnesses when they need to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.datadoghq.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Alp Keles, Jai Menon, Sesh Nalla, Vyom Shah","publication_date":"2026-03-09","publication_year":"2026","publication_venue":"","publisher":"Datadog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0226","title":"How to build a better agent harness with traces and evals","url":"https://arize.com/blog/improve-ai-agents-traces-evals-harness/","canonical_url":"https://arize.com/blog/improve-ai-agents-traces-evals-harness/","annotation":"Trace-evaluate-debug-refine loop for improving agent behavior from real runs.","key_contribution":"Trace-evaluate-debug-refine loop for improving agent behavior from real runs.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Trace-evaluate-debug-refine loop for improving agent behavior from real runs.","impact":"Gives readers a concrete source in How to build a better agent harness with traces and evals when they need to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from arize.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Aaron Winston","publication_date":"2026-05-29","publication_year":"2026","publication_venue":"","publisher":"Arize AI","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0227","title":"Better Harness: A Recipe for Harness Hill-Climbing with Evals","url":"https://www.langchain.com/blog/better-harness-a-recipe-for-harness-hill-climbing-with-evals","canonical_url":"https://www.langchain.com/blog/better-harness-a-recipe-for-harness-hill-climbing-with-evals","annotation":"LangChain's recipe for using evals as the learning signal for harness improvement.","key_contribution":"LangChain's recipe for using evals as the learning signal for harness improvement.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: LangChain's recipe for using evals as the learning signal for harness improvement.","impact":"Gives readers a concrete source in Better Harness: A Recipe for Harness Hill-Climbing with Evals when they need to measure progress and gate completion with repeatable evidence.","signal":"Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"evaluator","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"langchain.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0228","title":"Improving Deep Agents with harness engineering","url":"https://www.langchain.com/blog/improving-deep-agents-with-harness-engineering","canonical_url":"https://www.langchain.com/blog/improving-deep-agents-with-harness-engineering","annotation":"Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","key_contribution":"Practical discussion of self-verification, traces, middleware, and loop detection for coding agents.","novelty":"The agent workflow includes explicit self-checking or gated completion. 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Resource-specific angle: Optimizes the surrounding harness (tools, prompts, control flow) end to end against task outcomes, turning harness tuning into a measurable improvement loop instead of manual trial and error.","impact":"Gives readers a concrete source in Meta-Harness: End-to-End Optimization of Model Harnesses when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2603.28052; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yoonho Lee; Roshen Nair; Qizheng Zhang; Kangwook Lee; Omar Khattab; Chelsea Finn","publication_date":"2026-03-30","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.28052","date_added":""},{"row_id":"ale-0231","title":"HALO (Hierarchical Agent Loop Optimizer)","url":"https://github.com/context-labs/halo","canonical_url":"https://github.com/context-labs/halo","annotation":"Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","key_contribution":"Analyzes production agent traces to find harness-level failure modes, hands its report to a coding agent to apply fixes, and repeats the collect-analyze-fix-redeploy cycle, reporting AppWorld gains from harness changes alone.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. 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Resource-specific angle: Treats execution traces as the assurance substrate, pairing machine-checkable contracts, testing, and governance so recurring agent orchestration stays verifiable and auditable.","impact":"Gives readers a concrete source in A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2603.18096; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Ciprian Paduraru; Petru-Liviu Bouruc; Alin Stefanescu","publication_date":"2026-03-18","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.MA","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.18096","date_added":""},{"row_id":"ale-0254","title":"Self-Evolving Agents with Anytime-Valid Certificates","url":"https://arxiv.org/abs/2607.00871","canonical_url":"https://arxiv.org/abs/2607.00871","annotation":"Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","key_contribution":"Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Confines self-modification to a small steering adapter around a frozen base model and gates each change with anytime-valid statistical tests that emit auditable certificates, reporting solve-count gains and logged regression prevention on a SWE-bench Verified subset.","impact":"Gives readers a concrete source in Self-Evolving Agents with Anytime-Valid Certificates when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.00871; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Biswa Sengupta","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00871","date_added":""},{"row_id":"ale-0255","title":"Delayed Verification Destabilizes Multi-Agent LLM Belief","url":"https://arxiv.org/abs/2606.27409","canonical_url":"https://arxiv.org/abs/2606.27409","annotation":"Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","key_contribution":"Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Models verifier-corrector loops in multi-agent LLM systems as delayed consensus, deriving a stability threshold where verification that is too strong or too late turns factual consensus into oscillation, plus a greedy corrector-placement algorithm validated on five open models.","impact":"Gives readers a concrete source in Delayed Verification Destabilizes Multi-Agent LLM Belief when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2606.27409; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Igor Itkin","publication_date":"2026-06-25","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"20 pages, 5 figures, 1 table. Code and data: https://github.com/YehudaItkin/delayed-verification-llm","primary_category":"cs.MA","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.27409","date_added":""},{"row_id":"ale-0256","title":"Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory","url":"https://arxiv.org/abs/2606.06523","canonical_url":"https://arxiv.org/abs/2606.06523","annotation":"Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","key_contribution":"Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Models agent workflows and trajectories in Lean 4 dependent types so semantic consistency is machine-checked rather than judged by an LLM, with verification-passing workflows outperforming failing ones by an average of 11.94% on software-engineering benchmarks.","impact":"Gives readers a concrete source in Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2606.06523; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Ruida Wang; Jerry Huang; Pengcheng Wang; Xuanqing Liu; Luyang Kong; Tong Zhang","publication_date":"2026-06-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.06523","date_added":""},{"row_id":"ale-0257","title":"Regimes: An Auditable, Held-Out-Gated Improvement Loop","url":"https://arxiv.org/abs/2606.10241","canonical_url":"https://arxiv.org/abs/2606.10241","annotation":"Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","key_contribution":"Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Event-sourced agent runtime whose self-improvement loop gates every proposed repair behind static checks, sandbox execution, and held-out evaluation before adoption, keeping the full decision trail replayable.","impact":"Gives readers a concrete source in Regimes: An Auditable, Held-Out-Gated Improvement Loop when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2606.10241; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yohei Nakajima","publication_date":"2026-06-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"30 pages, 5 figures. Code and committed runs: https://github.com/yoheinakajima/regimes","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.10241","date_added":""},{"row_id":"ale-0258","title":"Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents","url":"https://arxiv.org/abs/2605.22608","canonical_url":"https://arxiv.org/abs/2605.22608","annotation":"Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","key_contribution":"Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Automated evaluation framework from IBM Research that grades agent behavior at system, trace, and node granularity without predefined error taxonomies, producing feedback aligned with human-annotated errors and predictive of task success.","impact":"Gives readers a concrete source in Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2605.22608; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Asaf Yehudai; Lilach Eden; Michal Shmueli-Scheuer","publication_date":"2026-05-21","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"ACL","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.22608","date_added":""},{"row_id":"ale-0259","title":"Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference","url":"https://arxiv.org/abs/2607.02882","canonical_url":"https://arxiv.org/abs/2607.02882","annotation":"FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","key_contribution":"FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: FlowFixer converts runs of platform-built agentic workflows (Dify, Coze, n8n) into symbolic traces, infers correctness specs and node dependencies to localize root-cause failures, and generates targeted repairs at a 71.3% success rate.","impact":"Gives readers a concrete source in Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.02882; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xuyan Ma; Yawen Wang; Junjie Wang; Xiaofei Xie; Boyu Wu; Mingyang Li; Dandan Wang; Qing Wang","publication_date":"2026-07-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.02882","date_added":""},{"row_id":"ale-0260","title":"SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use","url":"https://arxiv.org/abs/2607.01874","canonical_url":"https://arxiv.org/abs/2607.01874","annotation":"Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","key_contribution":"Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Self-evolving rubric framework that scores agent trajectories on skill selection, following, composition, and reflection, exposing failures that pass/fail outcome checks miss and beating outcome-only filtering as a training signal.","impact":"Gives readers a concrete source in SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.01874; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jiayin Zhu; Kelong Mao; Yudong Guo; Dengbo He; Sulong Xu; Simiu Gu; Yutao Yue","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.01874","date_added":""},{"row_id":"ale-0261","title":"SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Bug Reproduction Tests","url":"https://arxiv.org/abs/2607.00990","canonical_url":"https://arxiv.org/abs/2607.00990","annotation":"Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","key_contribution":"Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Shows that naively feeding bug-reproduction tests to software-engineering agents can mislead them, and instead pipes runtime diagnosis from multi-faceted reproduction tests into patch generation, reaching 75.7% on SWE-bench Verified.","impact":"Gives readers a concrete source in SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Bug Reproduction Tests when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.00990; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yaoqi Guo; Yang Liu; Jie M. Zhang; Yun Ma; Yiling Lou; Zhenpeng Chen","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00990","date_added":""},{"row_id":"ale-0262","title":"AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation","url":"https://arxiv.org/abs/2607.06273","canonical_url":"https://arxiv.org/abs/2607.06273","annotation":"Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","key_contribution":"Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Runtime repair layer that abstracts agent runs into a dependency-aware critical-transition graph, localizes failure-critical subtrajectories after a run, and guides recovery on re-execution without modifying the underlying agent.","impact":"Gives readers a concrete source in AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.06273; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Chenyu Zhao; Shenglin Zhang; Wenwei Gu; Yongqian Sun; Dan Pei; Chetan Bansal; Saravan Rajmohan; Minghua Ma","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06273","date_added":""},{"row_id":"ale-0263","title":"SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review","url":"https://arxiv.org/abs/2607.06065","canonical_url":"https://arxiv.org/abs/2607.06065","annotation":"Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","key_contribution":"Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","novelty":"The list is made machine-readable as a tabular dataset rather than only a Markdown page. Resource-specific angle: Replaces one-shot PR generation with a generate-review-revise loop in which a reviewer agent explores the repository, accepts or rejects the PR, and feeds structured feedback into revision, with an accompanying benchmark and trajectory dataset.","impact":"Gives readers a concrete source in SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.06065; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Ruoyu Wang; Jierun Chen; Shaowei Wang; Chaofan Tao; Sidi Yang; Yuxin Jiang; Kim-Hui Yap; Lifeng Shang; Xiaohui Li; Haoli Bai","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06065","date_added":""},{"row_id":"ale-0264","title":"Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode","url":"https://arxiv.org/abs/2607.07405","canonical_url":"https://arxiv.org/abs/2607.07405","annotation":"Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","key_contribution":"Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","novelty":"State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Finds that 78% of observed agent failures in a tau^2-bench domain are silent wrong-state failures invisible to both the tool and the agent's self-report, and that deterministic read-only pre-execution gates in the loop recover them.","impact":"Gives readers a concrete source in Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07405; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Vikas Reddy; Sumanth Reddy Challaram; Abhishek Basu","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07405","date_added":""},{"row_id":"ale-0265","title":"Harnessing Code Agents for Automatic Software Verification","url":"https://arxiv.org/abs/2607.06341","canonical_url":"https://arxiv.org/abs/2607.06341","annotation":"Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","key_contribution":"Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Wraps a general code agent in a verification harness and lets it run until every targeted Coq lemma is proved, beating fixed human-designed proof strategies and reaching full lemma coverage with no expert intervention.","impact":"Gives readers a concrete source in Harnessing Code Agents for Automatic Software Verification when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.06341; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Shuangxiang Kan; Shuanglong Kan; Sebastian Ertel","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.FL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06341","date_added":""},{"row_id":"ale-0266","title":"LLM-as-a-Verifier: A General-Purpose Verification Framework","url":"https://arxiv.org/abs/2607.05391","canonical_url":"https://arxiv.org/abs/2607.05391","annotation":"Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","key_contribution":"Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Treats verification as a scaling axis and builds a training-free framework that computes continuous scores from token logits for fine-grained agentic feedback, scaled via score granularity, repeated evaluation, and criteria decomposition.","impact":"Gives readers a concrete source in LLM-as-a-Verifier: A General-Purpose Verification Framework when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.05391; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jacky Kwok; Shulu Li; Pranav Atreya; Yuejiang Liu; Yixing Jiang; Chelsea Finn; Marco Pavone; Ion Stoica; Azalia Mirhoseini","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code: https://github.com/llm-as-a-verifier/llm-as-a-verifier Website: https://llm-as-a-verifier.com","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05391","date_added":""},{"row_id":"ale-0267","title":"From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents","url":"https://arxiv.org/abs/2607.08028","canonical_url":"https://arxiv.org/abs/2607.08028","annotation":"Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","key_contribution":"Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Moves deterministic agent behavior out of prompts into code, schemas, and behavior contracts, wrapping validation around a replaceable model boundary so enterprise agents remain auditable and safe across model substitutions.","impact":"Gives readers a concrete source in From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.08028; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Joongho Ahn; Moonsoo Kim","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"32 pages, 6 figures, 16 tables. Reference implementation and evaluation artifacts: https://github.com/hammerbaki/enterprise-llm-agent-harness (archived at https://doi.org/10.5281/zenodo.21269426)","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08028","date_added":""},{"row_id":"ale-0268","title":"From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization","url":"https://arxiv.org/abs/2607.07702","canonical_url":"https://arxiv.org/abs/2607.07702","annotation":"STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","key_contribution":"STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: STRACE structures redundant, heterogeneous agent execution traces by mining batch-level failure patterns and performing causal localization over a textual dependency graph, handing root causes rather than noisy trajectories to the reflection-based optimizer and lifting success on a formal verification task from 42.5% to 58.5%.","impact":"Gives readers a concrete source in From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07702; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Ying Chang; Jiahang Xu; Xuan Feng; Chenyuan Yang; Peng Cheng; Yuqing Yang","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07702","date_added":""},{"row_id":"ale-0269","title":"Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems","url":"https://arxiv.org/abs/2607.07989","canonical_url":"https://arxiv.org/abs/2607.07989","annotation":"AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","key_contribution":"AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: AgentLocate attributes failures in LLM multi-agent trajectories to both the responsible agent and the earliest decisive step, pairing LLM-based evaluation with independent assessor verification and confidence-weighted aggregation to outperform prior attribution methods on two benchmarks, the diagnose side of the verify step for dispatched-agent loops.","impact":"Gives readers a concrete source in Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07989; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yufei Xia; Anjun Gao; Yueyang Quan; Zhuqing Liu; Minghong Fang","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"To appear in COLM 2026","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07989","date_added":""},{"row_id":"ale-0270","title":"3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse","url":"https://arxiv.org/abs/2607.07980","canonical_url":"https://arxiv.org/abs/2607.07980","annotation":"Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","key_contribution":"Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Builds a causal theory of 26 constructs and 67 relationships from 3,100 coded practitioner documents on how AI-authored pull requests reshape code review, arguing review is the control point through which a coding agent's effect on software is decided and that outcomes hinge on team expertise and review process structure rather than AI itself.","impact":"Gives readers a concrete source in 3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07980; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"context","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Shyam Agarwal; Courtney Miller; Christian Kästner; Bogdan Vasilescu","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07980","date_added":""},{"row_id":"ale-0271","title":"Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring","url":"https://arxiv.org/abs/2607.08066","canonical_url":"https://arxiv.org/abs/2607.08066","annotation":"Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","key_contribution":"Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Stress-tests chain-of-thought monitoring as an in-loop safety gate: adversarial agents arguing for policy-violating proposals turn the scratchpad into a persuasion channel, with monitor access to the agent's reasoning increasing approval of harmful actions by 9.5% on average, while pairing monitor and fact-checker from different model families cuts violating approvals by up to 45%.","impact":"Gives readers a concrete source in Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.08066; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jennifer Za; Julija Bainiaksina; Nikita Ostrovsky; Tanush Chopra; Victoria Krakovna","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"25 pages, 10 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08066","date_added":""},{"row_id":"ale-0272","title":"Physics-Audited Agentic Discovery in Scientific Machine Learning","url":"https://arxiv.org/abs/2607.07379","canonical_url":"https://arxiv.org/abs/2607.07379","annotation":"Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","key_contribution":"Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Verification-first workflow (PA-SciML) for agentic model discovery in scientific ML: fixes the scoring evaluator before search, derives machine-checkable physics requirements (boundary conditions, superposition, stiffness scaling, causality), audits every trained candidate's predicted fields against them, and separately searches prescribed input ranges for high-violation cases, reporting a surrogate as verified only under the stated checks; a domain-specific case of verification-gated agentic search.","impact":"Gives readers a concrete source in Physics-Audited Agentic Discovery in Scientific Machine Learning when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07379; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"intake;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Diab W. Abueidda; Bilal Ahmed; Panos Pantidis; Mostafa E. Mobasher","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07379","date_added":""},{"row_id":"ale-0273","title":"Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair","url":"https://arxiv.org/abs/2607.07882","canonical_url":"https://arxiv.org/abs/2607.07882","annotation":"TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","key_contribution":"TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","novelty":"The resource is directly reusable as a starting artifact. Resource-specific angle: TrajSpec runs a trajectory-collection agent over the pre-fix repository and mines the unverified trajectory for specification evidence, refining vague bug reports into structured specifications that guide automated program-repair loops.","impact":"Gives readers a concrete source in Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.07882; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"S M Farah Al Fahim; Md Nakhla Rafi; Md Ahasanuzzaman; Zeyang Ma; Dong Jae Kim; Shaowei Wang; Tse-Hsun; Chen","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07882","date_added":""},{"row_id":"ale-0274","title":"Failure as a Process: An Anatomy of CLI Coding Agent Trajectories","url":"https://arxiv.org/abs/2607.09510","canonical_url":"https://arxiv.org/abs/2607.09510","annotation":"Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","key_contribution":"Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Empirical anatomy of 3,843 CLI coding-agent trajectories across seven models and three scaffolds (OpenHands, MiniSWE, Terminus2), with 1,794 fully annotated over 63,000+ manually reviewed steps; models failure as a temporal process of onset, evolution, and recovery and finds failures dominated by epistemic errors that begin within the first few steps yet stay undetected until recovery is impossible, arguing for in-loop validation and intervention over final-outcome evaluation.","impact":"Gives readers a concrete source in Failure as a Process: An Anatomy of CLI Coding Agent Trajectories when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.09510; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xiangxin Zhao; Han Li; Shuaiting Li; Tianyi Zhao; Earl T. Barr; Federica Sarro; He Ye","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"12 pages, 6 figures","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09510","date_added":""},{"row_id":"ale-0275","title":"Agentic Proof and Property-Based Testing via Property-Templates","url":"https://arxiv.org/abs/2607.09072","canonical_url":"https://arxiv.org/abs/2607.09072","annotation":"Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","key_contribution":"Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Dual-track verification-in-the-loop for AI-generated code: shared property templates drive both formal proof in Lean 4 and executable property-based tests for PySpark, raising agentic proof success up to 2.6x and cutting proof hallucinations by 59%.","impact":"Gives readers a concrete source in Agentic Proof and Property-Based Testing via Property-Templates when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.09072; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Seongmin Lee; Yaoxuan Wu; Miryung Kim","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"12 pages, 7 figures, 4 tables; supplementary material included as ancillary file","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09072","date_added":""},{"row_id":"ale-0276","title":"AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP","url":"https://arxiv.org/abs/2607.11098","canonical_url":"https://arxiv.org/abs/2607.11098","annotation":"Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","key_contribution":"Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","novelty":"Treats feedback, telemetry, and deterministic artifacts as loop-control gates. Resource-specific angle: Workbench that reproduces an agent failure, intervenes at the point it went wrong, and tests mitigations, turning one-off agent bugs into a repeatable diagnose-and-fix loop over MCP tool use.","impact":"Gives readers a concrete source in AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.11098; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"workspace;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Aritra Mazumder; Nusrat jahan Lia","publication_date":"2026-07-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.11098","date_added":"2026-07-15"},{"row_id":"ale-0277","title":"Latent Programming Horizons in Coding Agents","url":"https://arxiv.org/abs/2607.05188","canonical_url":"https://arxiv.org/abs/2607.05188","annotation":"Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","key_contribution":"Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Shows a coding agent's hidden states linearly encode program properties like correctness and test outcomes and predict future edits up to 25 steps ahead, a latent signal that could gate or steer verification loops before edits materialize.","impact":"Gives readers a concrete source in Latent Programming Horizons in Coding Agents when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.05188; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Verification And Feedback Gates","section_slug":"verification-and-feedback-gates","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"André Silva; Han Tu; Martin Monperrus","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05188","date_added":"2026-07-15"},{"row_id":"ale-0278","title":"The lethal trifecta for AI agents","url":"https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/","canonical_url":"https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/","annotation":"Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","key_contribution":"Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","novelty":"Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Simon Willison's rule of thumb: private data, untrusted content, and an exfiltration channel must never meet inside one unattended agent.","impact":"Gives readers a concrete source in The lethal trifecta for AI agents when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"Simon Willison","publication_date":"","publication_year":"2025","publication_venue":"","publisher":"Simon Willison’s Weblog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0279","title":"Prompt injection series","url":"https://simonwillison.net/series/prompt-injection/","canonical_url":"https://simonwillison.net/series/prompt-injection/","annotation":"Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.","key_contribution":"Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.","novelty":"Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Ongoing series on the core unsolved vulnerability for loops whose intake includes content written by strangers.","impact":"Gives readers a concrete source in Prompt injection series when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from simonwillison.net; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"intake","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"Simon Willison","publication_date":"","publication_year":"","publication_venue":"","publisher":"Simon Willison’s Weblog","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0280","title":"Agentic AI - Threats and Mitigations","url":"https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/","canonical_url":"https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/","annotation":"OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","key_contribution":"OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: OWASP threat model for agentic systems, useful when reviewing intake, memory, tool, and delegation boundaries.","impact":"Gives readers a concrete source in Agentic AI - Threats and Mitigations when they need to bound risk before recurring or unattended execution.","signal":"Primary documentation from a platform, SDK, standard, or framework; strong implementation signal.","resource_type":"Docs","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"intake;workspace;context;delegation","audience":"builder;operator;security","evidence_class":"technical-documentation","signal_strength":"high","source_status":"ok","authors":"OWASPGenAIProject Editor","publication_date":"","publication_year":"","publication_venue":"","publisher":"OWASP Gen AI Security Project","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0281","title":"Designing AI agents to resist prompt injection","url":"https://openai.com/index/designing-agents-to-resist-prompt-injection/","canonical_url":"https://openai.com/index/designing-agents-to-resist-prompt-injection/","annotation":"OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","key_contribution":"OpenAI's official defense-in-depth guidance: least privilege, sandboxed tools, output verification, and human confirmation for the high-impact actions an unattended loop might take.","novelty":"Primary-source operational guidance rather than commentary. 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Resource-specific angle: Infrastructure for running AI-generated code in fast, isolated sandboxes.","impact":"Gives readers a concrete source in Daytona when they need to bound risk before recurring or unattended execution.","signal":"Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"builder;operator;security","evidence_class":"implementation","signal_strength":"high","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"daytona.io","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0286","title":"peerd","url":"https://github.com/NotASithLord/peerd","canonical_url":"https://github.com/NotASithLord/peerd","annotation":"Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","key_contribution":"Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","novelty":"Orchestration and control flow are made explicit and inspectable. Resource-specific angle: Browser-extension harness that runs the agent loop entirely client-side with user-supplied keys, sandboxed compute, and per-environment actor agents that hold only their tools and no API keys, isolating the orchestrator from untrusted content as a prompt-injection boundary.","impact":"Gives readers a concrete source in peerd when they need to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (358 stars; 35 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;delegation","audience":"builder;operator;security","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-22","publication_year":"2026","publication_venue":"NotASithLord/peerd","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"NotASithLord/peerd","github_stars":"358","arxiv_id":"","date_added":""},{"row_id":"ale-0287","title":"When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents","url":"https://arxiv.org/abs/2607.05189","canonical_url":"https://arxiv.org/abs/2607.05189","annotation":"Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","key_contribution":"Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Shows one poisoned email can write hidden entries into a persistent personal agent's long-term memory that silently alter future unattended runs, introducing the 108-case WhisperBench evaluation and the MemGhost attack that reaches 87.5% success.","impact":"Gives readers a concrete source in When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.05189; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"context;verification;state","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yechao Zhang; Shiqian Zhao; Jiawen Zhang; Jie Zhang; Gelei Deng; Xiaogeng Liu; Chaowei Xiao; Tianwei Zhang","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"25 pages, 8 figures. Preprint","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05189","date_added":""},{"row_id":"ale-0288","title":"Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses","url":"https://arxiv.org/abs/2607.05029","canonical_url":"https://arxiv.org/abs/2607.05029","annotation":"Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","key_contribution":"Introduces FARMA, an attack that plants forged reasoning traces in an agent's persistent memory so poisoned rationales carry into future runs, and SENTINEL, a reasoning-guard defense that cut attack success from up to 100% to zero in evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. 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Systematizes 39 execution-security papers (2023-2026) into 17 verified categories. 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Resource-specific angle: Execution-boundary system where a deterministic gate admits a tool call only after field authority, exact-effect authorization, and invocation authority all bind to the same action manifest, protecting loops that read attacker-writable context.","impact":"Gives readers a concrete source in Context-to-Execution Integrity for LLM Agents when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.06000; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Igor Santos-Grueiro","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"20 pages","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06000","date_added":""},{"row_id":"ale-0294","title":"When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents","url":"https://arxiv.org/abs/2607.06595","canonical_url":"https://arxiv.org/abs/2607.06595","annotation":"GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.","key_contribution":"GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: GhostWriter is a two-phase attack that poisons the long-term memory store of tool-using personal agents so injected content persists across runs and activates in later tasks.","impact":"Gives readers a concrete source in When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.06595; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context;state","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"George Torres; Sharad Shrestha; Satyajayant Misra","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.06595","date_added":""},{"row_id":"ale-0295","title":"Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents","url":"https://arxiv.org/abs/2607.08395","canonical_url":"https://arxiv.org/abs/2607.08395","annotation":"Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","key_contribution":"Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: Proposes TokenWall, a runtime firewall that audits a long-lived agent's semantic flows (memory updates, tool arguments, inter-component messages) before they reach privileged sinks, reporting attack success reduced to 12.5% with a 97.4% benign pass rate and 0.69s added latency.","impact":"Gives readers a concrete source in Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.08395; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context;state;budget","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Puji Wang; Yingchen Zhang; Ruqing Zhang; Jiafeng Guo; Xueqi Cheng","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08395","date_added":""},{"row_id":"ale-0296","title":"Prismata: Confining Cross-Site Prompt Injection in Web Agents","url":"https://arxiv.org/abs/2607.08147","canonical_url":"https://arxiv.org/abs/2607.08147","annotation":"Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","key_contribution":"Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","novelty":"Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Applies contextual least privilege to web agents by dynamically labeling page content with trust levels and mechanically confining what the agent can see and do, cutting cross-site prompt-injection attack success on benign pages without requiring developer annotations.","impact":"Gives readers a concrete source in Prismata: Confining Cross-Site Prompt Injection in Web Agents when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.08147; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Corban Villa; Alp Eren Ozdarendeli; Sijun Tan; Raluca Ada Popa","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08147","date_added":""},{"row_id":"ale-0297","title":"TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories","url":"https://arxiv.org/abs/2607.08400","canonical_url":"https://arxiv.org/abs/2607.08400","annotation":"Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","key_contribution":"Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","novelty":"The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Embeds a two-channel attribution watermark in LLM-agent trajectory logs (one channel keyed on content for deletion resistance, one on log structure for rewrite resistance) so provenance survives an adversary with full read/write access, reporting detection scores near z = 100 on long-horizon ToolBench and ALFWorld trajectories with no loss of agent performance.","impact":"Gives readers a concrete source in TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.08400; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Zheng Gao; Xiaoyu Li; Xiaoyan Feng; Jiaojiao Jiang; Yang Song; Yulei Sui; Zhenchang Xing; Liming Zhu","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08400","date_added":""},{"row_id":"ale-0298","title":"Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents","url":"https://arxiv.org/abs/2607.07474","canonical_url":"https://arxiv.org/abs/2607.07474","annotation":"Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","key_contribution":"Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Replaces binary attack-success red-teaming metrics with a seven-level ordinal severity rubric (L0-L6) that grades harm along the agent's tool-call trajectory by action reversibility, scope expansion, and privilege escalation, validated with deterministic analysis and frontier-model judges across multiple victim models and defenses.","impact":"Gives readers a concrete source in Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07474; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Harry Owiredu-Ashley","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"8 pages, 6 figures. Code and artifacts: https://github.com/Harry-Ashley/action-graded-severity","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07474","date_added":""},{"row_id":"ale-0299","title":"Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting","url":"https://arxiv.org/abs/2607.07433","canonical_url":"https://arxiv.org/abs/2607.07433","annotation":"Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","key_contribution":"Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Introduces adversarial hallucination squatting, in which attackers pre-register resource names LLMs predictably hallucinate (at rates up to 85-100%) and plant universal, cross-model-transferable promptware payloads on the open web, reaching agent loops that autonomously ingest internet content with no direct injection channel.","impact":"Gives readers a concrete source in Beware of Agentic Botnets: Scalable Untargeted Promptware Attacks via Universal and Transferable Adversarial HalluSquatting when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07433; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Aya Spira; Stav Cohen; Elad Feldman; Ron Bitton; Avishai Wool; Ben Nassi","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Website: https://sites.google.com/view/agentic-botnets/home","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07433","date_added":""},{"row_id":"ale-0300","title":"GitLost: How We Tricked GitHub's AI Agent into Leaking Private Repos","url":"https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/","canonical_url":"https://noma.security/blog/gitlost-how-we-tricked-githubs-ai-agent-into-leaking-private-repos/","annotation":"Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","key_contribution":"Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Noma Labs researcher Sasi Levi shows how hidden plain-English instructions in a malicious GitHub Issue make an issue-triggered GitHub Agentic Workflows agent exfiltrate private-repo contents into public comments, bypassing GitHub's data-leak guardrails with a one-word reframe, disclosed responsibly to GitHub.","impact":"Gives readers a concrete source in GitLost: How We Tricked GitHub's AI Agent into Leaking Private Repos when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from noma.security; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"trigger;intake","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"noma.security","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0301","title":"ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents","url":"https://arxiv.org/abs/2607.07774","canonical_url":"https://arxiv.org/abs/2607.07774","annotation":"Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","key_contribution":"Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark of 4,897 tool calls from offensive-security agent trajectories, labeled by professional penetration testers, for studying pre-execution gating where a cheap trusted LLM judge accepts or rejects each proposed call before it runs and the engagement boundary must be inferred from the request rather than a fixed policy.","impact":"Gives readers a concrete source in ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agents when they need to bound risk before recurring or unattended execution.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;verification;budget","audience":"researcher;evaluator;operator;security","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Shane Caldwell; Max Harley; Ads Dawson; Michael Kouremetis; Vincent Abruzzo; Will Pearce","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"22 pages, 4 figures, 4 tables","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07774","date_added":""},{"row_id":"ale-0302","title":"Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors","url":"https://arxiv.org/abs/2607.07368","canonical_url":"https://arxiv.org/abs/2607.07368","annotation":"Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","key_contribution":"Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. Resource-specific angle: Studies AI control when several agents operating jointly on shared infrastructure pursue a malicious goal, evaluated on FakeLab (synthetic AI-lab codebase: 9 services, 86 benign tasks, 4 attack scenarios); finds a fragmentation effect (the more agents coordinate an attack, the less likely per-agent monitors catch any single attacker) and that explicit planners amplify attack success, directly relevant to monitoring fleets of background agents.","impact":"Gives readers a concrete source in Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07368; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"objective;delegation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Oliver Makins; Orazio Angelini; Zohreh Shams; Mary Phuong","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Submitted to NeurIPS; 81 pages; 32 figures and 24 tables","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07368","date_added":""},{"row_id":"ale-0303","title":"Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions","url":"https://arxiv.org/abs/2607.07461","canonical_url":"https://arxiv.org/abs/2607.07461","annotation":"Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","key_contribution":"Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Finds taint-style flaws make up a substantial fraction of MCP-server vulnerabilities and normally demand context-specific code fixes, then proposes SPELLSMITH, which hardens tool descriptions with security-aware behavioral guidance so the agent's own self-reflection steers it away from triggering the vulnerable flows, mitigating multiple vulnerability classes at the tool-protocol layer without code-level patches.","impact":"Gives readers a concrete source in Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07461; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;context","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yang Shi; Jiaheng Fu; Yihe Huang; Ruixiang Wu; Chengyao Sun; Kaifeng Huang","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07461","date_added":""},{"row_id":"ale-0304","title":"Factory Droid Shield 2.0: Learned Secret Detection for Autonomous Commits","url":"https://factory.ai/news/droid-shield-2-0","canonical_url":"https://factory.ai/news/droid-shield-2-0","annotation":"Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","key_contribution":"Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Factory upgrades the verification gate on every autonomous Droid commit with a two-model pipeline flanking the deterministic secret scanner, pairing a high-recall risk model with a precision referee.","impact":"Gives readers a concrete source in Factory Droid Shield 2.0: Learned Secret Detection for Autonomous Commits when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from factory.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"verification","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Factory","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"","publisher":"Factory","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0305","title":"destructive_command_guard","url":"https://github.com/Dicklesworthstone/destructive_command_guard","canonical_url":"https://github.com/Dicklesworthstone/destructive_command_guard","annotation":"Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","key_contribution":"Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. Resource-specific angle: Rust safety hook that intercepts and blocks destructive Git and shell commands (hard resets, recursive deletes, database drops) before AI coding agents execute them, across Claude Code and other harnesses.","impact":"Gives readers a concrete source in destructive_command_guard when they need to bound risk before recurring or unattended execution.","signal":"Inspectable GitHub source (5,012 stars; 188 forks; NOASSERTION license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"builder;operator;security","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-07","publication_year":"2026","publication_venue":"Dicklesworthstone/destructive_command_guard","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Dicklesworthstone/destructive_command_guard","github_stars":"5012","arxiv_id":"","date_added":""},{"row_id":"ale-0306","title":"Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution","url":"https://ainowinstitute.org/publications/friendly-fire-exploit-brief","canonical_url":"https://ainowinstitute.org/publications/friendly-fire-exploit-brief","annotation":"Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","key_contribution":"Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","novelty":"Untrusted intake is treated as a loop-level security boundary. Resource-specific angle: Proof-of-concept showing prompt injections spread across ordinary repository files can hijack defensive security agents into remote code execution, demonstrating that even security-focused agent loops inherit the untrusted-content attack surface.","impact":"Gives readers a concrete source in Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution when they need to bound risk before recurring or unattended execution.","signal":"Research source; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace;budget;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-paper","signal_strength":"medium","source_status":"ok","authors":"Boyan Milanov","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"","publisher":"AI Now Institute","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0307","title":"How We Contain Claude Across Products","url":"https://www.anthropic.com/engineering/how-we-contain-claude","canonical_url":"https://www.anthropic.com/engineering/how-we-contain-claude","annotation":"Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","key_contribution":"Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","novelty":"Execution isolation and permission boundaries are part of the design. Resource-specific angle: Anthropic engineering on capping agent blast radius with three containment architectures matched to threat models, including ephemeral sandbox containers for untrusted code execution.","impact":"Gives readers a concrete source in How We Contain Claude Across Products when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from www.anthropic.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Securing Unattended Loops","section_slug":"securing-unattended-loops","lifecycle_stages":"workspace","audience":"operator;security","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"Anthropic","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0308","title":"Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Scanner Reliability","url":"https://arxiv.org/abs/2607.11086","canonical_url":"https://arxiv.org/abs/2607.11086","annotation":"Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","key_contribution":"Large-scale study of live MCP servers finding widespread security weaknesses and that existing MCP security scanners miss or misreport many of them, a gap for anyone gating agent tool access on scanner output.","novelty":"Frames security as a recurring-loop boundary rather than a one-time prompt hygiene issue. 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Resource-specific angle: Git-plus-SQLite issue and memory store that agents read and write with a `bd` CLI, giving recurring loops durable task state and progress that survives context resets.","impact":"Gives readers a concrete source in Beads when they need to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (25,376 stars; 1,697 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"intake;context;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2025-10-12","publication_year":"2025","publication_venue":"steveyegge/beads","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"steveyegge/beads","github_stars":"25376","arxiv_id":"","date_added":""},{"row_id":"ale-0326","title":"ARC: Active and Reflection-driven Context Management for Long-Horizon Agents","url":"https://arxiv.org/abs/2601.12030","canonical_url":"https://arxiv.org/abs/2601.12030","annotation":"Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","key_contribution":"Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Treats context as a managed runtime artifact, reorganizing the working context when degradation or context rot is detected across a long run.","impact":"Gives readers a concrete source in ARC: Active and Reflection-driven Context Management for Long-Horizon Agents when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2601.12030; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yilun Yao; Shan Huang; Elsie Dai; Zhewen Tan; Zhenyu Duan; Shousheng Jia; Yanbing Jiang; Tong Yang","publication_date":"2026-01-17","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"15 pages, 5 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2601.12030","date_added":""},{"row_id":"ale-0327","title":"Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers","url":"https://arxiv.org/abs/2603.07670","canonical_url":"https://arxiv.org/abs/2603.07670","annotation":"Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","key_contribution":"Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Formalizes agent memory as a write-manage-read loop and surveys compression, retrieval, reflective self-improvement, and policy-learned management across recurring runs.","impact":"Gives readers a concrete source in Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2603.07670; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Pengfei Du","publication_date":"2026-03-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.07670","date_added":""},{"row_id":"ale-0328","title":"Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering","url":"https://arxiv.org/abs/2604.08224","canonical_url":"https://arxiv.org/abs/2604.08224","annotation":"Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","key_contribution":"Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Reviews how durable state, reusable skills, protocols, and the harness move out of model weights into external infrastructure, the substrate that lets loops persist progress and reuse capability across runs.","impact":"Gives readers a concrete source in Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2604.08224; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Chenyu Zhou; Huacan Chai; Wenteng Chen; Zihan Guo; Rong Shan; Yuanyi Song; Tianyi Xu; Yingxuan Yang; Aofan Yu; Weiming Zhang; Congming Zheng; Jiachen Zhu; Zeyu Zheng; Zhuosheng Zhang; Xingyu Lou; Changwang Zhang; Zhihui Fu; Jun Wang; Weiwen Liu; Jianghao Lin; Weinan Zhang","publication_date":"2026-04-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"54 pages, tech report on Externalization in LLM Agents","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.08224","date_added":""},{"row_id":"ale-0329","title":"Meta Context Engineering via Agentic Skill Evolution","url":"https://arxiv.org/abs/2601.21557","canonical_url":"https://arxiv.org/abs/2601.21557","annotation":"A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","key_contribution":"A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","novelty":"Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: A bi-level loop where a meta-agent evolves reusable skills while a base-agent optimizes context, co-evolving the harness and context artifacts across runs (ICML 2026).","impact":"Gives readers a concrete source in Meta Context Engineering via Agentic Skill Evolution when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2601.21557; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Haoran Ye; Xuning He; Vincent Arak; Haonan Dong; Guojie Song","publication_date":"2026-01-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"46 pages, 4 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2601.21557","date_added":""},{"row_id":"ale-0330","title":"Are We Ready for an Agent-Native Memory System?","url":"https://arxiv.org/abs/2606.24775","canonical_url":"https://arxiv.org/abs/2606.24775","annotation":"Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","key_contribution":"Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: Evaluates twelve agent memory systems across five workloads from a data-management perspective, decomposing memory into representation, extraction, retrieval, and maintenance modules and finding localized maintenance more cost-efficient than global reorganization.","impact":"Gives readers a concrete source in Are We Ready for an Agent-Native Memory System? when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2606.24775; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Wei Zhou; Xuanhe Zhou; Shaokun Han; Hongming Xu; Guoliang Li; Zhiyu Li; Feiyu Xiong; Fan Wu","publication_date":"2026-06-23","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Paper list available at: https://github.com/OpenDataBox/awesome-agent-memory. Source code available at: https://github.com/OpenDataBox/MemoryData","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.24775","date_added":""},{"row_id":"ale-0331","title":"Self-Evolving World Models for LLM Agent Planning","url":"https://arxiv.org/abs/2606.30639","canonical_url":"https://arxiv.org/abs/2606.30639","annotation":"Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","key_contribution":"Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","novelty":"Makes persistence and context management visible as runtime design choices. Resource-specific angle: Evolves a deployment-time world model while the agent and model weights stay frozen, retrieving observed transitions, distilling rules from prediction-observation mismatches, and filtering low-confidence forecasts so each run's errors improve later planning.","impact":"Gives readers a concrete source in Self-Evolving World Models for LLM Agent Planning when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2606.30639; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xuan Zhang; Wenxuan Zhang; See-Kiong Ng; Yang Deng","publication_date":"2026-06-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.30639","date_added":""},{"row_id":"ale-0332","title":"Rethinking Continual Experience Internalization for Self-Evolving LLM Agents","url":"https://arxiv.org/abs/2606.04703","canonical_url":"https://arxiv.org/abs/2606.04703","annotation":"Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","key_contribution":"Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","novelty":"Makes persistence and context management visible as runtime design choices. Resource-specific angle: Finds that naively re-internalizing accumulated experience causes progressive capability collapse across self-improvement iterations, and identifies what keeps the loop stable: principle-level abstractions, step-wise injection for tool use, and off-policy distillation from stronger teacher trajectories.","impact":"Gives readers a concrete source in Rethinking Continual Experience Internalization for Self-Evolving LLM Agents when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2606.04703; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Jingwen Chen; Wenkai Yang; Shengda Fan; Wenbo Nie; Chenxing Sun; Shaodong Zheng; Yangen Hu; Lu Pan; Ke Zeng; Yankai Lin","publication_date":"2026-06-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"10 pages, 8 figures","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.04703","date_added":""},{"row_id":"ale-0333","title":"GenericAgent","url":"https://github.com/lsdefine/GenericAgent","canonical_url":"https://github.com/lsdefine/GenericAgent","annotation":"Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","key_contribution":"Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: Self-evolving agent that grows a skill tree from a small seed, crystallizing completed runs into layered memory and reusable skills, with a master-worker mode for long-horizon goals.","impact":"Gives readers a concrete source in GenericAgent when they need to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (13,459 stars; 1,557 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"objective;context","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-01-16","publication_year":"2026","publication_venue":"lsdefine/GenericAgent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"lsdefine/GenericAgent","github_stars":"13459","arxiv_id":"","date_added":""},{"row_id":"ale-0334","title":"Self-GC: Self-Governing Context for Long-Horizon LLM Agents","url":"https://arxiv.org/abs/2607.00692","canonical_url":"https://arxiv.org/abs/2607.00692","annotation":"Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","key_contribution":"Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Governs long-horizon agent context as indexed lifecycle objects in an explicit nod to garbage collection, with a side-channel planner proposing fold, mask, and prune actions under harness-enforced recoverable sidecars, cutting production input tokens by 10-15%.","impact":"Gives readers a concrete source in Self-GC: Self-Governing Context for Long-Horizon LLM Agents when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.00692; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xubin Hao; Hongjin Meng; Xin Yin; Jiawei Zhu; Chenpeng Cao","publication_date":"2026-07-01","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.00692","date_added":""},{"row_id":"ale-0335","title":"CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents","url":"https://arxiv.org/abs/2607.05378","canonical_url":"https://arxiv.org/abs/2607.05378","annotation":"Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","key_contribution":"Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Reinforcement-learning method that jointly optimizes task execution and compaction-summary generation so long-horizon agents can continue past finite context windows, lifting GLM-4.5-Air to 66.8% on SWE-bench Verified and shipping in the GLM-5.2 pipeline.","impact":"Gives readers a concrete source in CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.05378; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yujiang Li; Zhenyu Hou; Yi Jing; Jie Tang; Yuxiao Dong","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05378","date_added":""},{"row_id":"ale-0336","title":"SelfMem: Self-Optimizing Memory for AI Agents","url":"https://arxiv.org/abs/2607.03726","canonical_url":"https://arxiv.org/abs/2607.03726","annotation":"Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","key_contribution":"Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","novelty":"Primary-source operational guidance rather than commentary. Resource-specific angle: Memory framework in which the agent autonomously optimizes its own storage, retrieval, and summarization strategies per task instead of a fixed pipeline, improving BEAM's official score over the strongest baseline by 48.7%, 40.8%, and 41.9% at 100K, 500K, and 1M tokens, respectively.","impact":"Gives readers a concrete source in SelfMem: Self-Optimizing Memory for AI Agents when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.03726; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Shu Yang; Junchao Wu; Derek F. Wong; Di Wang","publication_date":"2026-07-04","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.03726","date_added":""},{"row_id":"ale-0337","title":"Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture","url":"https://arxiv.org/abs/2607.04391","canonical_url":"https://arxiv.org/abs/2607.04391","annotation":"Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","key_contribution":"Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: Model-, storage-, and API-agnostic agent memory architecture where the agent drives symbolic retrieval over a structured relational database, making long-term memory auditable and reproducible instead of opaque embedding search, validated in a year-long deployment.","impact":"Gives readers a concrete source in Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.04391; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;delegation","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Serge Lacasse; Jérémie Hatier; Alex Baker","publication_date":"2026-07-05","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"22 pages, 2 figures","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.04391","date_added":""},{"row_id":"ale-0338","title":"The Log Is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems","url":"https://arxiv.org/abs/2605.21997","canonical_url":"https://arxiv.org/abs/2605.21997","annotation":"BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","key_contribution":"BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: BabyAGI creator Yohei Nakajima makes an append-only event log the source of truth and the working graph a deterministic projection, giving long-running loops deterministic replay, cheap forking at any event, and end-to-end causal lineage from goal to model call.","impact":"Gives readers a concrete source in The Log Is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2605.21997; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"objective;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yohei Nakajima","publication_date":"2026-05-21","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"11 pages, 1 figure. Open-source Apache-2.0 implementation with reproducible quickstart demo, deterministic replay, fork-and-diff, and lineage tracing","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.21997","date_added":""},{"row_id":"ale-0339","title":"Agentics: Memorizing Session Transcripts Isn't Useful","url":"https://12gramsofcarbon.com/p/agentics-memorizing-session-transcripts","canonical_url":"https://12gramsofcarbon.com/p/agentics-memorizing-session-transcripts","annotation":"From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","key_contribution":"From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: From thousands of agent sessions at Nori, reports zero coding-task benefit from giving agents search over prior session transcripts and argues loop state belongs in distilled artifacts like commits and docs because agents never prune stale context.","impact":"Gives readers a concrete source in Agentics: Memorizing Session Transcripts Isn't Useful when they need to carry context, state, and receipts across runs and failures.","signal":"Contextual source from 12gramsofcarbon.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"theahura","publication_date":"","publication_year":"","publication_venue":"","publisher":"12gramsofcarbon.com","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0340","title":"Long-Running Agents","url":"https://addyo.substack.com/p/long-running-agents","canonical_url":"https://addyo.substack.com/p/long-running-agents","annotation":"Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","key_contribution":"Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Addy Osmani's essay on the infrastructure behind agents that run for hours or days, naming three walls (finite context, missing persistent state, unreliable self-verification) and the patterns that address them: durable event logs, checkpoint-and-resume, external state, and a planner/worker/judge split.","impact":"Gives readers a concrete source in Long-Running Agents when they need to carry context, state, and receipts across runs and failures.","signal":"Contextual source from addyo.substack.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification;state","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"Addy Osmani","publication_date":"","publication_year":"","publication_venue":"","publisher":"Substack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0341","title":"StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems","url":"https://arxiv.org/abs/2607.05844","canonical_url":"https://arxiv.org/abs/2607.05844","annotation":"Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","key_contribution":"Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: Conflict-aware replicated memory contract with immutable history, explicit conflict objects, and projection-time resolution, so agent systems that accumulate contradictory observations across branches, retries, and replicas record state auditably instead of silently overwriting it.","impact":"Gives readers a concrete source in StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.05844; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;delegation;state;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Sergey Volkov; Yang Li; Ye Luo","publication_date":"2026-07-07","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Code and supplementary materials available at: https://github.com/nZiben/statefuse","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05844","date_added":""},{"row_id":"ale-0342","title":"Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents","url":"https://arxiv.org/abs/2607.08716","canonical_url":"https://arxiv.org/abs/2607.08716","annotation":"Names the failure mode \"behavioral state decay\" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","key_contribution":"Names the failure mode \"behavioral state decay\" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: Names the failure mode \"behavioral state decay\" (decision-relevant state such as prior attempts, diagnoses, and open subgoals gets buried or evicted as trajectories grow) and pairs the action agent with a proactive memory agent that maintains a structured memory bank and injects memory-grounded reminders only when needed, gaining +8.3 points on Terminal-Bench 2.0 and +6.8 on τ²-Bench.","impact":"Gives readers a concrete source in Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.08716; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Yifan Wu; Lizhu Zhang; Yuhang Zhou; Mingyi Wang; Bo Peng; Serena Li; Xiangjun Fan; Zhuokai Zhao","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08716","date_added":""},{"row_id":"ale-0343","title":"What to Keep, What to Forget: A Rate-Distortion View of Memory Compaction","url":"https://arxiv.org/abs/2607.08032","canonical_url":"https://arxiv.org/abs/2607.08032","annotation":"Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","key_contribution":"Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: Rate-distortion framing that unifies KV-cache eviction, prompt compression, recurrent state, and cross-session agent memory compaction under a single objective with a layer-agnostic lower bound, showing why attention- and recency-based eviction discards information before future queries reveal what mattered.","impact":"Gives readers a concrete source in What to Keep, What to Forget: A Rate-Distortion View of Memory Compaction when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.08032; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"objective;context;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Ashwin Gerard Colaco; Nada Lahjouji","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08032","date_added":""},{"row_id":"ale-0344","title":"A Hierarchical Memory Architecture Overcomes Context Limits in Long-Horizon Multi-Agent Modeling","url":"https://arxiv.org/abs/2607.07666","canonical_url":"https://arxiv.org/abs/2607.07666","annotation":"Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","key_contribution":"Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Three-layer hierarchical memory keeps injected context bounded and roughly constant across multi-session, long-horizon multi-agent workflows, demonstrated by the Ensemble QSP framework autonomously selecting pharmacokinetic-pharmacodynamic models across 104 runs with overseer agents handling verification and troubleshooting.","impact":"Gives readers a concrete source in A Hierarchical Memory Architecture Overcomes Context Limits in Long-Horizon Multi-Agent Modeling when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.07666; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;delegation;verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Shivendra G. Tewari; Holly Kimko","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"19 pages, 4 figures, 2 tables. Preprint submitted for publication","primary_category":"q-bio.QM","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07666","date_added":""},{"row_id":"ale-0345","title":"SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents","url":"https://arxiv.org/abs/2607.07676","canonical_url":"https://arxiv.org/abs/2607.07676","annotation":"Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","key_contribution":"Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","novelty":"State persistence is explicit enough for repeated runs and handoff. Resource-specific angle: Claims the largest open skill library for agents (216,938 structured skills across 24 domain bundles), built with an LLM quality gate (SkillGate) and iterative source-grounding that maps each retained claim to an exact source quotation, and shipped as offline-searchable SQLite FTS5 bundles, infrastructure for skills-as-persistent-state that agent loops accumulate and reuse.","impact":"Gives readers a concrete source in SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.07676; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Tianming Sha; Yue Zhao; Lichao Sun; Yushun Dong","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"44 pages, 5 figures. Code: https://github.com/LabRAI/SkillCenter ; Data: https://huggingface.co/datasets/Tommysha/skillcenter-bundles","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07676","date_added":""},{"row_id":"ale-0346","title":"How version control will evolve for the agent boom","url":"https://entire.io/blog/how-version-control-will-evolve-for-the-agent-boom","canonical_url":"https://entire.io/blog/how-version-control-will-evolve-for-the-agent-boom","annotation":"Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","key_contribution":"Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: Thomas Dohmke (former GitHub CEO, now founder of Entire) argues that agent session logs (prompts, tool calls, and decision checkpoints) are becoming the most important artifact in software development and should be versioned alongside code so agent fleets stop repeating mistakes, and that Git hosting must re-decentralize for agent-scale parallelism.","impact":"Gives readers a concrete source in How version control will evolve for the agent boom when they need to carry context, state, and receipts across runs and failures.","signal":"Contextual source from entire.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Blog","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace;state;exit","audience":"builder","evidence_class":"practitioner-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"","publisher":"Entire","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0347","title":"self-learning-skills","url":"https://github.com/Kulaxyz/self-learning-skills","canonical_url":"https://github.com/Kulaxyz/self-learning-skills","annotation":"Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","key_contribution":"Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Meta-skill for Claude Code, Cursor, and AGENTS.md-compatible agents that recognizes when a session has earned a hard-won golden path (or hit a dead-end worth remembering), distills the procedure including failed approaches, and persists it as a skill or rule auto-loaded next run, turning each session's discoveries into durable cross-session loop state.","impact":"Gives readers a concrete source in self-learning-skills when they need to carry context, state, and receipts across runs and failures.","signal":"Inspectable GitHub source (875 stars; 34 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-28","publication_year":"2026","publication_venue":"Kulaxyz/self-learning-skills","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"Kulaxyz/self-learning-skills","github_stars":"875","arxiv_id":"","date_added":""},{"row_id":"ale-0348","title":"GitLake: Git-for-data for the agentic lakehouse","url":"https://arxiv.org/abs/2607.08319","canonical_url":"https://arxiv.org/abs/2607.08319","annotation":"Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","key_contribution":"Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","novelty":"Makes persistence and context management visible as runtime design choices. Resource-specific angle: Git-for-data design for an agent-first lakehouse that lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, so agents work on isolated branches while humans review and publish, and pipeline outputs become visible atomically or not at all, with production lessons and correctness insights from a preliminary Alloy model of the core abstractions.","impact":"Gives readers a concrete source in GitLake: Git-for-data for the agentic lakehouse when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.08319; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Weiming Sheng; Jinlang Wang; Manuel Barros; Aldrin Montana; Jacopo Tagliabue; Luca Bigon","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Pre-print of the paper accepted at DASHSys, VLDB 2026, Boston, USA","primary_category":"cs.DB","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08319","date_added":""},{"row_id":"ale-0349","title":"Shared Selective Persistent Memory for Agentic LLM Systems","url":"https://arxiv.org/abs/2607.09493","canonical_url":"https://arxiv.org/abs/2607.09493","annotation":"Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","key_contribution":"Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: Architecture that selectively persists four categories of reusable context (task specifications, data schemas, tool configurations, output constraints) across agent sessions while discarding session-specific reasoning traces, with cross-user sharing under access controls and a zero-token refresh path for recurring data updates, reporting 96% task completion vs 79% without memory and 71% with full-history carryover.","impact":"Gives readers a concrete source in Shared Selective Persistent Memory for Agentic LLM Systems when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.09493; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"workspace;context;state;budget;exit","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Sanjana Pedada; Aditya Dhavala; Neelraj Patil","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"11 pages, 2 figures, 4 tables","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09493","date_added":""},{"row_id":"ale-0350","title":"Scoped Verification for Reliable Long-Horizon Agentic Context Evolution","url":"https://arxiv.org/abs/2607.09175","canonical_url":"https://arxiv.org/abs/2607.09175","annotation":"GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","key_contribution":"GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: GRACE represents an agent's persistent instructions as a typed semantic graph and runs scoped verification over the local neighborhood of each proposed edit before committing it, so accumulated context evolves reliably across long-horizon deployment under distribution shift instead of drifting as flat text; evaluated on telecom agent tasks.","impact":"Gives readers a concrete source in Scoped Verification for Reliable Long-Horizon Agentic Context Evolution when they need to carry context, state, and receipts across runs and failures.","signal":"Research source arXiv:2607.09175; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Persist","user_goal":"Carry context, state, and receipts across runs.","section":"State, Memory, And Context Persistence","section_slug":"state-memory-and-context-persistence","lifecycle_stages":"context;verification;state","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Dan C. 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Resource-specific angle: Databricks' open-source meta-harness and control plane that runs Claude Code, Codex, Cursor, and Pi under shared policies, with budget caps and human-approval gates enforced at the harness layer rather than in prompts.","impact":"Gives readers a concrete source in Omnigent when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (7,378 stars; 1,040 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"budget;escalation","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-11","publication_year":"2026","publication_venue":"omnigent-ai/omnigent","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"omnigent-ai/omnigent","github_stars":"7378","arxiv_id":"","date_added":""},{"row_id":"ale-0371","title":"From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution","url":"https://arxiv.org/abs/2604.11378","canonical_url":"https://arxiv.org/abs/2604.11378","annotation":"Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","key_contribution":"Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Replaces opaque agent loops with immutable plan-version DAGs and a planning-execution-recovery split, giving inspectable scheduling, deterministic recovery, escalation, and termination guarantees.","impact":"Gives readers a concrete source in From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution when they need to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2604.11378; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;escalation;exit","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Hu Wei","publication_date":"2026-04-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"51 pages, 4 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.11378","date_added":""},{"row_id":"ale-0372","title":"Eve","url":"https://github.com/vercel/eve","canonical_url":"https://github.com/vercel/eve","annotation":"Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","key_contribution":"Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","novelty":"Durable execution and replay are treated as first-class loop infrastructure. Resource-specific angle: Vercel's TypeScript-native agent framework with durable execution, sandboxed compute, and OpenTelemetry tracing built in, so recurring agent work persists, replays, and is observable across runs by default.","impact":"Gives readers a concrete source in Eve when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (3,735 stars; 337 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"workspace;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-16","publication_year":"2026","publication_venue":"vercel/eve","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"vercel/eve","github_stars":"3735","arxiv_id":"","date_added":""},{"row_id":"ale-0373","title":"Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework","url":"https://arxiv.org/abs/2603.11445","canonical_url":"https://arxiv.org/abs/2603.11445","annotation":"Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","key_contribution":"Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Decomposes work into a dependency-aware DAG, runs domain agents in parallel, and uses an LLM verifier to drive adaptive replanning with configurable stop conditions, the verify-and-replan core of a reliable loop.","impact":"Gives readers a concrete source in Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework when they need to choose an implementation surface for repeatable agent work.","signal":"Research source arXiv:2603.11445; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"delegation;verification;exit","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xing Zhang; Yanwei Cui; Guanghui Wang; Wei Qiu; Ziyuan Li; Fangwei Han; Yajing Huang; Hengzhi Qiu; Bing Zhu; Peiyang He","publication_date":"2026-03-12","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"ICLR 2026 Workshop on MALGAI","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.11445","date_added":""},{"row_id":"ale-0374","title":"From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents","url":"https://arxiv.org/abs/2603.22386","canonical_url":"https://arxiv.org/abs/2603.22386","annotation":"Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","key_contribution":"Organizes how agent workflows are fixed ahead of time or generated and revised per run, and which evaluation signals drive that choice, a map of the design space for recurring loops.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. 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Resource-specific angle: Agentic model routing for coding agents reframed as a context-action-feedback loop (ACRouter: orchestrator, verifier, memory) that learns which LLM to route each task to from execution feedback rather than frozen priors, with the CodeRouterBench benchmark across 8 frontier models.","impact":"Gives readers a concrete source in Agent-as-a-Router when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (632 stars; 14 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"context;delegation;verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-06-20","publication_year":"2026","publication_venue":"LanceZPF/agent-as-a-router","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"LanceZPF/agent-as-a-router","github_stars":"632","arxiv_id":"","date_added":""},{"row_id":"ale-0376","title":"Amp: Custom Agents","url":"https://ampcode.com/news/custom-agents","canonical_url":"https://ampcode.com/news/custom-agents","annotation":"Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","key_contribution":"Amp's plugin-defined custom agents that run as the main agent or as subagents, spawn parallel workers, join tool pipelines, and use thread actions to build background review threads that report results back to a parent thread.","novelty":"The work separates roles across agents, verifiers, or orchestration layers. 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Resource-specific angle: Self-hosted control plane for running fleets of coding agents across your own machines, with scheduling, per-pod Git worktree isolation, Kanban work tracking, and merge-request integration.","impact":"Gives readers a concrete source in AgentsMesh when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (2,282 stars; 228 forks; NOASSERTION license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;workspace","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-02-28","publication_year":"2026","publication_venue":"AgentsMesh/AgentsMesh","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"AgentsMesh/AgentsMesh","github_stars":"2282","arxiv_id":"","date_added":""},{"row_id":"ale-0378","title":"Bernstein","url":"https://github.com/sipyourdrink-ltd/bernstein","canonical_url":"https://github.com/sipyourdrink-ltd/bernstein","annotation":"Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","key_contribution":"Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","novelty":"Workspace isolation is part of the loop design, not an afterthought. Resource-specific angle: Deterministic Python orchestrator that runs parallel CLI coding agents in isolated Git worktrees, gates merges on tests, lint, and type checks, and records every scheduling decision in a tamper-evident audit log.","impact":"Gives readers a concrete source in Bernstein when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (681 stars; 62 forks; Apache-2.0 license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;workspace;delegation;verification","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-22","publication_year":"2026","publication_venue":"sipyourdrink-ltd/bernstein","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"sipyourdrink-ltd/bernstein","github_stars":"681","arxiv_id":"","date_added":""},{"row_id":"ale-0379","title":"Aeon","url":"https://github.com/aaronjmars/aeon","canonical_url":"https://github.com/aeonfun/aeon","annotation":"Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","key_contribution":"Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: Autonomous agent framework that runs Claude Code unattended on GitHub Actions, dispatching skills on cron or reactive triggers with per-run quality scoring, persistent memory, and self-healing skill repair.","impact":"Gives readers a concrete source in Aeon when they need to choose an implementation surface for repeatable agent work.","signal":"Inspectable GitHub source (577 stars; 208 forks; MIT license; updated 2026-07-16); popularity is context, not proof of reliability.","resource_type":"Tool","collection":"Build","user_goal":"Choose runtimes, tools, and delegation surfaces.","section":"Orchestration And Multi-Agent Delegation","section_slug":"orchestration-and-multi-agent-delegation","lifecycle_stages":"trigger;context;state","audience":"builder","evidence_class":"source-implementation","signal_strength":"medium","source_status":"ok","authors":"","publication_date":"2026-03-04","publication_year":"2026","publication_venue":"aaronjmars/aeon","publisher":"GitHub","doi":"","publication_note":"","primary_category":"","metadata_source":"github-api","github_repo":"aaronjmars/aeon","github_stars":"577","arxiv_id":"","date_added":""},{"row_id":"ale-0380","title":"h5i","url":"https://github.com/h5i-dev/h5i","canonical_url":"https://github.com/h5i-dev/h5i","annotation":"Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","key_contribution":"Gives each coding agent an isolated sandboxed Git worktree, dispatches one task to a team that peer-reviews each other's candidates, then replays and tests each candidate with a neutral verifier before merging the winner.","novelty":"Workspace isolation is part of the loop design, not an afterthought. 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Resource-specific angle: Multi-file refactoring tasks that require tracking and carrying state across many steps, isolating the durable-state weakness that breaks long agent loops.","impact":"Gives readers a concrete source in RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"state","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Dhruv Gautam; Spandan Garg; Jinu Jang; Neel Sundaresan; Roshanak Zilouchian Moghaddam","publication_date":"2025-03-10","publication_year":"2025","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"ICLR 2025 Camera Ready","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2503.07832","date_added":""},{"row_id":"ale-0414","title":"RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents","url":"https://arxiv.org/abs/2606.22678","canonical_url":"https://arxiv.org/abs/2606.22678","annotation":"Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","key_contribution":"Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Scores planning, verification coverage, recovery, abstention, and atomic transitions (not just whether code passes), measuring the loop discipline that separates reliable agents from reckless trial-and-error.","impact":"Gives readers a concrete source in RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Meher Bhaskar Madiraju; Meher Sai Preetam Madiraju","publication_date":"2026-06-21","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"9 pages, 7 tables, 1 figure","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.22678","date_added":""},{"row_id":"ale-0415","title":"SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks","url":"https://arxiv.org/abs/2603.24755","canonical_url":"https://arxiv.org/abs/2603.24755","annotation":"Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","key_contribution":"Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: Quantifies structural erosion and verbosity creep across iteration checkpoints in native harnesses like Claude Code and Codex, evidence for why loops need verification and budgets.","impact":"Gives readers a concrete source in SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;state;budget","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Gabriel Orlanski; Devjeet Roy; Alexander Yun; Changho Shin; Alex Gu; Albert Ge; Dyah Adila; Nicholas Roberts; Frederic Sala; Aws Albarghouthi","publication_date":"2026-03-25","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"10.5281/zenodo.18405900,","publication_note":"Code and Leaderboards are located at https://www.scbench.ai","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.24755","date_added":""},{"row_id":"ale-0416","title":"LongCLI-Bench: A Preliminary Benchmark for Long-horizon Agentic Programming in Command-Line Interfaces","url":"https://arxiv.org/abs/2602.14337","canonical_url":"https://arxiv.org/abs/2602.14337","annotation":"Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","key_contribution":"Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Long-horizon CLI tasks where most runs stall below 30% completion, mapping where unattended loops break down.","impact":"Gives readers a concrete source in LongCLI-Bench: A Preliminary Benchmark for Long-horizon Agentic Programming in Command-Line Interfaces when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;exit","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Yukang Feng; Jianwen Sun; Zelai Yang; Jiaxin Ai; Chuanhao Li; Zizhen Li; Fanrui Zhang; Kang He; Rui Ma; Jifan Lin; Jie Sun; Yang Xiao; Sizhuo Zhou; Wenxiao Wu; Yiming Liu; Pengfei Liu; Yu Qiao; Shenglin Zhang; Kaipeng Zhang","publication_date":"2026-02-15","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2602.14337","date_added":""},{"row_id":"ale-0417","title":"Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios?","url":"https://arxiv.org/abs/2606.29920","canonical_url":"https://arxiv.org/abs/2606.29920","annotation":"Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","key_contribution":"Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Benchmark of 2,458 instances across research and coding domains measuring how reliably LLM judges verify rubrics on agent outputs, finding substantial noise even in strong models and quantifying the trade-offs of prompt design, batched evaluation, and majority voting.","impact":"Gives readers a concrete source in Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios? when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Yangda Peng; Yunjia Qi; Hao Peng; Haotian Xia; Guanzhong He; Xintong Shi; Richeng Xuan; Songyuanyi Lu; Yixian Liu; Zhichao Hu; Yuhong Liu; Lei Hou; Bin Xu; Juanzi Li","publication_date":"2026-06-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.29920","date_added":""},{"row_id":"ale-0418","title":"SentinelBench: A Benchmark for Long-Running Monitoring Agents","url":"https://arxiv.org/abs/2606.05342","canonical_url":"https://arxiv.org/abs/2606.05342","annotation":"Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","key_contribution":"Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Microsoft Research benchmark of 100 tasks across 10 synthetic web environments that evaluates long-running monitoring agents on whether they wait or act appropriately, scoring task completion, response speed, and resource efficiency.","impact":"Gives readers a concrete source in SentinelBench: A Benchmark for Long-Running Monitoring Agents when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;exit","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Matheus Kunzler Maldaner; Adam Fourney; Amanda Swearngin; Hussein Mozannar; Gagan Bansal; Maya Murad; Rafah Hosn; Saleema Amershi","publication_date":"2026-06-03","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"18 pages, 16 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.05342","date_added":""},{"row_id":"ale-0419","title":"SWE-Together: Evaluating Coding Agents in Interactive User Sessions","url":"https://arxiv.org/abs/2606.29957","canonical_url":"https://arxiv.org/abs/2606.29957","annotation":"Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","key_contribution":"Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Multi-session coding benchmark of 109 repository-level tasks reconstructed from 11,260 recorded user-agent sessions, replayed with an LLM user simulator and scored on final correctness and the number of corrective feedback turns.","impact":"Gives readers a concrete source in SWE-Together: Evaluating Coding Agents in Interactive User Sessions when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Yifan Wu; Zhuokai Zhao; Songlin Li; Ho Hin Lee; Jiacheng Zhu; Shirley Wu; Tianhe Yu; Serena Li; Lizhu Zhang; Xiangjun Fan; Shengzhi Li","publication_date":"2026-06-29","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.29957","date_added":""},{"row_id":"ale-0420","title":"The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break","url":"https://arxiv.org/abs/2604.11978","canonical_url":"https://arxiv.org/abs/2604.11978","annotation":"Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","key_contribution":"Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Cross-domain diagnostic benchmark that scales task horizon through depth and breadth extension, then attributes failures across 3,100+ agent trajectories to a seven-category taxonomy via a trajectory-grounded LLM judge validated against human annotation.","impact":"Gives readers a concrete source in The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Xinyu Jessica Wang; Haoyue Bai; Yiyou Sun; Haorui Wang; Shuibai Zhang; Wenjie Hu; Mya Schroder; Bilge Mutlu; Dawn Song; Robert D Nowak","publication_date":"2026-04-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.11978","date_added":""},{"row_id":"ale-0421","title":"Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents","url":"https://arxiv.org/abs/2603.29231","canonical_url":"https://arxiv.org/abs/2603.29231","annotation":"Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","key_contribution":"Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","novelty":"The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: Reliability metrics for long-horizon agents (reliability decay, variance amplification, graceful degradation, meltdown onset) measured over 23,392 episodes across 10 models, showing capability and reliability rankings diverge as tasks lengthen.","impact":"Gives readers a concrete source in Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2603.29231; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Aaditya Khanal; Yangyang Tao; Junxiu Zhou","publication_date":"2026-03-31","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"23 pages, 4 figures","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.29231","date_added":""},{"row_id":"ale-0422","title":"SEAGym: An Evaluation Environment for Self-Evolving LLM Agents","url":"https://arxiv.org/abs/2606.17546","canonical_url":"https://arxiv.org/abs/2606.17546","annotation":"Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","key_contribution":"Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Evaluation environment that measures whether a self-evolving agent's modifications to prompts, memory, and tools generalize to held-out tasks, using train, validation, and test splits and cost metrics on Terminal-Bench 2.0 and HLE.","impact":"Gives readers a concrete source in SEAGym: An Evaluation Environment for Self-Evolving LLM Agents when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;context;verification;budget","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Congjie Zheng; Chuanyi Xue; Bin Liang; Jun Yang; Changshui Zhang","publication_date":"2026-06-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.17546","date_added":""},{"row_id":"ale-0423","title":"EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions","url":"https://arxiv.org/abs/2605.24110","canonical_url":"https://arxiv.org/abs/2605.24110","annotation":"Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","key_contribution":"Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Benchmark of 26 evolving coding tasks across 227 evaluation rounds using cumulative executable tests to check that agents keep prior requirements working as specifications change, with top agents reaching only about 50% on multi-turn success metrics.","impact":"Gives readers a concrete source in EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Haiyang Shen; Xuanzhong Chen; Wendong Xu; Yun Ma; Liang Chen; Kuan Li","publication_date":"2026-05-22","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Work in Progress; 32 pages, 10 figures, preprint","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.24110","date_added":""},{"row_id":"ale-0424","title":"On the Reliability of Computer Use Agents","url":"https://arxiv.org/abs/2604.17849","canonical_url":"https://arxiv.org/abs/2604.17849","annotation":"Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","key_contribution":"Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. Resource-specific angle: Repeated-execution study on OSWorld decomposing why computer-use agents fail tasks they previously completed, separating execution stochasticity, task-specification ambiguity, and behavioral variability as distinct causes of unreliability.","impact":"Gives readers a concrete source in On the Reliability of Computer Use Agents when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2604.17849; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Gonzalo Gonzalez-Pumariega; Saaket Agashe; Jiachen Yang; Ang Li; Xin Eric Wang","publication_date":"2026-04-20","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"33 pages, 3 figures, 4 tables","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2604.17849","date_added":""},{"row_id":"ale-0425","title":"AgentLens: Revealing the Lucky Pass Problem in SWE-Agent Evaluation","url":"https://arxiv.org/abs/2605.12925","canonical_url":"https://arxiv.org/abs/2605.12925","annotation":"Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","key_contribution":"Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Grades 2,614 SWE-agent trajectories across eight models to show that 10.7% of passing trajectories in its 1,815-trajectory evaluation subset are lucky trial-and-error successes, replacing binary pass/fail with process-quality tiers that shift model rankings.","impact":"Gives readers a concrete source in AgentLens: Revealing the Lucky Pass Problem in SWE-Agent Evaluation when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2605.12925; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Priyam Sahoo; Gaurav Mittal; Xiaomin Li; Shengjie Ma; Benjamin Steenhoek; Pingping Lin; Yu Hu","publication_date":"2026-05-13","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.12925","date_added":""},{"row_id":"ale-0426","title":"ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction","url":"https://arxiv.org/abs/2601.21008","canonical_url":"https://arxiv.org/abs/2601.21008","annotation":"Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","key_contribution":"Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Formalizes infeasible-model debugging as a solver-in-the-loop process where each action triggers solver re-execution and infeasibility recomputation, giving deterministic verification for iterative repair in operations research.","impact":"Gives readers a concrete source in ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"trigger;verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Ruicheng Ao; David Simchi-Levi; Xinshang Wang","publication_date":"2026-01-28","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"58 pages, accepted by ICML 2026","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2601.21008","date_added":""},{"row_id":"ale-0427","title":"LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis","url":"https://arxiv.org/abs/2605.30434","canonical_url":"https://arxiv.org/abs/2605.30434","annotation":"Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","key_contribution":"Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark of 68 real-world data-analysis tasks built from Kaggle notebooks spanning 2,225 interactive turns, finding that long-horizon errors account for 52-69% of agent failures and that maintaining a correct analytical state is the core bottleneck.","impact":"Gives readers a concrete source in LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;state","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Kewei Xu; Xiaoben Lu; Shuofei Qiao; Zihan Ding; Haoming Xu; Lei Liang; Ningyu Zhang","publication_date":"2026-05-28","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Ongoing work","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.30434","date_added":""},{"row_id":"ale-0428","title":"MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks","url":"https://arxiv.org/abs/2602.16313","canonical_url":"https://arxiv.org/abs/2602.16313","annotation":"Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","key_contribution":"Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Multi-session benchmark of interdependent agentic tasks where agents must distill earlier sessions into memory and use it to guide later actions, showing that near-saturated scores on long-context memory benchmarks fail to transfer.","impact":"Gives readers a concrete source in MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Zexue He; Yu Wang; Churan Zhi; Yuanzhe Hu; Tzu-Ping Chen; Lang Yin; Ze Chen; Tong Arthur Wu; Siru Ouyang; Zihan Wang; Jiaxin Pei; Julian McAuley; Yejin Choi; Alex Pentland","publication_date":"2026-02-18","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2602.16313","date_added":""},{"row_id":"ale-0429","title":"Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations","url":"https://arxiv.org/abs/2606.00832","canonical_url":"https://arxiv.org/abs/2606.00832","annotation":"Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","key_contribution":"Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for persistent, tool-mediated task completion across multiple sessions, finding that agents fail by treating prior-session history as current context instead of stale state that needs re-validation.","impact":"Gives readers a concrete source in Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;context;verification;state;exit","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Adril Putra Merin; David Anugraha; Ayu Purwarianti; Genta Indra Winata","publication_date":"2026-05-30","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Preprint","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.00832","date_added":""},{"row_id":"ale-0430","title":"π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows","url":"https://arxiv.org/abs/2605.14678","canonical_url":"https://arxiv.org/abs/2605.14678","annotation":"Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","key_contribution":"Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark of 100 multi-turn tasks across 5 user personas with hidden intents, inter-task dependencies, and cross-session continuity, measuring agent proactivity separately from task completion in long-horizon trajectories.","impact":"Gives readers a concrete source in π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification;exit","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Haoran Zhang; Luxin Xu; Zhilin Wang; Runquan Gui; Shunkai Zhang; Haodi Lei; Zihao He; Bingsu He; Chicheng Qin; Tong Zhu; Xiaoye Qu; Yang Yang; Yu Cheng; Yafu Li","publication_date":"2026-05-14","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"44 pages","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2605.14678","date_added":""},{"row_id":"ale-0431","title":"Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation","url":"https://arxiv.org/abs/2603.23638","canonical_url":"https://arxiv.org/abs/2603.23638","annotation":"A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","key_contribution":"A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","novelty":"The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: A 132-month CFO simulation where agents repeat a monthly cycle of liquidity management, financial closings, and financing decisions with compounding state, and only 15.4% of trials survive the full horizon.","impact":"Gives readers a concrete source in Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"state","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Yi Han; Yan Wang; Lingfei Qian; Haohang Li; Yupeng Cao; Yueru He; Xueqing Peng; Nanhan Shen; Yitao Xu; Yankai Chen; Dongji Feng; Jimin Huang; Xue Liu; Jian-Yun Nie; Sophia Ananiadou","publication_date":"2026-03-24","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.23638","date_added":""},{"row_id":"ale-0432","title":"EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer","url":"https://arxiv.org/abs/2607.05202","canonical_url":"https://arxiv.org/abs/2607.05202","annotation":"Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","key_contribution":"Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","novelty":"Control flow is represented as an inspectable graph rather than an opaque prompt loop. Resource-specific angle: Benchmark isolating whether agents transfer reusable procedures such as searching, debugging, and verification across episodes in four long-horizon domains linked by ability graphs, finding curated experience transfers but no automatic method yields consistent gains.","impact":"Gives readers a concrete source in EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Xingze Gao; Chuanrui Hu; Hongda Chen; Pengfei Yao; Zhao Wang; Yi Bai; Zhengwei Wu; Yunyun Han; Xiaofeng Cong; Jie Gui; Yafeng Deng; Teng Li","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"15 pages, 2 figures, 8 tables","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05202","date_added":""},{"row_id":"ale-0433","title":"AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents","url":"https://arxiv.org/abs/2607.02255","canonical_url":"https://arxiv.org/abs/2607.02255","annotation":"Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","key_contribution":"Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","novelty":"Persistent memory is treated as an external runtime artifact. Resource-specific angle: Bounded-memory testbed built on Slay the Spire 2 where every agent decision is made from a fresh prompt assembled by typed retrieval over recorded state, keeping prompt size bounded across runs of any length, with 298 documented trajectories released.","impact":"Gives readers a concrete source in AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;state","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Xiangchen Cheng; Yunwei Jiang; Jianwen Sun; Zizhen Li; Chuanhao Li; Xiangcheng Cao; Yihao Liu; Fanrui Zhang; Li Jin; Kaipeng Zhang","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.02255","date_added":""},{"row_id":"ale-0434","title":"Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops","url":"https://arxiv.org/abs/2607.05197","canonical_url":"https://arxiv.org/abs/2607.05197","annotation":"Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","key_contribution":"Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Empirical evaluation of iteration budgets for generate-validate-repair loops across code generation, test generation, and translation, finding the first three to four iterations capture most gains and that orchestration and feedback design matter more than the model.","impact":"Gives readers a concrete source in Is Three the Magic Number? An Empirical Evaluation of LLM-Based Repair Loops when they need to measure progress and gate completion with repeatable evidence.","signal":"Research source arXiv:2607.05197; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"delegation;verification;budget","audience":"researcher;evaluator","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Tobias Kiecker; Eik Reichmann; Hosung Kang; Gabin An; Lars Grunske","publication_date":"2026-07-06","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"4 Pages (+1 for references), NIER Paper","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.05197","date_added":""},{"row_id":"ale-0435","title":"DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks","url":"https://arxiv.org/abs/2607.07946","canonical_url":"https://arxiv.org/abs/2607.07946","annotation":"113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","key_contribution":"113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","novelty":"The work targets tasks that exceed a single context window or prompt session. Resource-specific angle: 113 from-scratch, contamination-free long-horizon software-engineering tasks with custom verifiers that accept any correct implementation, built to sidestep SWE-bench-style pretraining recall and better differentiate frontier coding agents.","impact":"Gives readers a concrete source in DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Wenqi Huang; Charley Lee; Leonard Tng; Serena Ge","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"32 pages, 10 figures. Code and data: https://github.com/datacurve-ai/deep-swe ; https://deepswe.datacurve.ai/","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07946","date_added":""},{"row_id":"ale-0436","title":"PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization","url":"https://arxiv.org/abs/2607.07744","canonical_url":"https://arxiv.org/abs/2607.07744","annotation":"Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","key_contribution":"Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Benchmarks the profile-diagnose-edit-verify loop where the verifier is a profiler rather than a test suite: agents must deliver measured, reproducible speedups without breaking correctness, and across seven agent configurations the framework choice shifts results even with identical models.","impact":"Gives readers a concrete source in PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Yingyun Cui; Yi Xie; Piaohong Wang; Jiawei Ma; Bo Liu; Liangliang Cao","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07744","date_added":""},{"row_id":"ale-0437","title":"Benchmarking coding agents on Databricks' multi-million line codebase","url":"https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase","canonical_url":"https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase","annotation":"Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","key_contribution":"Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Databricks engineering post (July 8, 2026, authors including Matei Zaharia and Patrick Wendell) on an internal benchmark built from real merged PRs with test-suite verification, finding that models cluster into three capability tiers, token price is a poor proxy for end-to-end task cost, and harness choice matters, with their Pi harness sending about 3x less context per turn at equal quality.","impact":"Gives readers a concrete source in Benchmarking coding agents on Databricks' multi-million line codebase when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"context;verification;budget","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"","publication_date":"2026","publication_year":"2026","publication_venue":"","publisher":"Databricks","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0438","title":"UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks","url":"https://arxiv.org/abs/2607.08768","canonical_url":"https://arxiv.org/abs/2607.08768","annotation":"Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","key_contribution":"Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","novelty":"Checkpointed state makes long-running agent work recoverable across failures. Resource-specific angle: Capability-driven benchmark of 400 bilingual tasks for proactive agents operating everyday tools in live Docker environments with step-level checkpoints, decomposed into five foundational capabilities, skill usage, exploration, long-context reasoning, multimodal understanding, and cross-platform coordination, so failures localize to a root-cause capability instead of mixing capabilities per task, with closed-loop evaluation using multiple agent roles to simulate human feedback without leaking grading criteria.","impact":"Gives readers a concrete source in UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"workspace;context;verification;state;escalation","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Zhekai Chen; Chengqi Duan; Kaiyue Sun; Bohao Li; Yuqing Wang; Manyuan Zhang; Xihui Liu","publication_date":"2026-07-09","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Project Page: https://uniclawbench.github.io | GitHub Repo: https://github.com/HKU-MMLab/UniClawBench","primary_category":"cs.CL","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.08768","date_added":""},{"row_id":"ale-0439","title":"SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills","url":"https://arxiv.org/abs/2607.09016","canonical_url":"https://arxiv.org/abs/2607.09016","annotation":"Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","key_contribution":"Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Benchmark for whether agent loops respect the logical relations inside skill files (preconditions, constraints, fallbacks): 70% of 5,000+ public skills contain at least one such relation, and on 86 executable cases leading coding agents show unsafe-behavior rates up to 70%, with an inference-time scaffold cutting violations by 63%.","impact":"Gives readers a concrete source in SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills when they need to measure progress and gate completion with repeatable evidence.","signal":"Benchmark or leaderboard source with repeatable tasks or scores; compare systems only after checking setup and scope.","resource_type":"Benchmark","collection":"Verify","user_goal":"Gate progress with tests, evals, and evidence.","section":"Benchmarks And Evaluation","section_slug":"benchmarks-and-evaluation","lifecycle_stages":"verification","audience":"researcher;evaluator","evidence_class":"benchmark","signal_strength":"high","source_status":"ok","authors":"Xuan Chen; Chengpeng Wang; Lu Yan; Xiangyu Zhang","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.CR","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.09016","date_added":""},{"row_id":"ale-0440","title":"SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution","url":"https://arxiv.org/abs/2603.13428","canonical_url":"https://arxiv.org/abs/2603.13428","annotation":"Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","key_contribution":"Commit-history-derived milestone task streams where agents must preserve system integrity across successive runs - frontier-model scores collapse from >80% on isolated tasks to at most 38% in continuous settings, quantifying the error-accumulation gap loop engineering targets.","novelty":"Links loop design to measurable tasks where progress and failure can be compared. 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Resource-specific angle: Cautions that agentic systems fail at boundaries when permissions, verification, traceability, and escalation are weak.","impact":"Gives readers a concrete source in Engineering Agentic Systems for Reliability when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from pruningmypothos.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"workspace;verification;escalation","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"Shailesh Rawat","publication_date":"","publication_year":"","publication_venue":"","publisher":"Sans Serif Systems","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0474","title":"Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared","url":"https://callsphere.ai/blog/self-correcting-agents-reflexion-critic-react-loops-compared-2026","canonical_url":"https://callsphere.ai/blog/self-correcting-agents-reflexion-critic-react-loops-compared-2026","annotation":"Compares self-correction patterns and their cost/failure tradeoffs.","key_contribution":"Compares self-correction patterns and their cost/failure tradeoffs.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Compares self-correction patterns and their cost/failure tradeoffs.","impact":"Gives readers a concrete source in Self-Correcting Agents: Reflexion, CRITIC, and ReAct Loops Compared when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from callsphere.ai; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification;budget","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"CallSphere","publication_date":"2026-04-24","publication_year":"2026","publication_venue":"","publisher":"CallSphere","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0475","title":"How to Build an AI Agent Harness: A 2026 Complete Guide","url":"https://atlan.com/know/how-to-build-ai-agent-harness/","canonical_url":"https://atlan.com/know/how-to-build-ai-agent-harness/","annotation":"Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","key_contribution":"Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Broad guide with useful warnings on data readiness, permissions, context management, and evaluation.","impact":"Gives readers a concrete source in How to Build an AI Agent Harness: A 2026 Complete Guide when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from atlan.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"workspace;context;verification","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"","publication_year":"","publication_venue":"","publisher":"atlan.com","doi":"","publication_note":"","primary_category":"","metadata_source":"domain-fallback","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0476","title":"Harness Engineering vs Prompt Engineering vs Context Engineering Explained","url":"https://medium.com/@visrow/harness-engineering-vs-prompt-engineering-vs-context-engineering-explained-0423b692c87d","canonical_url":"https://medium.com/@visrow/harness-engineering-vs-prompt-engineering-vs-context-engineering-explained-0423b692c87d","annotation":"Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.","key_contribution":"Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.","novelty":"Context is managed as durable loop state rather than a single prompt payload. Resource-specific angle: Adjacent framing that helps avoid confusing loop engineering with the surrounding harness discipline.","impact":"Gives readers a concrete source in Harness Engineering vs Prompt Engineering vs Context Engineering Explained when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from medium.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"context","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"Vishal Mysore","publication_date":"2026-05-19","publication_year":"2026","publication_venue":"","publisher":"Medium","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0477","title":"Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering","url":"https://arxiv.org/abs/2606.17799","canonical_url":"https://arxiv.org/abs/2606.17799","annotation":"Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","key_contribution":"Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","novelty":"The work turns loop quality into a measurable task or score. Resource-specific angle: Argues benchmark scores conflate the model with the harness and penalize valid alternatives, so headline numbers hide which loop and harness choices actually move performance.","impact":"Gives readers a concrete source in Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2606.17799; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Maria I. Gorinova; Macey Baker; Amy Heineike; Maksim Shaposhnikov; Rob Willoughby; Dru Knox","publication_date":"2026-06-16","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.17799","date_added":""},{"row_id":"ale-0478","title":"Understanding the Challenges in Iterative Generative Optimization with LLMs","url":"https://arxiv.org/abs/2603.23994","canonical_url":"https://arxiv.org/abs/2603.23994","annotation":"Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","key_contribution":"Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Empirically isolates three hidden design choices that make self-improving agent loops succeed or fail - starting artifacts, credit horizons over execution traces, and batching strategy - explaining why iterative refinement loops stay brittle in production.","impact":"Gives readers a concrete source in Understanding the Challenges in Iterative Generative Optimization with LLMs when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2603.23994; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget;escalation;exit","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Allen Nie; Xavier Daull; Zhiyi Kuang; Abhinav Akkiraju; Anish Chaudhuri; Max Piasevoli; Ryan Rong; YuCheng Yuan; Prerit Choudhary; Shannon Xiao; Rasool Fakoor; Adith Swaminathan; Ching-An Cheng","publication_date":"2026-03-25","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"39 pages, 17 figures","primary_category":"cs.LG","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2603.23994","date_added":""},{"row_id":"ale-0479","title":"The Illusion of Multi-Agent Advantage","url":"https://arxiv.org/abs/2606.13003","canonical_url":"https://arxiv.org/abs/2606.13003","annotation":"Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","key_contribution":"Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","novelty":"Evaluation data is used as the feedback signal for improving loop behavior. Resource-specific angle: Systematic evaluation showing automatically generated multi-agent systems consistently underperform chain-of-thought self-consistency while costing up to 10x more, cautioning that auto-designed orchestration adds complexity without functional benefit.","impact":"Gives readers a concrete source in The Illusion of Multi-Agent Advantage when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2606.13003; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"delegation;verification","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Prathyusha Jwalapuram; Hehai Lin; Chuyuan Li; Fangkai Jiao; Sudong Wang; Yifei Ming; Zixuan Ke; Chengwei Qin; Giuseppe Carenini; Shafiq Joty","publication_date":"2026-06-11","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.13003","date_added":""},{"row_id":"ale-0480","title":"The Coming Loop","url":"https://lucumr.pocoo.org/2026/6/23/the-coming-loop/","canonical_url":"https://lucumr.pocoo.org/2026/6/23/the-coming-loop/","annotation":"Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","key_contribution":"Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Flask creator Armin Ronacher's skeptical essay on harness loops, examining what continuously re-driving agents past their natural stopping points does to code quality, review capacity, and human understanding of the resulting systems.","impact":"Gives readers a concrete source in The Coming Loop when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from lucumr.pocoo.org; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"escalation;exit","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-06-23","publication_year":"2026","publication_venue":"","publisher":"Armin Ronacher's Thoughts and Writings","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0481","title":"Loop Engineering, the Latest AI Buzzword, Still Needs Humans in the Loop","url":"https://www.theregister.com/ai-and-ml/2026/06/24/loop-engineering-latest-ai-buzzword-still-needs-humans-in-the-loop/5261735","canonical_url":"https://www.theregister.com/ai-and-ml/2026/06/24/loop-engineering-latest-ai-buzzword-still-needs-humans-in-the-loop/5261735","annotation":"The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","key_contribution":"The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: The Register's report on the June 2026 loop-engineering discussion, collecting the Steinberger, Osmani, and Cherny quotes while arguing that vendor token-consumption incentives and model non-determinism keep humans in the loop.","impact":"Gives readers a concrete source in Loop Engineering, the Latest AI Buzzword, Still Needs Humans in the Loop when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from www.theregister.com; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-06-24","publication_year":"2026","publication_venue":"","publisher":"theregister","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0482","title":"When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents","url":"https://arxiv.org/abs/2607.01641","canonical_url":"https://arxiv.org/abs/2607.01641","annotation":"Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","key_contribution":"Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Characterizes infinite agentic loops, a failure class where unbounded feedback paths make agents repeat calls, tools, or handoffs forever, and ships IAL-Scan, a static analyzer that confirmed 68 real cases across 47 of 6,549 scanned agent projects at 91.9% precision.","impact":"Gives readers a concrete source in When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.01641; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"workspace;delegation;exit","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xinyi Hou; Shenao Wang; Yanjie Zhao; Haoyu Wang","publication_date":"2026-07-02","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.SE","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.01641","date_added":""},{"row_id":"ale-0483","title":"The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents","url":"https://arxiv.org/abs/2607.07436","canonical_url":"https://arxiv.org/abs/2607.07436","annotation":"Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.","key_contribution":"Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Shows via corrupted-reward analysis that false-pass bias in an LLM judge silently disables the skill-retirement mechanism that keeps a self-evolving agent's growing skill library from drifting below the no-skill baseline.","impact":"Gives readers a concrete source in The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07436; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget;escalation;exit","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Xing Zhang; Yanwei Cui; Guanghui Wang; Ziyuan Li; Wei Qiu; Bing Zhu; Peiyang He","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07436","date_added":""},{"row_id":"ale-0484","title":"Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows","url":"https://arxiv.org/abs/2607.07504","canonical_url":"https://arxiv.org/abs/2607.07504","annotation":"Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","key_contribution":"Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Negative result for low-curation skill libraries: across four data-science lifecycle stages (56 tasks), fully LLM-generated skill files show no reliable improvement over plain task prompting, and component ablations find no skill part that helps either (all p > 0.396). Useful counterweight to the skill-generation enthusiasm in self-evolving agent stacks, curation still matters.","impact":"Gives readers a concrete source in Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2607.07504; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"budget;escalation;exit","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Wei-Jung Huang","publication_date":"2026-07-08","publication_year":"2026","publication_venue":"KDD 2026 Workshop on AI Data Scientist","publisher":"arXiv","doi":"","publication_note":"KDD 2026 Workshop on AI Data Scientist","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2607.07504","date_added":""},{"row_id":"ale-0485","title":"The Verification Horizon: No Silver Bullet for Coding Agent Rewards","url":"https://arxiv.org/abs/2606.26300","canonical_url":"https://arxiv.org/abs/2606.26300","annotation":"Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","key_contribution":"Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","novelty":"Verification is promoted from a final check to a loop-control signal. Resource-specific angle: Position paper arguing verification has become harder than generation for coding agents: every verifier is only a proxy for underspecified human intent, so reward design faces a horizon that no single verification mechanism crosses.","impact":"Gives readers a concrete source in The Verification Horizon: No Silver Bullet for Coding Agent Rewards when they need to bound risk before recurring or unattended execution.","signal":"Research source arXiv:2606.26300; inspect its method and evaluation before treating results as production evidence.","resource_type":"Paper","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"verification;escalation","audience":"researcher;evaluator;operator;security","evidence_class":"research-preprint","signal_strength":"medium","source_status":"ok","authors":"Binghai Wang; Chenlong Zhang; Dayiheng Liu; Jiajun Zhang; Jiawei Chen; Mingze Li; Mouxiang Chen; Rongyao Fang; Siyuan Zhang; Xuwu Wang; Yuheng Jing; Zeyao Ma; Zeyu Cui","publication_date":"2026-06-24","publication_year":"2026","publication_venue":"arXiv","publisher":"arXiv","doi":"","publication_note":"Authors are listed alphabetically by their first names","primary_category":"cs.AI","metadata_source":"arxiv-api","github_repo":"","github_stars":"","arxiv_id":"2606.26300","date_added":""},{"row_id":"ale-0486","title":"Write Code Like a Human Will Maintain It","url":"https://unstack.io/write-code-like-a-human-will-maintain-it","canonical_url":"https://unstack.io/write-code-like-a-human-will-maintain-it","annotation":"Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","key_contribution":"Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","novelty":"Keeps adoption grounded in known failure modes, economics, and operational limits. Resource-specific angle: Argues that agent-driven codebases create a compounding feedback loop where every merged shortcut becomes training signal for the next generation of changes, so code quality standards matter more, not less, under automation.","impact":"Gives readers a concrete source in Write Code Like a Human Will Maintain It when they need to bound risk before recurring or unattended execution.","signal":"Contextual source from unstack.io; useful for practice signals or boundary conditions, not independent validation.","resource_type":"Critique","collection":"Govern","user_goal":"Bound permissions, cost, failure, and escalation.","section":"Critiques, Risks, And Limitations","section_slug":"critiques-risks-and-limitations","lifecycle_stages":"escalation","audience":"operator;security","evidence_class":"risk-analysis","signal_strength":"contextual","source_status":"ok","authors":"","publication_date":"2026-07-10","publication_year":"2026","publication_venue":"","publisher":"Unstack","doi":"","publication_note":"","primary_category":"","metadata_source":"html-meta","github_repo":"","github_stars":"","arxiv_id":"","date_added":""},{"row_id":"ale-0487","title":"Claude Code Sends 33k Tokens Before Reading the Prompt","url":"https://systima.ai/blog/claude-code-vs-opencode-token-overhead","canonical_url":"https://systima.ai/blog/claude-code-vs-opencode-token-overhead","annotation":"July 12, 2026 proxy-interception study of per-turn harness overhead: Claude Code sends ~33k tokens of scaffolding before user input versus OpenCode's ~7k (a 4.7x gap that narrows to 3.3x on newer models), mid-session cache-block rewrites produce up to 54x more cache-write tokens on identical tasks, a 72KB instruction file adds ~20k tokens per request, five MCP servers add 5-7k more, and subagent delegation alone multiplied total cost 4.2x. 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