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Hand-picked research papers on the AI agent ecosystem, published in 2026.

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# Awesome AI Agent Papers A curated collection of research papers **published in 2026** and sourced from arXiv, covering core topics from the AI agent ecosystem like **multi-agent coordination**, **memory & RAG**, **tooling**, **evaluation & observability**, and **security**. Whether you're an AI engineer building agent systems, a researcher exploring new architectures, or a developer integrating LLM agents into products, these papers help you stay on top of what's actually working, what's breaking, and where the field is heading. Updated weekly from arXiv. ### Why this list exists Hundreds of papers are published on arXiv every week, and a growing number of them touch on AI agents. We go through them all, filter the ones that are directly relevant to the AI agent ecosystem, and categorize them so you don't have to. This list only includes papers published from January 2026 onward. ### Table of Contents - [Multi-Agent](#multi-agent) (53) - [Memory & RAG](#memory--rag) (56) - [Eval & Observability](#eval--observability) (80) - [Agent Tooling](#agent-tooling) (95) - [AI Agent Security](#ai-agent-security) (82)

Multi-Agent (53)
| Paper | arXiv ID | |---|:---:| | **[AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing](https://arxiv.org/pdf/2602.17607)** - A multi-agent pipeline that reads a PDE problem description in plain text and writes, debugs, and validates a classical numerical solver end-to-end. Generates spectral and finite-difference code (no neural networks), scoring ~6 orders of magnitude below FNO and CodePDE baselines. | arXiv | | **[Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation](https://arxiv.org/abs/2604.09549)** - Evaluates recommender systems via agent-RS interactions. | arXiv | | **[CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery](https://arxiv.org/pdf/2604.01658)** - Introduces long-running multi-agent systems that self-evolve via shared persistent memory, asynchronous execution, and heartbeat-based interventions; 3–10× higher improvement rates than fixed evolutionary-search baselines on 10 math/algorithmic/systems tasks. | arXiv | | **[DyTopo: Dynamic Topology Routing for Multi-Agent Reasoning via Semantic Matching](https://arxiv.org/pdf/2602.06039v1)** - Investigates dynamically rewiring agent-to-agent connections at each reasoning round via semantic matching instead of fixed communication topologies. | arXiv | | **[RuleSmith: Multi-Agent LLMs for Automated Game Balancing](https://arxiv.org/pdf/2602.06232v1)** - Explores automated game balancing by combining multi-agent LLM self-play with Bayesian optimization on a civ-style game. | arXiv | | **[CommCP: Efficient Multi-Agent Coordination via LLM-Based Communication with Conformal Prediction](https://arxiv.org/pdf/2602.06038v1)** - Examines how conformal prediction can filter noisy inter-agent messages to improve multi-robot coordination. | arXiv | | **[AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions](https://arxiv.org/pdf/2602.06008v1)** - Introduces a 110+ task benchmark to evaluate how well multi-agent LLM systems handle buyer-seller negotiation through natural language. | arXiv | | **[Gender Dynamics and Homophily in a Social Network of LLM Agents](https://arxiv.org/pdf/2602.02606v1)** - Analyzes social network formation among 70K+ autonomous LLM agents on Chirper.ai to study emergent group behavior and bias. | arXiv | | **[ROMA: Recursive Open Meta-Agent Framework for Long-Horizon Multi-Agent Systems](https://arxiv.org/pdf/2602.01848v1)** - Proposes breaking large tasks into subtask trees that run in parallel across multiple agents to handle long-horizon workflows without exceeding context windows. | arXiv | | **[ORCH: many analyses, one merge — a deterministic multi-agent orchestrator](https://arxiv.org/pdf/2602.01797v1)** - Proposes a deterministic multi-agent orchestrator where multiple LLMs analyze a problem independently and a merge agent selects the best answer without any training. | arXiv | | **[H-AdminSim: A Multi-Agent Simulator for Realistic Hospital Administrative Workflows](https://arxiv.org/pdf/2602.05407v1)** - Simulates end-to-end hospital administrative workflows with multi-agent LLMs and FHIR integration to test LLM-driven automation in healthcare settings. | arXiv | | **[Agyn: A Multi-Agent System for Team-Based Autonomous Software Engineering](https://arxiv.org/pdf/2602.01465v2)** - Proposes a multi-agent system for autonomous software engineering that assigns specialized agents to roles like coordination, research, implementation, and review. | arXiv | | **[Multi-Agent Teams Hold Experts Back](https://arxiv.org/pdf/2602.01011v3)** - Examines whether self-organizing LLM agent teams can match or beat their best member's performance across collaborative benchmarks. | arXiv | | **[Evolving Interpretable Constitutions for Multi-Agent Coordination](https://arxiv.org/pdf/2602.00755v1)** - Explores using LLM-driven genetic programming to automatically discover behavioral norms for multi-agent coordination in a survival-pressure grid-world simulation. | arXiv | | **[Scaling Multiagent Systems with Process Rewards](https://arxiv.org/pdf/2601.23228v2)** - Proposes per-action process rewards from AI feedback to improve credit assignment and sample efficiency when finetuning multi-agent LLM systems. | arXiv | | **[MonoScale: Scaling Multi-Agent System with Monotonic Improvement](https://arxiv.org/pdf/2601.23219v1)** - Proposes a framework for safely growing multi-agent pools by generating familiarization tasks and building routing memory, with a guaranteed non-decreasing performance across onboarding rounds. | arXiv | | **[Task-Aware LLM Council with Adaptive Decision Pathways for Decision Support](https://arxiv.org/pdf/2601.22662v1)** - Proposes a task-adaptive multi-agent framework that routes control to the most suitable LLM at each decision step using semantic matching against each model's success history. | arXiv | | **[SYMPHONY: Synergistic Multi-agent Planning with Heterogeneous Language Model Assembly](https://arxiv.org/pdf/2601.22623v1)** - Explores using a pool of different LLM agents within MCTS planning to increase rollout diversity and improve multi-step reasoning. | arXiv | | **[Learning to Recommend Multi-Agent Subgraphs from Calling Trees](https://arxiv.org/pdf/2601.22209v1)** - Proposes a recommendation framework that uses historical calling trees to select the best agents or agent teams for each subtask in multi-agent orchestration. | arXiv | | **[Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic](https://arxiv.org/pdf/2601.21972v2)** - Investigates actor-critic reinforcement learning methods for training decentralized LLM agent collaboration across writing, coding, and game-playing tasks. | arXiv | | **[AgenticSimLaw: A Juvenile Courtroom Multi-Agent Debate Simulation for Explainable High-Stakes Tabular Decision Making](https://arxiv.org/pdf/2601.21936v1)** - Proposes a role-structured multi-agent courtroom debate framework with defined agent roles, interaction protocols, and private reasoning strategies for auditable high-stakes decision-making. | arXiv | | **[Epistemic Context Learning: Building Trust the Right Way in LLM-Based Multi-Agent Systems](https://arxiv.org/pdf/2601.21742v1)** - Introduces a reasoning framework that builds peer reliability profiles from interaction history so agents in multi-agent systems learn which peers to trust when uncertain. | arXiv | | **[Adaptive Confidence Gating in Multi-Agent Collaboration for Efficient and Optimized Code Generation](https://arxiv.org/pdf/2601.21469v1)** - Explores structured multi-agent debate with three role-based agents and adaptive confidence gating to improve small language model code generation. | arXiv | | **[CASTER: Context-Aware Strategy for Task Efficient Routing in Multi-Agent Systems](https://arxiv.org/pdf/2601.19793v1)** - Proposes a lightweight router for dynamic model selection in graph-based multi-agent systems that combines semantic embeddings with structural meta-features and self-optimizes through on-policy negative feedback. | arXiv | | **[Phase Transition for Budgeted Multi-Agent Synergy](https://arxiv.org/pdf/2601.17311v1)** - Develops a theory for predicting when budgeted multi-agent LLM systems improve, saturate, or collapse based on context windows, communication fidelity, and shared-error correlation. | arXiv | | **[Dynamic Role Assignment for Multi-Agent Debate](https://arxiv.org/pdf/2601.17152v1)** - Proposes a meta-debate framework that dynamically assigns roles in multi-agent systems by matching model capabilities to positions through proposal and peer review stages. | arXiv | | **[Learning to Collaborate: An Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federation](https://arxiv.org/pdf/2601.17133v1)** - Introduces orchestrated decentralized peer-to-peer LLM collaboration that uses contextual bandits to learn optimal matchmaking between heterogeneous agents via secure distillation. | arXiv | | **[Mixture-of-Models: Unifying Heterogeneous Agents via N-Way Self-Evaluating Deliberation](https://arxiv.org/pdf/2601.16863v1)** - Explores a runtime Mixture-of-Models architecture with a dynamic expertise broker and quadratic voting consensus that enables small model ensembles to match frontier performance. | arXiv | | **[Multi-Agent Constraint Factorization Reveals Latent Invariant Solution Structure](https://arxiv.org/pdf/2601.15077v1)** - Formalizes through operator theory why multi-agent LLM systems access invariant solutions that a single agent applying all constraints simultaneously cannot reach. | arXiv | | **[MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks](https://arxiv.org/pdf/2601.14652v2)** - Proposes a training-time framework that formulates multi-agent orchestration as function-calling reinforcement learning with holistic system-level reasoning and introduces MASBENCH for controlled evaluation. | arXiv | | **[MASCOT: Towards Multi-Agent Socio-Collaborative Companion Systems](https://arxiv.org/pdf/2601.14230v1)** - Proposes a bi-level optimization framework for multi-agent companions that aligns individual personas via RLAIF and optimizes collaborative dialogue through group-level meta-policy rewards. | arXiv | | **[If You Want Coherence, Orchestrate a Team of Rivals: Multi-Agent Models of Organizational Intelligence](https://arxiv.org/pdf/2601.14351v1)** - Explores a team-of-rivals multi-agent architecture with specialized roles and a remote code executor that separates reasoning from data execution to maintain clean context windows. | arXiv | | **[The Orchestration of Multi-Agent Systems: Architectures, Protocols, and Enterprise Adoption](https://arxiv.org/pdf/2601.13671v1)** - Formalizes a unified architectural framework for orchestrated multi-agent systems integrating MCP for tool access and Agent2Agent protocol for peer coordination, delegation, and policy enforcement. | arXiv | | **[MARO: Learning Stronger Reasoning from Social Interaction](https://arxiv.org/pdf/2601.12323v2)** - Proposes Multi-Agent Reward Optimization, a method that decomposes multi-agent social interaction outcomes into per-behavior learning signals to improve LLM reasoning through simulated social environments. | arXiv | | **[LSTM-MAS: A Long Short-Term Memory Inspired Multi-Agent System for Long-Context Understanding](https://arxiv.org/pdf/2601.11913v1)** - Introduces an LSTM-inspired multi-agent architecture with worker, filter, judge, and manager agents that emulate gated memory mechanisms to control information flow for long-context understanding. | arXiv | | **[Do We Always Need Query-Level Workflows? Rethinking Agentic Workflow Generation for Multi-Agent Systems](https://arxiv.org/pdf/2601.11147v1)** - Examines whether query-level workflow generation is always necessary in multi-agent systems and proposes a low-cost task-level framework that uses self-prediction with few-shot calibration instead of full execution. | arXiv | | **[Learning Latency-Aware Orchestration for Parallel Multi-Agent Systems](https://arxiv.org/pdf/2601.10560v1)** - Proposes a latency-aware multi-agent orchestration framework that explicitly optimizes the critical execution path under parallel execution to reduce end-to-end latency while maintaining task performance. | arXiv | | **[TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems](https://arxiv.org/pdf/2601.10120v1)** - Proposes a one-shot topology generation framework with diverse interaction modes that enables decentralized agents to autonomously construct heterogeneous communication topologies without iterative coordination. | arXiv | | **[Beyond Rule-Based Workflows: An Information-Flow-Orchestrated Multi-Agents Paradigm via A2A Communication from CORAL](https://arxiv.org/pdf/2601.09883v1)** - Replaces predefined multi-agent workflows with a dynamic information-flow orchestrator that coordinates agents through natural-language A2A communication. | arXiv | | **[LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities](https://arxiv.org/pdf/2601.09822v2)** - Reviews LLM-based multi-agent systems across the software development lifecycle, covering frameworks, communication protocols, and orchestration challenges from requirements to debugging. | arXiv | | **[Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning](https://arxiv.org/pdf/2601.09667v2)** - Explores injecting structured textual experience into multi-agent deliberation at test time to improve reasoning accuracy without any model tuning. | arXiv | | **[The End of Reward Engineering: How LLMs Are Redefining Multi-Agent Coordination](https://arxiv.org/pdf/2601.08237v1)** - Argues that LLMs can replace hand-crafted numerical reward functions with language-based objective specifications for multi-agent coordination, drawing on EUREKA and RLVR as evidence. | arXiv | | **[A Large-Scale Study on the Development and Issues of Multi-Agent AI Systems](https://arxiv.org/pdf/2601.07136v1)** - Analyzes over 42K commits and 4.7K resolved issues across eight leading multi-agent AI systems (LangChain, CrewAI, AutoGen, etc.) to study development patterns, maintenance practices, and ecosystem maturity. | arXiv | | **[StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management](https://arxiv.org/pdf/2601.05890v1)** - Proposes a hierarchical multi-agent framework that decouples high-level coordination from subtask execution with active task-level memory control and reinforcement-learning-driven experience reuse. | arXiv | | **[CTHA: Constrained Temporal Hierarchical Architecture for Stable Multi-Agent LLM Systems](https://arxiv.org/pdf/2601.10738v1)** - Proposes a constrained temporal hierarchical architecture for multi-agent LLM systems that projects inter-layer communication onto structured manifolds with typed message contracts and authority bounds. | arXiv | | **[DynaDebate: Breaking Homogeneity in Multi-Agent Debate with Dynamic Path Generation](https://arxiv.org/pdf/2601.05746v1)** - Introduces dynamic path generation for multi-agent debate that allocates diverse solution paths to agents, shifts focus to step-by-step logic critique, and uses a trigger-based verification agent to resolve deadlocks. | arXiv | | **[Demystifying Multi-Agent Debate: The Role of Confidence and Diversity](https://arxiv.org/pdf/2601.19921v1)** - Investigates how diversity-aware initialization and confidence-modulated updates improve multi-agent debate, connecting findings from human deliberation research to LLM-based debate protocols. | arXiv | | **[Orchestrating Intelligence: Confidence-Aware Routing for Multi-Agent Collaboration](https://arxiv.org/pdf/2601.04861v2)** - Proposes a multi-agent framework with confidence-aware routing that dynamically selects agent roles and model scales across heterogeneous LLMs based on task complexity. | arXiv | | **[Belief in Authority: Impact of Authority in Multi-Agent Evaluation Framework](https://arxiv.org/pdf/2601.04790v1)** - Analyzes role-based authority bias in multi-agent evaluation frameworks using French and Raven's power-based theory across legitimate, referent, and expert power types. | arXiv | | **[When Single-Agent with Skills Replace Multi-Agent Systems and When They Fail](https://arxiv.org/pdf/2601.04748v2)** - Investigates when a single agent with a skill library can replace multi-agent systems, studying scaling limits and phase transitions in skill selection as libraries grow. | arXiv | | **[ResMAS: Resilience Optimization in LLM-based Multi-Agent Systems](https://arxiv.org/pdf/2601.04694v1)** - Proposes a two-stage framework for enhancing multi-agent system resilience through RL-based topology generation and topology-aware prompt optimization under perturbations. | arXiv | | **[TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration](https://arxiv.org/pdf/2601.04544v1)** - Proposes an adaptive reasoning router for multi-agent systems that generates natural-language reasoning chains before predicting candidate agents, with a collaborative execution pipeline. | arXiv | | **[When Numbers Start Talking: Implicit Numerical Coordination Among LLM-Based Agents](https://arxiv.org/pdf/2601.03846v1)** - Investigates covert communication in LLM multi-agent systems through game-theoretic analysis of implicit coordination signals across different communication regimes. | arXiv | | **[Bayesian Orchestration of Multi-LLM Agents for Cost-Aware Sequential Decision-Making](https://arxiv.org/pdf/2601.01522v1)** - Proposes a Bayesian, cost-aware multi-LLM orchestration framework that treats LLMs as approximate likelihood models and aggregates across diverse models for sequential decision-making. | arXiv | | **[OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents](https://arxiv.org/pdf/2504.16918)** - Turns natural-language optimization problems into working solver code with a four-agent pipeline (Formulator, Planner, Coder, Critic) and UCB bandit scheduling over candidate formulations. Mixing Gemma 27B and Llama 70B beats any single model; gets 88% on NLP4LP vs. 72% prior best. | arXiv |

Memory & RAG (56)
| Paper | arXiv ID | |---|:---:| | **[BudgetMem: Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory](https://arxiv.org/pdf/2602.06025v1)** - Investigates routing agent memory queries to different processing tiers based on query difficulty to control the cost-accuracy trade-off at runtime. | arXiv | | **[Learning to Share: Selective Memory for Efficient Parallel Agentic Systems](https://arxiv.org/pdf/2602.05965v1)** - Proposes a shared memory bank with a learned controller that decides what information is worth passing between parallel agent teams to reduce redundant work. | arXiv | | **[CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering](https://arxiv.org/pdf/2602.05728v1)** - Explores converting a corpus into atomic QA pairs offline to resolve multi-hop questions with just two LLM calls regardless of hop count. | arXiv | | **[Mitigating Hallucination in Financial Retrieval-Augmented Generation via Fine-Grained Knowledge Verification](https://arxiv.org/pdf/2602.05723v1)** - Examines breaking financial RAG answers into atomic facts and verifying each against retrieved documents using reinforcement learning rewards. | arXiv | | **[Graph-based Agent Memory: Taxonomy, Techniques, and Applications](https://arxiv.org/pdf/2602.05665v1)** - Surveys graph-based memory architectures for agents, covering extraction, storage, retrieval, and how memory evolves over time. | arXiv | | **[AI Agent Systems for Supply Chains: Structured Decision Prompts and Memory Retrieval](https://arxiv.org/pdf/2602.05524v1)** - Proposes a multi-agent system for inventory management that retrieves similar past decisions to adapt ordering across various supply chain scenarios. | arXiv | | **[SOPRAG: Multi-view Graph Experts Retrieval for Industrial Standard Operating Procedures](https://arxiv.org/pdf/2602.01858v1)** - Explores replacing flat chunk-based RAG with graph experts that understand entity relationships, causality, and process flows for structured documents like SOPs. | arXiv | | **[ProcMEM: Learning Reusable Procedural Memory from Experience via Non-Parametric PPO for LLM Agents](https://arxiv.org/pdf/2602.01869v1)** - Investigates letting agents save step-by-step procedural skills from past runs and reuse them later without retraining to reduce repeated computation. | arXiv | | **[Aggregation Queries over Unstructured Text: Benchmark and Agentic Method](https://arxiv.org/pdf/2602.01355v2)** - Proposes an agentic method for aggregation queries over unstructured text that tries to find all matching evidence, breaking the task into disambiguation, filtering, and aggregation stages. | arXiv | | **[DIVERGE: Diversity-Enhanced RAG for Open-Ended Information Seeking](https://arxiv.org/pdf/2602.00238v1)** - Proposes an agentic RAG framework that uses reflection and memory-based refinement to generate diverse answers for open-ended questions. | arXiv | | **[JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG](https://arxiv.org/pdf/2601.21916v1)** - Proposes joint optimization of planning and execution in agentic RAG by modeling the system as a cooperative multi-agent team with shared backbone and outcome-based rewards. | arXiv | | **[ProRAG: Process-Supervised Reinforcement Learning for Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.21912v1)** - Proposes process-supervised reinforcement learning for RAG that uses MCTS-based step-level rewards to identify and fix flawed reasoning steps in multi-hop retrieval. | arXiv | | **[E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory](https://arxiv.org/pdf/2601.21714v1)** - Introduces an episodic memory framework where assistant agents maintain uncompressed memory contexts while a master agent orchestrates global planning, replacing destructive memory compression with context reconstruction. | arXiv | | **[ShardMemo: Masked MoE Routing for Sharded Agentic LLM Memory](https://arxiv.org/pdf/2601.21545v1)** - Proposes a tiered memory service for agentic LLM systems that uses masked mixture-of-experts routing to probe only eligible memory shards under a fixed budget. | arXiv | | **[When should I search more: Adaptive Complex Query Optimization with Reinforcement Learning](https://arxiv.org/pdf/2601.21208v1)** - Explores adaptive query optimization in RAG using reinforcement learning to dynamically decide when to split complex queries into sub-queries and fuse the retrieved results. | arXiv | | **[A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning](https://arxiv.org/pdf/2601.21162v1)** - Introduces an adaptive agentic Graph-RAG framework that verifies evidence sufficiency and progressively escalates retrieval effort, mapping graph signals back to source text to handle extraction loss. | arXiv | | **[MemCtrl: Using MLLMs as Active Memory Controllers on Embodied Agents](https://arxiv.org/pdf/2601.20831v1)** - Investigates augmenting multimodal LLMs with a trainable memory gate that decides which observations to retain, update, or discard during online embodied agent exploration. | arXiv | | **[AMA: Adaptive Memory via Multi-Agent Collaboration](https://arxiv.org/pdf/2601.20352v2)** - Proposes a multi-agent memory framework with hierarchical granularity, adaptive query routing, consistency verification, and targeted memory refresh for long-term agent interaction. | arXiv | | **[When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering](https://arxiv.org/pdf/2601.19827v2)** - Examines when iterative retrieval-reasoning loops outperform static gold-context RAG in scientific multi-hop QA, diagnosing failure modes across retrieval coverage, hypothesis drift, and stopping calibration. | arXiv | | **[Dep-Search: Learning Dependency-Aware Reasoning Traces with Persistent Memory](https://arxiv.org/pdf/2601.18771v1)** - Introduces a dependency-aware search framework that uses GRPO reinforcement learning to teach LLMs to decompose questions with dependency relationships and store intermediate results in persistent memory. | arXiv | | **[FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory](https://arxiv.org/pdf/2601.18642v2)** - Proposes a biologically-inspired agent memory architecture with adaptive exponential decay, LLM-guided conflict resolution, and intelligent memory fusion across a dual-layer hierarchy. | arXiv | | **[FastInsight: Fast and Insightful Retrieval via Fusion Operators for Graph RAG](https://arxiv.org/pdf/2601.18579v1)** - Explores two fusion operators for Graph RAG that combine graph-aware reranking with semantic-topological expansion to improve retrieval accuracy and generation quality. | arXiv | | **[Less is More for RAG: Information Gain Pruning for Generator-Aligned Reranking and Evidence Selection](https://arxiv.org/pdf/2601.17532v1)** - Proposes a generator-aligned reranking and pruning module for RAG that selects evidence using utility signals and filters weak or harmful passages before context truncation. | arXiv | | **[DeepEra: A Deep Evidence Reranking Agent for Scientific Retrieval-Augmented Generated Question Answering](https://arxiv.org/pdf/2601.16478v1)** - Introduces a step-by-step reasoning reranking agent for RAG that distinguishes semantically similar but logically irrelevant passages in retrieval-augmented question answering. | arXiv | | **[SPARC-RAG: Adaptive Sequential-Parallel Scaling with Context Management for Retrieval-Augmented Generation](https://arxiv.org/pdf/2602.00083v1)** - Introduces a multi-agent RAG framework that coordinates sequential and parallel inference-time scaling under unified context management to prevent contamination and improve multi-hop reasoning. | arXiv | | **[Incorporating Q&A Nuggets into Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.13222v1)** - Proposes a nugget-augmented generation system that constructs a bank of Q&A nuggets from retrieved documents to guide extraction, selection, and report generation with citation provenance. | arXiv | | **[Augmenting Question Answering with A Hybrid RAG Approach](https://arxiv.org/pdf/2601.12658v2)** - Introduces a hybrid RAG architecture combining query augmentation, agentic routing, and structured retrieval that merges vector and graph-based techniques with context unification for question answering. | arXiv | | **[Utilizing Metadata for Better Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.11863v1)** - Presents a systematic study of metadata-aware retrieval strategies for RAG, comparing prefix, suffix, unified embedding, and late-fusion approaches with field-level ablations on embedding space structure. | arXiv | | **[Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration](https://arxiv.org/pdf/2601.11144v3)** - Proposes a hierarchical global-to-local retrieval strategy for GraphRAG with beam search-optimized re-ranking and a compact LLM integration module trained via dynamic-weighting reinforcement learning. | arXiv | | **[Grounding Agent Memory in Contextual Intent](https://arxiv.org/pdf/2601.10702v1)** - Introduces an agentic memory system that indexes trajectory steps with structured contextual intent cues and retrieves history by intent compatibility to reduce interference in long-horizon goal-oriented interactions. | arXiv | | **[Structure and Diversity Aware Context Bubble Construction for Enterprise Retrieval Augmented Systems](https://arxiv.org/pdf/2601.10681v1)** - Proposes a structure-informed and diversity-constrained context bubble construction framework for RAG that preserves document structure and balances relevance, coverage, and redundancy under strict token budgets. | arXiv | | **[Topo-RAG: Topology-aware retrieval for hybrid text-table documents](https://arxiv.org/pdf/2601.10215v1)** - Introduces a dual-architecture RAG framework that routes narrative through dense retrievers and tabular data through a cell-aware late interaction mechanism to preserve spatial relationships in hybrid documents. | arXiv | | **[Continuum Memory Architectures for Long-Horizon LLM Agents](https://arxiv.org/pdf/2601.09913v1)** - Defines a class of memory systems for long-horizon agents that maintain persistent, temporally chained internal state instead of stateless RAG lookups, specifying the architectural requirements they must satisfy. | arXiv | | **[Rethinking Memory Mechanisms of Foundation Agents in the Second Half: A Survey](https://arxiv.org/pdf/2602.06052v3)** - Surveys foundation agent memory organized by substrate (internal/external), cognitive mechanism (episodic, semantic, working, procedural), and subject (agent- vs user-centric). | arXiv | | **[The AI Hippocampus: How Far are We From Human Memory?](https://arxiv.org/pdf/2601.09113v1)** - Surveys memory in LLMs and multimodal LLMs across implicit, explicit, and agentic paradigms, covering cross-modal integration and challenges like capacity, alignment, and factual consistency. | arXiv | | **[AtomMem: Learnable Dynamic Agentic Memory with Atomic Memory Operation](https://arxiv.org/pdf/2601.08323v2)** - Decomposes memory management into atomic CRUD operations and learns an autonomous policy via SFT + RL to study whether learnable memory outperforms static-workflow methods on long-context tasks. | arXiv | | **[OpenDecoder: Open LLM Decoding to Incorporate Document Quality in RAG](https://arxiv.org/pdf/2601.09028v2)** - Feeds explicit document quality signals (relevance score, ranking, QPP) into RAG generation to study whether exposing retrieval metadata makes the model more robust to noisy context. | arXiv | | **[Reliable Graph-RAG for Codebases: AST-Derived Graphs vs LLM-Extracted Knowledge Graphs](https://arxiv.org/pdf/2601.08773v1)** - Benchmarks vector-only, LLM-extracted KG, and AST-derived graph pipelines for code RAG, comparing correctness and indexing cost across deterministic and LLM-based graph construction. | arXiv | | **[To Retrieve or To Think? An Agentic Approach for Context Evolution](https://arxiv.org/pdf/2601.08747v2)** - Proposes an agentic RAG framework that dynamically decides whether to retrieve new evidence or reason over existing context at each step, aiming to eliminate redundant retrieval. | arXiv | | **[Parallel Context-of-Experts Decoding for Retrieval Augmented Generation](https://arxiv.org/pdf/2601.08670v1)** - Proposes a training-free RAG decoding method that treats retrieved documents as isolated "experts" and aggregates their logits via retrieval-aware contrastive decoding to recover cross-document reasoning. | arXiv | | **[SwiftMem: Fast Agentic Memory via Query-aware Indexing](https://arxiv.org/pdf/2601.08160v1)** - Proposes a query-aware agentic memory system that achieves sub-linear retrieval through temporal and semantic DAG-Tag indexing with an embedding-tag co-consolidation mechanism for memory fragmentation. | arXiv | | **[Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory](https://arxiv.org/pdf/2601.07470v1)** - Proposes treating memory abstraction as a learnable cognitive skill, training a memory copilot via DPO to determine how memories should be structured, abstracted, and reused across tasks. | arXiv | | **[Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents](https://arxiv.org/pdf/2601.07468v1)** - Introduces a temporal semantic memory framework that organizes memories by actual occurrence time rather than dialogue time and consolidates temporally continuous information into durative memory. | arXiv | | **[Active Context Compression: Autonomous Memory Management in LLM Agents](https://arxiv.org/pdf/2601.07190v1)** - Proposes an agent-centric architecture inspired by Physarum polycephalum where the agent autonomously decides when to consolidate learnings and prune raw interaction history to manage context growth. | arXiv | | **[Relink: Constructing Query-Driven Evidence Graph On-the-Fly for GraphRAG](https://arxiv.org/pdf/2601.07192v1)** - Proposes a reason-and-construct paradigm for GraphRAG that dynamically builds query-specific evidence graphs by instantiating facts from a latent relation pool and discarding distractor facts. | arXiv | | **[Seeing through the Conflict: Transparent Knowledge Conflict Handling in RAG](https://arxiv.org/pdf/2601.06842v1)** - Introduces a plug-and-play RAG framework that disentangles semantic match from factual consistency and estimates self-answerability to make the conflict-resolution decision process observable and controllable. | arXiv | | **[CIRAG: Construction-Integration Retrieval and Adaptive Generation for Multi-hop Question Answering](https://arxiv.org/pdf/2601.06799v1)** - Proposes a construction-integration approach for multi-hop RAG that preserves multiple evidence chains via iterative triple construction and adaptively expands context granularity from triples to full passages. | arXiv | | **[Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning](https://arxiv.org/pdf/2601.06282v1)** - Proposes a working memory framework that constructs structured episodic narratives from conversational fragments, consolidates memories with momentum, and semanticizes peripheral facts into semantic memory during offline time. | arXiv | | **[L-RAG: Balancing Context and Retrieval with Entropy-Based Lazy Loading](https://arxiv.org/pdf/2601.06551v1)** - Proposes an adaptive RAG framework that uses entropy-based gating to bypass vector database retrieval when model uncertainty is low, triggering expensive chunk retrieval only when genuine uncertainty is detected. | arXiv | | **[PRISMA: Reinforcement Learning Guided Two-Stage Policy Optimization in Multi-Agent Architecture for Open-Domain Multi-Hop QA](https://arxiv.org/pdf/2601.05465v1)** - Proposes a decoupled multi-agent RAG framework for multi-hop QA with a Plan-Retrieve-Inspect-Solve-Memoize architecture and two-stage GRPO optimization to address retrieval collapse over large corpora. | arXiv | | **[Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction](https://arxiv.org/pdf/2601.05107v1)** - Proposes a framework for user-controllable memory reliance in long-term agent interactions, modeling memory dependence as an explicit and steerable dimension. | arXiv | | **[Beyond Static Summarization: Proactive Memory Extraction for LLM Agents](https://arxiv.org/pdf/2601.04463v1)** - Proposes proactive memory extraction using self-questioning feedback loops instead of one-off static summarization to recover missing information and correct errors iteratively. | arXiv | | **[Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents](https://arxiv.org/pdf/2601.03785v2)** - Proposes a hierarchical memory architecture with a Topic Loom that groups consecutive same-topic dialogue turns into coherent memory boxes and links them via long-range event-timeline traces. | arXiv | | **[MAGMA: A Multi-Graph based Agentic Memory Architecture](https://arxiv.org/pdf/2601.03236v1)** - Proposes a multi-graph agentic memory architecture that represents memories across orthogonal semantic, temporal, causal, and entity graphs with policy-guided traversal for retrieval. | arXiv | | **[HiMeS: Hippocampus-inspired Memory System for Personalized AI Assistants](https://arxiv.org/pdf/2601.06152v1)** - Proposes a hippocampus-inspired memory architecture for AI assistants that fuses RL-trained short-term memory extraction with partitioned long-term memory for personalization. | arXiv | | **[SimpleMem: Efficient Lifelong Memory for LLM Agents](https://arxiv.org/pdf/2601.02553v3)** - Proposes a three-stage memory framework based on semantic lossless compression with structured compression, online semantic synthesis, and intent-aware retrieval planning. | arXiv |

Eval & Observability (80)
| Paper | arXiv ID | |---|:---:| | **[ClawBench: Evaluating Browser Agents on Live Production Websites with Submission-Interception](https://arxiv.org/pdf/2604.08523v1)** - Benchmarks browser agents on 153 everyday tasks across 144 live production sites, with a Chrome-extension plus CDP layer that blocks only the final write request so agents can run end-to-end on real sites without real-world side effects. Evaluates 7 frontier models; top passes 33.3% of tasks. | arXiv | | **[From Features to Actions: Explainability in Traditional and Agentic AI Systems](https://arxiv.org/pdf/2602.06841v1)** - Compares attribution-based explanations with trace-based diagnostics across static and agentic settings to study how explainability methods translate to multi-step agent trajectories. | arXiv | | **[Agentic Uncertainty Reveals Agentic Overconfidence](https://arxiv.org/pdf/2602.06948v1)** - Investigates whether agents can accurately predict their own success rates in agentic tasks. | arXiv | | **[AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents](https://arxiv.org/pdf/2602.06855v1)** - Introduces 20 research tasks from real ML papers covering idea generation, experiments, and refinement for benchmarking science agents. | arXiv | | **[JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Tasks](https://arxiv.org/pdf/2602.06486v1)** - Proposes evaluating agent outputs by decomposing responses into individual claims and checking each against expert knowledge. | arXiv | | **[Completing Missing Annotation: Multi-Agent Debate for Accurate Relevant Assessment](https://arxiv.org/pdf/2602.06526v1)** - Explores using multi-agent debate to fill missing labels in information retrieval benchmarks. | arXiv | | **[TrajAD: Trajectory Anomaly Detection for Trustworthy LLM Agents](https://arxiv.org/pdf/2602.06443v1)** - Proposes a specialized verifier that detects and locates errors in agent execution trajectories at runtime to enable precise rollback-and-retry. | arXiv | | **[Emulating Aggregate Human Choice Behavior and Biases with GPT Conversational Agents](https://arxiv.org/pdf/2602.05597v1)** - Examines whether GPT-4/5 agents can reproduce aggregate human cognitive biases in interactive decision-making scenarios. | arXiv | | **[Capture the Flags: Family-Based Evaluation of Agentic LLMs](https://arxiv.org/pdf/2602.05523v1)** - Proposes generating families of equivalent CTF challenges through code transformations to test whether agents truly understand exploits or just memorize patterns. | arXiv | | **[PieArena: Frontier Language Agents Achieve MBA-Level Negotiation](https://arxiv.org/pdf/2602.05302v1)** - Introduces a negotiation benchmark where frontier LLM agents are evaluated against MBA students to reveal cross-model differences in deception, accuracy, and trustworthiness. | arXiv | | **[ES-MemEval: Benchmarking Conversational Agents on Personalized Long-Term Emotional Support](https://arxiv.org/pdf/2602.01885v1)** - Benchmarks how well conversational agents retain and use personal information over long emotional support conversations. | arXiv | | **[HumanStudy-Bench: Towards AI Agent Design for Participant Simulation](https://arxiv.org/pdf/2602.00685v1)** - Introduces a benchmark that replays published human-subject experiments with LLM agents to test how well they simulate real participants. | arXiv | | **[Benchmarking Agents in Insurance Underwriting Environments](https://arxiv.org/pdf/2602.00456v1)** - Proposes an expert-designed multi-turn insurance underwriting benchmark to evaluate agent performance under real-world enterprise conditions with noisy tools and proprietary knowledge. | arXiv | | **[TriCEGAR: A Trace-Driven Abstraction Mechanism for Agentic AI](https://arxiv.org/pdf/2601.22997v1)** - Proposes automated state abstraction from agent execution traces using predicate trees and counterexample refinement for probabilistic runtime verification of agent behavior. | arXiv | | **[Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering](https://arxiv.org/pdf/2601.22952v1)** - Compares three LLM agent frameworks (Aider, OpenHands, SWE-agent) on vulnerability false positive filtering to study how agent design and backbone model affect triage performance. | arXiv | | **[Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study](https://arxiv.org/pdf/2602.00164v1)** - Analyzes 8,106 fix-related pull requests from five AI coding agents to catalog the reasons agent-generated contributions are closed without merging. | arXiv | | **[JAF: Judge Agent Forest](https://arxiv.org/pdf/2601.22269v1)** - Proposes a judge agent framework that evaluates query-response pairs jointly across a cohort rather than in isolation, using in-context neighborhoods for cross-instance pattern detection. | arXiv | | **[Stalled, Biased, and Confused: Uncovering Reasoning Failures in LLMs for Cloud-Based Root Cause Analysis](https://arxiv.org/pdf/2601.22208v1)** - Evaluates LLM reasoning under ReAct and Plan-and-Execute agentic workflows across 48,000 simulated failure scenarios, producing a taxonomy of 16 common reasoning failures. | arXiv | | **[CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty](https://arxiv.org/pdf/2601.22027v1)** - Introduces a benchmark for evaluating LLM agent consistency, uncertainty handling, and capability awareness in multi-turn tool-using scenarios with incomplete or ambiguous user requests. | arXiv | | **[More Code, Less Reuse: Investigating Code Quality and Reviewer Sentiment towards AI-generated Pull Requests](https://arxiv.org/pdf/2601.21276v1)** - Examines code quality, maintainability, and reviewer sentiment toward AI-agent-generated pull requests compared to human-authored contributions. | arXiv | | **[The Quiet Contributions: Insights into AI-Generated Silent Pull Requests](https://arxiv.org/pdf/2601.21102v1)** - Analyzes silent (no-comment) AI-generated pull requests to examine their impact on code complexity, quality issues, and security vulnerabilities. | arXiv | | **[Agent Benchmarks Fail Public Sector Requirements](https://arxiv.org/pdf/2601.20617v1)** - Analyzes over 1,300 agent benchmarks against public-sector requirements including process-based evaluation, realism, and domain-specific metrics. | arXiv | | **[Interpreting Emergent Extreme Events in Multi-Agent Systems](https://arxiv.org/pdf/2601.20538v1)** - Applies Shapley values to attribute emergent extreme events in LLM multi-agent systems to specific agent actions across time, agent, and behavior dimensions. | arXiv | | **[Who Writes the Docs in SE 3.0? Agent vs. Human Documentation Pull Requests](https://arxiv.org/pdf/2601.20171v1)** - Analyzes AI agent contributions to documentation pull requests and examines how human developers review and intervene in agent-authored documentation changes. | arXiv | | **[Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agents](https://arxiv.org/pdf/2601.20106v1)** - Examines how core and peripheral developers differ in their use, review, modification, and verification of coding-agent-generated pull requests. | arXiv | | **[DevOps-Gym: Benchmarking AI Agents in Software DevOps Cycle](https://arxiv.org/pdf/2601.20882v1)** - Introduces an end-to-end benchmark with 700+ real-world tasks across build, monitoring, issue resolving, and test generation for evaluating AI agents in full software DevOps workflows. | arXiv | | **[Toward Architecture-Aware Evaluation Metrics for LLM Agents](https://arxiv.org/pdf/2601.19583v1)** - Proposes an architecture-informed evaluation approach that links agent components like planners, memory, and tool routers to observable behaviors and diagnostic metrics. | arXiv | | **[Balancing Sustainability And Performance: The Role Of Small-Scale LLMs In Agentic AI Systems](https://arxiv.org/pdf/2601.19311v2)** - Investigates whether smaller-scale language models can reduce energy consumption in multi-agent agentic AI systems without compromising task quality. | arXiv | | **[Understanding Dominant Themes in Reviewing Agentic AI-authored Code](https://arxiv.org/pdf/2601.19287v1)** - Analyzes 19,450 inline review comments on agent-authored pull requests and derives a taxonomy of 12 review themes to understand how reviewers respond to AI-generated code. | arXiv | | **[Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests](https://arxiv.org/pdf/2601.18749v1)** - Analyzes 40,214 developer and agentic pull requests to compare merge outcomes and identify how submitter attributes and review features differ between human and AI agent contributions. | arXiv | | **[Automated Structural Testing of LLM-Based Agents: Methods, Framework, and Case Studies](https://arxiv.org/pdf/2601.18827v1)** - Presents structural testing methods for LLM-based agents using OpenTelemetry traces, mocking for reproducible behavior, and automated assertions for component-level verification. | arXiv | | **[When AI Agents Touch CI/CD Configurations: Frequency and Success](https://arxiv.org/pdf/2601.17413v1)** - Analyzes how five AI coding agents interact with CI/CD configurations across 8,031 pull requests, examining modification frequency, merge rates, and build success. | arXiv | | **[Fingerprinting AI Coding Agents on GitHub](https://arxiv.org/pdf/2601.17406v1)** - Identifies behavioral signatures of five AI coding agents from 33,580 pull requests using commit, PR structure, and code features for agent attribution. | arXiv | | **[Interpreting Agentic Systems: Beyond Model Explanations to System-Level Accountability](https://arxiv.org/pdf/2601.17168v1)** - Assesses existing interpretability methods for agentic systems and identifies gaps in explaining temporal dynamics, compounding decisions, and context-dependent behaviors. | arXiv | | **[AI builds, We Analyze: An Empirical Study of AI-Generated Build Code Quality](https://arxiv.org/pdf/2601.16839v1)** - Investigates maintainability and security-related build code smells in AI-agent-generated pull requests across 364 identified quality issues. | arXiv | | **[Will It Survive? Deciphering the Fate of AI-Generated Code in Open Source](https://arxiv.org/pdf/2601.16809v1)** - Examines long-term survival of AI-agent-generated code through survival analysis of 200,000+ code units across 201 open-source projects. | arXiv | | **[LUMINA: Long-horizon Understanding for Multi-turn Interactive Agents](https://arxiv.org/pdf/2601.16649v1)** - Develops an oracle counterfactual framework for multi-turn agentic tasks that measures the criticality of individual capabilities like planning and state tracking. | arXiv | | **[When Agents Fail to Act: A Diagnostic Framework for Tool Invocation Reliability in Multi-Agent LLM Systems](https://arxiv.org/pdf/2601.16280v1)** - Presents a 12-category error taxonomy and diagnostic framework for evaluating tool-use reliability across open-weight and proprietary LLMs in multi-agent systems on edge hardware. | arXiv | | **[Agentic Confidence Calibration](https://arxiv.org/pdf/2601.15778v1)** - Introduces the problem of agentic confidence calibration and proposes Holistic Trajectory Calibration, extracting process-level features across an agent's entire trajectory to diagnose failures. | arXiv | | **[Improving Methodologies for Agentic Evaluations Across Domains: Leakage of Sensitive Information, Fraud and Cybersecurity Threats](https://arxiv.org/pdf/2601.15679v1)** - Examines methodological challenges in evaluating AI agents across sensitive information leakage, fraud, and cybersecurity threats through a multi-national collaborative benchmarking exercise. | arXiv | | **[MiRAGE: A Multiagent Framework for Generating Multimodal Multihop Question-Answer Dataset for RAG Evaluation](https://arxiv.org/pdf/2601.15487v1)** - Introduces a multi-agent framework that generates verified, domain-specific, multimodal, multi-hop question-answer datasets for benchmarking retrieval-augmented generation systems. | arXiv | | **[When Agents Fail: A Comprehensive Study of Bugs in LLM Agents with Automated Labeling](https://arxiv.org/pdf/2601.15232v1)** - Analyzes 1,187 bug reports from LLM agent software across seven frameworks to categorize bug types, root causes, effects, and tests automated bug labeling with a ReAct agent. | arXiv | | **[The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution](https://arxiv.org/pdf/2601.15075v2)** - Proposes a hierarchical framework for general agentic attribution that identifies internal factors driving agent actions through temporal likelihood dynamics and perturbation-based analysis. | arXiv | | **[Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering](https://arxiv.org/pdf/2601.14470v1)** - Analyzes token consumption patterns across software development lifecycle stages in a multi-agent system to identify where tokens are consumed and which stages drive cost. | arXiv | | **[APEX-Agents](https://arxiv.org/pdf/2601.14242v2)** - Introduces a benchmark of 480 long-horizon, cross-application productivity tasks created by investment banking analysts, consultants, and lawyers for evaluating AI agent capabilities in realistic work environments. | arXiv | | **[CooperBench: Why Coding Agents Cannot be Your Teammates Yet](https://arxiv.org/pdf/2601.13295v2)** - Introduces a benchmark of 600+ collaborative coding tasks to evaluate whether coding agents can coordinate as effective teammates under various coordination structures. | arXiv | | **[Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?](https://arxiv.org/pdf/2601.13227v1)** - Investigates how RAG systems can game nugget-based LLM judge evaluations through metric overfitting, demonstrating near-perfect scores when evaluation elements are leaked or predictable. | arXiv | | **[Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents](https://arxiv.org/pdf/2601.15322v1)** - Introduces the Determinism-Faithfulness Assurance Harness for measuring trajectory determinism and evidence-conditioned faithfulness in tool-using LLM agents across 74 configurations and 12 models. | arXiv | | **[AEMA: Verifiable Evaluation Framework for Trustworthy and Controlled Agentic LLM Systems](https://arxiv.org/pdf/2601.11903v1)** - Presents a process-aware and auditable multi-agent evaluation framework that plans, executes, and aggregates multi-step evaluations across heterogeneous agentic workflows under human oversight. | arXiv | | **[Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces](https://arxiv.org/pdf/2601.11868v1)** - Introduces a curated benchmark of 89 hard tasks in computer terminal environments with unique environments, human-written solutions, and comprehensive tests for evaluating frontier agent capabilities. | arXiv | | **[ATOD: An Evaluation Framework and Benchmark for Agentic Task-Oriented Dialogue Systems](https://arxiv.org/pdf/2601.11854v2)** - Introduces a benchmark and evaluation framework for agentic task-oriented dialogue systems covering multi-goal coordination, dependency management, memory, adaptability, and proactivity. | arXiv | | **[What Do LLM Agents Know About Their World? Task2Quiz](https://arxiv.org/pdf/2601.09503v1)** - Decouples task execution from environment understanding with a deterministic QA paradigm to study whether task success is actually a good proxy for how well agents understand their environment. | arXiv | | **[The Hierarchy of Agentic Capabilities: Evaluating Frontier Models on Realistic RL Environments](https://arxiv.org/pdf/2601.09032v1)** - Evaluates frontier models on 150 workplace tasks to identify an empirical hierarchy of agentic capabilities spanning tool use, planning, adaptability, groundedness, and common-sense reasoning. | arXiv | | **[ViDoRe V3: A Comprehensive Evaluation of RAG in Complex Real-World Scenarios](https://arxiv.org/pdf/2601.08620v1)** - Introduces a multimodal RAG benchmark with 26K pages and 3,099 queries in 6 languages to evaluate retrieval across non-textual elements and open-ended queries. | arXiv | | **[M3-BENCH: Process-Aware Evaluation of LLM Agents Social Behaviors in Mixed-Motive Games](https://arxiv.org/pdf/2601.08462v1)** - Evaluates LLM agent social behaviors in mixed-motive games using process-aware analysis of both reasoning and communication rather than outcome-only metrics. | arXiv | | **[Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents](https://arxiv.org/pdf/2601.19935v1)** - Benchmarks whether agents can proactively use long-term memory to execute tool-based actions, rather than just passively retrieving facts on demand. | arXiv | | **[Active Evaluation of General Agents: Problem Definition and Comparison of Baseline Algorithms](https://arxiv.org/pdf/2601.07651v2)** - Proposes a formal framework for actively evaluating general-purpose agents across multiple tasks, selecting which tasks and agents to sample next to minimize ranking error over time. | arXiv | | **[VirtualEnv: A Platform for Embodied AI Research](https://arxiv.org/pdf/2601.07553v2)** - Introduces an Unreal Engine 5 simulation platform for benchmarking LLM-driven agents on embodied tasks including navigation, object manipulation, and multi-agent coordination in procedurally generated environments. | arXiv | | **[FROAV: A Framework for RAG Observation and Agent Verification](https://arxiv.org/pdf/2601.07504v1)** - Presents an open-source platform combining visual workflow orchestration with LLM-as-a-Judge evaluation for prototyping and validating RAG-based agent pipelines without infrastructure coding. | arXiv | | **[Lost in the Noise: How Reasoning Models Fail with Contextual Distractors](https://arxiv.org/pdf/2601.07226v1)** - Benchmarks model robustness across 11 RAG, reasoning, alignment, and tool-use tasks against diverse contextual noise types including random documents, irrelevant histories, and hard negative distractors. | arXiv | | **[RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction](https://arxiv.org/pdf/2601.06966v1)** - Introduces a project-oriented memory benchmark with 2,000+ cross-session dialogues across eleven scenarios to evaluate how well agents track evolving goals and dynamic context dependencies. | arXiv | | **[IDRBench: Interactive Deep Research Benchmark](https://arxiv.org/pdf/2601.06676v1)** - Introduces the first benchmark for interactive deep research combining a modular multi-agent framework with on-demand user interaction, a scalable user simulator, and interaction-aware metrics measuring quality, alignment, and cost. | arXiv | | **[ToolGym: an Open-world Tool-using Environment for Scalable Agent Testing and Data Curation](https://arxiv.org/pdf/2601.06328v1)** - Introduces an open-world tool-using environment with 5,571 tools across 204 apps, a task engine for multi-tool workflows with wild constraints, and a state controller that injects failures to stress-test robustness. | arXiv | | **[TowerMind: A Tower Defence Game Learning Environment and Benchmark for LLM as Agents](https://arxiv.org/pdf/2601.05899v1)** - Introduces a tower defense environment for evaluating LLM agent planning and decision-making with low computational demands, multimodal observation, and hallucination assessment support. | arXiv | | **[MineNPC-Task: Task Suite for Memory-Aware Minecraft Agents](https://arxiv.org/pdf/2601.05215v2)** - Introduces a user-authored benchmark for memory-aware LLM agents in Minecraft with parametric task templates, machine-checkable validators, and bounded-knowledge evaluation under a no-shortcut policy. | arXiv | | **[Internal Representations as Indicators of Hallucinations in Agent Tool Selection](https://arxiv.org/pdf/2601.05214v1)** - Proposes a framework for detecting tool-calling hallucinations in LLM agents by analyzing internal representations during a single forward pass, targeting incorrect tool selection, parameter errors, and tool bypass. | arXiv | | **[Agent-as-a-Judge](https://arxiv.org/pdf/2601.05111v1)** - Surveys the evolution from LLM-as-a-Judge to Agent-as-a-Judge, where agentic judges employ planning, tool-augmented verification, multi-agent collaboration, and persistent memory for evaluation. | arXiv | | **[Arabic Prompts with English Tools: A Benchmark](https://arxiv.org/pdf/2601.05101v1)** - Introduces the first benchmark for evaluating tool-calling and agentic capabilities of LLMs in Arabic, measuring functional accuracy and robustness in Arabic agentic workflows. | arXiv | | **[Effects of Personality Steering on Cooperative Behavior in LLM Agents](https://arxiv.org/pdf/2601.05302v2)** - Examines how Big Five personality steering affects cooperative behavior in LLM agents using repeated Prisoner's Dilemma games across multiple model generations. | arXiv | | **[Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests](https://arxiv.org/pdf/2601.04886v2)** - Analyzes message-code inconsistency in pull requests authored by AI coding agents across five agent systems to study trustworthiness of agent-generated PR descriptions. | arXiv | | **[GUITester: Enabling GUI Agents for Exploratory Defect Discovery](https://arxiv.org/pdf/2601.04500v1)** - Proposes a multi-agent framework for autonomous exploratory GUI testing that decouples navigation from verification via planning-execution and hierarchical reflection modules. | arXiv | | **[Agent Drift: Quantifying Behavioral Degradation in Multi-Agent LLM Systems](https://arxiv.org/pdf/2601.04170v1)** - Introduces the concept of agent drift and a composite metric framework for quantifying semantic, coordination, and behavioral degradation in multi-agent LLM systems over extended interactions. | arXiv | | **[M3MAD-Bench: Are Multi-Agent Debates Really Effective Across Domains and Modalities?](https://arxiv.org/pdf/2601.02854v1)** - Introduces a unified benchmark for evaluating Multi-Agent Debate methods across multiple domains, modalities, and efficiency metrics including token consumption and inference time. | arXiv | | **[Why LLMs Aren't Scientists Yet: Lessons from Four Autonomous Research Attempts](https://arxiv.org/pdf/2601.03315v1)** - Documents six recurring failure modes across four end-to-end attempts at autonomous ML research using a pipeline of LLM agents mapped to stages of the scientific workflow. | arXiv | | **[LongDA: Benchmarking LLM Agents for Long-Document Data Analysis](https://arxiv.org/pdf/2601.02598v2)** - Introduces a data analysis benchmark for evaluating LLM agents under documentation-intensive analytical workflows requiring long document navigation and multi-step computation. | arXiv | | **[The Rise of Agentic Testing: Multi-Agent Systems for Robust Software Quality Assurance](https://arxiv.org/pdf/2601.02454v1)** - Proposes a closed-loop multi-agent testing framework with generation, execution analysis, and review optimization agents for autonomous software test refinement. | arXiv | | **[Project Ariadne: A Structural Causal Framework for Auditing Faithfulness in LLM Agents](https://arxiv.org/pdf/2601.02314v1)** - Proposes a causal framework using structural causal models and counterfactual interventions to audit whether reasoning traces in LLM agents are faithful generative drivers or post-hoc rationalizations. | arXiv | | **[ReliabilityBench: Evaluating LLM Agent Reliability Under Production-Like Stress Conditions](https://arxiv.org/pdf/2601.06112v1)** - Introduces a benchmark for evaluating agent reliability across consistency, robustness to perturbations, and fault tolerance under chaos-engineering-style tool failure injection. | arXiv | | **[MAESTRO: Multi-Agent Evaluation Suite for Testing, Reliability, and Observability](https://arxiv.org/pdf/2601.00481v1)** - Introduces an evaluation suite that standardizes MAS configuration and execution, exports framework-agnostic execution traces, and enables systematic reliability assessment across agent architectures. | arXiv | | **[Beyond Perfect APIs: WildAGTEval](https://arxiv.org/pdf/2601.00268v1)** - Introduces a benchmark for evaluating LLM agent function-calling under realistic API complexity including noisy outputs, detailed specifications, and runtime challenges. | arXiv |

Agent Tooling (95)
| Paper | arXiv ID | |---|:---:| | **[TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging](https://arxiv.org/pdf/2602.06875v1)** - Proposes a multi-agent observe-analyze-repair loop that uses runtime traces to find and fix bugs in LLM-generated code. | arXiv | | **[Generative Ontology: When Structured Knowledge Learns to Create](https://arxiv.org/pdf/2602.05636v1)** - Explores constraining LLM generation with executable schemas and multi-agent roles to produce structurally valid yet creative outputs. | arXiv | | **[Structured Context Engineering for File-Native Agentic Systems](https://arxiv.org/pdf/2602.05447v1)** - Tests how context format (YAML, JSON, Markdown) affects agent accuracy across 9,649 experiments in file-native agentic systems. | arXiv | | **[ProAct: Agentic Lookahead in Interactive Environments](https://arxiv.org/pdf/2602.05327v1)** - Explores training agents to think ahead by distilling environment search into causal reasoning chains in interactive environments. | arXiv | | **[Autonomous Question Formation for Large Language Model-Driven AI Systems](https://arxiv.org/pdf/2602.01556v1)** - Investigates teaching agents to ask themselves the right questions before acting to adapt to new situations autonomously. | arXiv | | **[From Perception to Action: Spatial AI Agents and World Models](https://arxiv.org/pdf/2602.01644v1)** - Surveys the connection between agentic architectures and spatial tasks like robotics and navigation, covering memory, planning, and world models in embodied agents. | arXiv | | **[World Models as an Intermediary between Agents and the Real World](https://arxiv.org/pdf/2602.00785v1)** - Argues for using world models as a bridge between agents and high-cost real-world environments to provide richer learning signals across domains like robotics and ML engineering. | arXiv | | **[Engineering AI Agents for Clinical Workflows: A Case Study in Architecture, MLOps, and Governance](https://arxiv.org/pdf/2602.00751v1)** - Presents a reference architecture for production AI agents integrating Clean Architecture, event-driven design, per-agent MLOps lifecycles, and human-in-the-loop governance. | arXiv | | **[Autonomous Data Processing using Meta-Agents](https://arxiv.org/pdf/2602.00307v1)** - Proposes a meta-agent framework that builds, runs, and keeps refining data processing pipelines through hierarchical agent orchestration. | arXiv | | **[MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering](https://arxiv.org/pdf/2601.22859v2)** - Proposes a multi-agent framework for automatically building executable test environments across ten programming languages using planning-execution-verification with environment reuse. | arXiv | | **[Learning with Challenges: Adaptive Difficulty-Aware Data Generation for Mobile GUI Agent Training](https://arxiv.org/pdf/2601.22781v1)** - Proposes an adaptive data generation framework for training mobile GUI agents that matches task difficulty to the agent's current capability level. | arXiv | | **[AutoRefine: From Trajectories to Reusable Expertise for Continual LLM Agent Refinement](https://arxiv.org/pdf/2601.22758v1)** - Proposes extracting dual-form reusable expertise from agent execution histories — specialized subagents for procedural tasks and skill patterns for static knowledge — with continuous pruning and merging. | arXiv | | **[ToolTok: Tool Tokenization for Efficient and Generalizable GUI Agents](https://arxiv.org/pdf/2602.02548v1)** - Proposes modeling GUI agent operations as sequences of learnable tool tokens with semantic anchoring and curriculum-based training instead of coordinate-based visual grounding. | arXiv | | **[From Self-Evolving Synthetic Data to Verifiable-Reward RL: Post-Training Multi-turn Interactive Tool-Using Agents](https://arxiv.org/pdf/2601.22607v2)** - Proposes a framework combining a self-evolving multi-agent data engine with verifier-based reinforcement learning to train multi-turn interactive tool-using agents. | arXiv | | **[Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents](https://arxiv.org/pdf/2601.22311v1)** - Investigates why step-wise reasoning struggles with long-horizon planning in LLM agents and proposes future-aware lookahead with reward estimation to let early actions account for delayed outcomes. | arXiv | | **[SWE-Replay: Efficient Test-Time Scaling for Software Engineering Agents](https://arxiv.org/pdf/2601.22129v2)** - Proposes a test-time scaling method for software engineering agents that recycles prior trajectories and branches at critical intermediate steps instead of resampling from scratch. | arXiv | | **[Optimizing Agentic Workflows using Meta-tools](https://arxiv.org/pdf/2601.22037v2)** - Proposes bundling recurring sequences of agent tool calls into deterministic meta-tools to skip unnecessary intermediate LLM reasoning steps and cut failures. | arXiv | | **[astra-langchain4j: Experiences Combining LLMs and Agent Programming](https://arxiv.org/pdf/2601.21879v1)** - Explores integrating LLM capabilities into the ASTRA agent programming language to study how traditional agent toolkits and modern LLM-based agentic platforms can inform each other. | arXiv | | **[Meta Context Engineering via Agentic Skill Evolution](https://arxiv.org/pdf/2601.21557v2)** - Introduces a bi-level framework where a meta-agent evolves context engineering skills via agentic crossover while a base agent executes them to optimize context as files and code. | arXiv | | **[DataCross: A Unified Benchmark and Agent Framework for Cross-Modal Heterogeneous Data Analysis](https://arxiv.org/pdf/2601.21403v1)** - Proposes a multi-agent framework and benchmark for cross-modal data analysis that coordinates specialized sub-agents via a divide-and-conquer workflow across structured and unstructured data sources. | arXiv | | **[CovAgent: Overcoming the 30% Curse of Mobile Application Coverage with Agentic AI and Dynamic Instrumentation](https://arxiv.org/pdf/2601.21253v1)** - Explores agentic AI for Android app testing that uses code inspection and dynamic instrumentation to reach activities that standard GUI fuzzers cannot access. | arXiv | | **[CUA-Skill: Develop Skills for Computer Using Agent](https://arxiv.org/pdf/2601.21123v2)** - Introduces a large-scale computer-using agent skill library with parameterized execution, composition graphs, dynamic retrieval, and memory-aware failure recovery for desktop applications. | arXiv | | **[Textual Equilibrium Propagation for Deep Compound AI Systems](https://arxiv.org/pdf/2601.21064v2)** - Explores local equilibrium propagation for optimizing deep compound AI systems that avoids signal degradation in long-horizon agentic workflows by replacing global textual backpropagation. | arXiv | | **[Should I Have Expressed a Different Intent? Counterfactual Generation for LLM-Based Autonomous Control](https://arxiv.org/pdf/2601.20090v2)** - Investigates counterfactual reasoning in agentic LLM control scenarios using structural causal models and conformal prediction for formal reliability guarantees. | arXiv | | **[Insight Agents: An LLM-Based Multi-Agent System for Data Insights](https://arxiv.org/pdf/2601.20048v2)** - Introduces a hierarchical multi-agent system with out-of-domain detection and BERT-based agent routing for delivering personalized data insights at production scale. | arXiv | | **[Agentic Design Patterns: A System-Theoretic Framework](https://arxiv.org/pdf/2601.19752v1)** - Introduces a system-theoretic framework that decomposes agentic AI into five functional subsystems and derives 12 reusable design patterns for building robust agent architectures. | arXiv | | **[A Practical Guide to Agentic AI Transition in Organizations](https://arxiv.org/pdf/2602.10122v1)** - Explores a pragmatic framework for transitioning organizational processes to agentic AI, covering domain-driven use case identification, task delegation, and human-in-the-loop operating models. | arXiv | | **[JitRL: Just-In-Time Reinforcement Learning for Continual Learning in LLM Agents Without Gradient Updates](https://arxiv.org/pdf/2601.18510v1)** - Proposes a training-free continual learning framework for LLM agents that retrieves relevant past experiences and modulates output logits at test time without gradient updates. | arXiv | | **[Think-Augmented Function Calling: Improving LLM Parameter Accuracy Through Embedded Reasoning](https://arxiv.org/pdf/2601.18282v2)** - Proposes embedding explicit reasoning at both function and parameter levels during agent tool calls, with dynamic complexity scoring to trigger granular justification for critical decisions. | arXiv | | **[Paying Less Generalization Tax: A Cross-Domain Generalization Study of RL Training for LLM Agents](https://arxiv.org/pdf/2601.18217v1)** - Investigates which RL training environment properties and modeling choices most influence cross-domain generalization for LLM agents deployed beyond their training domains. | arXiv | | **[Think Locally, Explain Globally: Graph-Guided LLM Investigations via Local Reasoning and Belief Propagation](https://arxiv.org/pdf/2601.17915v2)** - Proposes disaggregating LLM investigation into bounded local evidence mining with deterministic graph traversal and belief propagation for reliable open-ended agent reasoning. | arXiv | | **[AI Agent for Reverse-Engineering Legacy Finite-Difference Code](https://arxiv.org/pdf/2601.18381v1)** - Presents a LangGraph-based AI agent framework combining GraphRAG, multi-stage retrieval, and RL-inspired adaptive feedback for reverse-engineering legacy scientific code. | arXiv | | **[PatchIsland: Orchestration of LLM Agents for Continuous Vulnerability Repair](https://arxiv.org/pdf/2601.17471v1)** - Proposes a continuous vulnerability repair system that orchestrates a diverse LLM agent ensemble with two-phase deduplication for integration with continuous fuzzing pipelines. | arXiv | | **[DALIA: Towards a Declarative Agentic Layer for Intelligent Agents in MCP-Based Server Ecosystems](https://arxiv.org/pdf/2601.17435v1)** - Introduces a declarative architectural layer for agentic workflows with formalized capabilities, declarative discovery protocol, and deterministic task graph construction. | arXiv | | **[SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents](https://arxiv.org/pdf/2601.16746v2)** - Presents a task-aware context pruning framework for coding agents that trains a lightweight neural skimmer to selectively retain relevant code lines based on explicit goals. | arXiv | | **[REprompt: Prompt Generation for Intelligent Software Development Guided by Requirements Engineering](https://arxiv.org/pdf/2601.16507v1)** - Proposes a multi-agent prompt optimization framework guided by requirements engineering principles for system and user prompts in agent-based software development. | arXiv | | **[EvoConfig: Self-Evolving Multi-Agent Systems for Efficient Autonomous Environment Configuration](https://arxiv.org/pdf/2601.16489v1)** - Introduces a self-evolving multi-agent framework for automated environment configuration with expert diagnosis and dynamic error-fixing priority adjustment. | arXiv | | **[SemanticALLI: Caching Reasoning, Not Just Responses, in Agentic Systems](https://arxiv.org/pdf/2601.16286v2)** - Proposes a pipeline-aware caching architecture for agentic systems that elevates structured intermediate reasoning representations to first-class cacheable artifacts to reduce redundant LLM calls. | arXiv | | **[Controlling Long-Horizon Behavior in Language Model Agents with Explicit State Dynamics](https://arxiv.org/pdf/2601.16087v1)** - Investigates imposing explicit dynamical structure on an external affective state to induce temporal coherence and controlled recovery in multi-turn dialogue agents. | arXiv | | **[Agentic Uncertainty Quantification](https://arxiv.org/pdf/2601.15703v1)** - Proposes a Dual-Process framework that transforms verbalized uncertainty into bi-directional control signals for agent memory and reflection to prevent cascading hallucination errors. | arXiv | | **[Agentic AI Governance and Lifecycle Management in Healthcare](https://arxiv.org/pdf/2601.15630v1)** - Presents a Unified Agent Lifecycle Management blueprint with five control-plane layers for governing agent fleets including identity registry, orchestration, and runtime policy enforcement. | arXiv | | **[Autonomous Business System via Neuro-symbolic AI](https://arxiv.org/pdf/2601.15599v1)** - Introduces a neuro-symbolic architecture that integrates LLM agents with predicate-logic programming and knowledge graphs to orchestrate end-to-end business initiatives through task-specific logic programs. | arXiv | | **[How to Build AI Agents by Augmenting LLMs with Codified Human Expert Domain Knowledge? A Software Engineering Framework](https://arxiv.org/pdf/2601.15153v1)** - Proposes a software engineering framework for capturing and embedding codified human domain knowledge into LLM-based agents through request classification, RAG, and expert rule integration. | arXiv | | **[Agent Identity URI Scheme: Topology-Independent Naming and Capability-Based Discovery for Multi-Agent Systems](https://arxiv.org/pdf/2601.14567v1)** - Defines the agent:// URI scheme that decouples agent identity from network location through trust roots, hierarchical capability paths, and cryptographic attestation for multi-agent discovery. | arXiv | | **[Toward Efficient Agents: Memory, Tool learning, and Planning](https://arxiv.org/pdf/2601.14192v1)** - Surveys efficiency in agent systems across memory, tool learning, and planning, comparing approaches under fixed cost budgets and analyzing the Pareto frontier between effectiveness and cost. | arXiv | | **[Toward self-coding information systems](https://arxiv.org/pdf/2601.14132v1)** - Proposes self-coding information systems that use agentic AI to dynamically generate, test, and redeploy their own source code at runtime to reduce feature delivery time. | arXiv | | **[A Lightweight Modular Framework for Constructing Autonomous Agents Driven by Large Language Models: Design, Implementation, and Applications in AgentForge](https://arxiv.org/pdf/2601.13383v1)** - Presents a lightweight open-source Python framework for building LLM-driven agents with composable skill abstractions, a unified LLM backend interface, and declarative YAML-based configuration. | arXiv | | **[MagicGUI-RMS: A Multi-Agent Reward Model System for Self-Evolving GUI Agents via Automated Feedback Reflux](https://arxiv.org/pdf/2601.13060v1)** - Introduces a multi-agent reward model system for GUI agents that combines domain-specific and general-purpose reward models with automated data reflux for self-evolving agent training. | arXiv | | **[Agentic AI Meets Edge Computing in Autonomous UAV Swarms](https://arxiv.org/pdf/2601.14437v1)** - Investigates three deployment architectures for integrating LLM-based agentic AI with edge computing in UAV swarms, covering standalone, edge-enabled, and edge-cloud hybrid configurations. | arXiv | | **[Agentic Artificial Intelligence (AI): Architectures, Taxonomies, and Evaluation of Large Language Model Agents](https://arxiv.org/pdf/2601.12560v1)** - Proposes a unified taxonomy decomposing AI agents into Perception, Brain, Planning, Action, Tool Use, and Collaboration subsystems, covering MCP, native computer use, and evaluation practices. | arXiv | | **[Agentic Reasoning for Large Language Models](https://arxiv.org/pdf/2601.12538v1)** - Surveys agentic reasoning across foundational, self-evolving, and collective multi-agent dimensions, distinguishing in-context reasoning from post-training approaches across planning, tool use, and coordination. | arXiv | | **[POLARIS: Typed Planning and Governed Execution for Agentic AI in Back-Office Automation](https://arxiv.org/pdf/2601.11816v1)** - Introduces a governed orchestration framework that treats agentic automation as typed plan synthesis with DAG-based planning, rubric-guided selection, validator-gated execution, and compiled policy guardrails. | arXiv | | **[From Everything-is-a-File to Files-Are-All-You-Need: How Unix Philosophy Informs the Design of Agentic AI Systems](https://arxiv.org/pdf/2601.11672v1)** - Explores how the Unix 'everything is a file' principle informs agentic AI design through file-like abstractions and code-based specifications for composable, auditable agent interfaces. | arXiv | | **[Towards AGI A Pragmatic Approach Towards Self Evolving Agent](https://arxiv.org/pdf/2601.11658v1)** - Introduces a hierarchical self-evolving multi-agent framework that integrates curriculum learning, reward-based learning, and genetic algorithm evolution for continuous autonomous capability expansion. | arXiv | | **[EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines](https://arxiv.org/pdf/2601.09465v2)** - Proposes a self-evolving agent framework that evolves an explicit finite state machine instead of free-form code rewriting, constraining flow and skill optimization to a structured representation. | arXiv | | **[Investigating Tool-Memory Conflicts in Tool-Augmented LLMs](https://arxiv.org/pdf/2601.09760v1)** - Identifies and studies a conflict type where a tool-augmented LLM's internal knowledge contradicts external tool outputs, evaluating whether existing resolution techniques like prompting and RAG address it. | arXiv | | **[MAXS: Meta-Adaptive Exploration with LLM Agents](https://arxiv.org/pdf/2601.09259v1)** - Uses lookahead planning to estimate the value of tool usage at each step and selects stable, high-value reasoning paths, with a convergence mechanism that halts rollouts once consistency is reached. | arXiv | | **[ToolACE-MCP: Generalizing History-Aware Routing from MCP Tools to the Agent Web](https://arxiv.org/pdf/2601.08276v1)** - Trains history-aware routers for large-scale MCP tool ecosystems using dependency graphs and multi-turn trajectory synthesis to generalize across multi-agent collaboration and massive tool catalogs. | arXiv | | **[Beyond Single-Shot: Multi-step Tool Retrieval via Query Planning](https://arxiv.org/pdf/2601.07782v1)** - Proposes iterative query planning for tool retrieval that decomposes instructions into sub-tasks and dynamically generates queries, trained via synthetic trajectories and reinforcement learning with verifiable rewards. | arXiv | | **[OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent](https://arxiv.org/pdf/2601.07779v1)** - Introduces a Computer-Using Agent framework with milestone-driven long-term memory for trajectory-level self-correction and a multimodal searcher that synthesizes live, visually aligned tutorials for unseen scenarios. | arXiv | | **[SAGE: Tool-Augmented LLM Task Solving Strategies in Scalable Multi-Agent Environments](https://arxiv.org/pdf/2601.09750v1)** - Presents a conversational AI interface for dynamic tool discovery and execution via the OPACA framework, comparing multiple task-solving strategies across different agent setups and prompting methods. | arXiv | | **[Beyond Static Tools: Test-Time Tool Evolution for Scientific Reasoning](https://arxiv.org/pdf/2601.07641v1)** - Proposes test-time tool evolution where agents synthesize, verify, and evolve executable tools during inference instead of relying on static pre-defined tool libraries. | arXiv | | **[MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era](https://arxiv.org/pdf/2601.07526v2)** - Introduces a large-scale distributed orchestration system that decouples agent training into independent Model, Agent, and Environment services for scheduling tens of thousands of concurrent agent tasks. | arXiv | | **[JudgeFlow: Agentic Workflow Optimization via Block Judge](https://arxiv.org/pdf/2601.07477v2)** - Proposes an evaluation-judge-optimization pipeline that assigns block-level responsibility scores to failing logic blocks in agentic workflows, focusing modifications on the most problematic components. | arXiv | | **[R-LAM: Reproducibility-Constrained Large Action Models for Scientific Workflow Automation](https://arxiv.org/pdf/2601.09749v1)** - Introduces a reproducibility-constrained framework for Large Action Models with structured action schemas, deterministic execution policies, and provenance tracking to ensure auditable and replayable workflows. | arXiv | | **[OpenTinker: Separating Concerns in Agentic Reinforcement Learning](https://arxiv.org/pdf/2601.07376v1)** - Proposes a composable RL infrastructure for LLM agents that separates algorithm design, execution, and agent-environment interaction with a centralized scheduler for managing shared training and inference resources. | arXiv | | **[ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging](https://arxiv.org/pdf/2601.07309v1)** - Introduces activation-guided, role-conditioned neuron transplantation for training-free merging of environment-specific LLM agent experts into a single generalist model. | arXiv | | **[PRISM: Disentangling SFT and RL Data via Gradient Concentration](https://arxiv.org/pdf/2601.07224v1)** - Proposes a dynamics-aware framework grounded in Schema Theory that routes agent training data to SFT or RL based on gradient concentration, using cognitive conflict as the allocation signal. | arXiv | | **[ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration](https://arxiv.org/pdf/2601.06860v2)** - Introduces a training framework for calibrating agent tool-use behavior through a self-evolving data flywheel and two-phase behavior calibration to reduce redundant and insufficient tool calls. | arXiv | | **[No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning](https://arxiv.org/pdf/2601.06794v1)** - Proposes a co-evolutionary framework that jointly optimizes the agent policy and its natural-language critic through synchronized GRPO updates, preventing the critic from becoming stale as the policy evolves. | arXiv | | **[CEDAR: Context Engineering for Agentic Data Science](https://arxiv.org/pdf/2601.06606v1)** - Introduces context engineering techniques for agentic workflows including structured DS-specific prompting, separate plan and code agents, and smart history rendering for fault tolerance and context management. | arXiv | | **[ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking](https://arxiv.org/pdf/2601.06487v2)** - Proposes a reinforcement learning paradigm that replaces pointwise scalar scoring with intra-group relative ranking via tournament-based schemes to address discrimination collapse in reward models for open-ended agent tasks. | arXiv | | **[Architecting AgentOps Needs CHANGE](https://arxiv.org/pdf/2601.06456v1)** - Introduces a conceptual framework with six capabilities (Contextualize, Harmonize, Anticipate, Negotiate, Generate, Evolve) for architecting AgentOps platforms that manage the lifecycle of evolving agentic AI systems. | arXiv | | **[Can We Predict Before Executing Machine Learning Agents?](https://arxiv.org/pdf/2601.05930v1)** - Proposes internalizing execution priors to predict agent outcomes before physical execution, using a Predict-then-Verify loop to accelerate ML agent workflows without running expensive experiments. | arXiv | | **[EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis](https://arxiv.org/pdf/2601.05808v1)** - Proposes an automated framework for generating scalable tool-interaction environments via programmatic synthesis, constructing diverse environment skeletons and task scenarios for agent SFT and RL training. | arXiv | | **[LIDL: LLM Integration Defect Localization via Knowledge Graph-Enhanced Multi-Agent Analysis](https://arxiv.org/pdf/2601.05539v1)** - Proposes a multi-agent framework for localizing integration defects in LLM-integrated software using code knowledge graphs enriched with LLM-aware annotations and counterfactual reasoning for root cause validation. | arXiv | | **[AT²PO: Agentic Turn-based Policy Optimization via Tree Search](https://arxiv.org/pdf/2601.04767v1)** - Proposes a unified framework for multi-turn agentic RL that uses a turn-level tree structure for entropy-guided exploration, turn-wise credit assignment, and turn-based policy optimization. | arXiv | | **[M-ASK: Multi-Agent Search and Knowledge Optimization Framework](https://arxiv.org/pdf/2601.04703v1)** - Proposes a framework that decouples agentic search into Search Behavior Agents and Knowledge Management Agents with turn-level rewards for multi-hop QA. | arXiv | | **[AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering](https://arxiv.org/pdf/2601.04620v1)** - Reframes agent self-improvement as a release engineering pipeline with implementation-blind quality signals, symptom-level diagnosis, and flip-centered regression gating. | arXiv | | **[4D-ARE: 4-Dimensional Attribution-Driven Agent Requirements Engineering](https://arxiv.org/pdf/2601.04556v1)** - Proposes an attribution-driven requirements engineering methodology for specifying what domain knowledge LLM agents need at design time, organized along four causal dimensions. | arXiv | | **[XGrammar 2: Dynamic and Efficient Structured Generation Engine for Agentic LLMs](https://arxiv.org/pdf/2601.04426v1)** - Proposes a structured generation engine for agentic LLMs with dynamic tag dispatching, JIT compilation, and cross-grammar caching for tool calling and conditional structured generation. | arXiv | | **[Transitive Expert Error and Routing Problems in Complex AI Systems](https://arxiv.org/pdf/2601.04416v1)** - Formalizes transitive expert error in AI routing architectures including MoE, multi-model orchestration, and tool-using agents, proposing boundary-aware calibration and coverage gap detection. | arXiv | | **[O-Researcher: An Open Ended Deep Research Model via Multi-Agent Distillation and Agentic RL](https://arxiv.org/pdf/2601.03743v1)** - Introduces a multi-agent workflow for synthesizing research-grade training data with a two-stage SFT plus agentic RL strategy for open-source deep research models. | arXiv | | **[Architecting Agentic Communities using Design Patterns](https://arxiv.org/pdf/2601.03624v2)** - Proposes design patterns for architecting agentic communities derived from enterprise distributed systems standards, covering coordination, governance, and formal collaboration agreements. | arXiv | | **[SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models](https://arxiv.org/pdf/2601.03555v1)** - Proposes a skill-conditioned RL framework for tool-using agents that grounds reward modeling in a library of skill prototypes for mid-level credit assignment. | arXiv | | **[Enhancing Model Context Protocol (MCP) with Context-Aware Server Collaboration](https://arxiv.org/pdf/2601.11595v2)** - Proposes a Context-Aware MCP architecture with a Shared Context Store that enables MCP servers to coordinate autonomously by reading from and writing to shared context memory. | arXiv | | **[Enhancing LLM Instruction Following: An Evaluation-Driven Multi-Agentic Workflow for Prompt Instructions Optimization](https://arxiv.org/pdf/2601.03359v1)** - Proposes a multi-agentic workflow that decouples optimization of primary task descriptions from constraint optimization using quantitative feedback for iterative prompt refinement. | arXiv | | **[InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents](https://arxiv.org/pdf/2601.03204v1)** - Proposes a general-purpose agent framework that keeps reasoning context bounded regardless of task duration by externalizing persistent state into a file-centric state abstraction. | arXiv | | **[The Path Ahead for Agentic AI: Challenges and Opportunities](https://arxiv.org/pdf/2601.02749v1)** - Surveys agentic AI architectures covering planning, memory, tool use, and iterative reasoning with a critical assessment of safety, alignment, and reliability challenges. | arXiv | | **[AMER-RCL: Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices](https://arxiv.org/pdf/2601.02732v1)** - Proposes an agentic memory enhanced recursive reasoning framework for root cause localization with cross-alert memory reuse and multi-agent recursive refinement. | arXiv | | **[Orchestral AI: A Framework for Agent Orchestration](https://arxiv.org/pdf/2601.02577v1)** - Introduces a lightweight Python framework providing a unified, type-safe interface for building LLM agents across multiple providers with tool calling, memory management, and MCP integration. | arXiv | | **[AI Agent Systems: Architectures, Applications, and Evaluation](https://arxiv.org/pdf/2601.01743v1)** - Surveys AI agent architectures spanning reasoning, planning, tool calling, orchestration patterns, and deployment settings with a unified taxonomy of agent components and design trade-offs. | arXiv | | **[CaveAgent: Transforming LLMs into Stateful Runtime Operators](https://arxiv.org/pdf/2601.01569v3)** - Proposes a dual-stream architecture that elevates the persistent Python runtime as the central locus of agent state, with stateful runtime management and skill injection for long-horizon tasks. | arXiv | | **[Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements](https://arxiv.org/pdf/2601.04235v1)** - Proposes an active feedback model where AI agents proactively interact with the environment to discover and verify feedback without relying on predefined measurements. | arXiv | | **[Warp-Cortex: An Asynchronous, Memory-Efficient Architecture for Million-Agent Cognitive Scaling on Consumer Hardware](https://arxiv.org/pdf/2601.01298v1)** - Proposes an asynchronous architecture for million-agent scaling that reduces memory complexity via singleton weight sharing and topological synapse-inspired KV-cache sparsification. | arXiv |

AI Agent Security (82)
| Paper | arXiv ID | |---|:---:| | **[Internal Safety Collapse in Frontier Large Language Models](https://arxiv.org/pdf/2603.23509)** - Reveals that AI agents produce harmful content (toxic text, exploits, dangerous data) as a side effect of completing normal professional tasks — no adversarial prompting needed. At least one mode (single-turn, ICL, or agentic) succeeds on every frontier model tested. 56 cross-domain scenarios across 8+ disciplines. | arXiv | | **[Confundo: Learning to Generate Robust Poison for Practical RAG Systems](https://arxiv.org/pdf/2602.06616v1)** - Trains an LLM to generate RAG poison that survives real-world content processing and query variation for stress-testing RAG defenses. | arXiv | | **[Malicious Agent Skills in the Wild: A Large-Scale Security Empirical Study](https://arxiv.org/pdf/2602.06547v1)** - Analyzes 98K agent skills from community registries to study the prevalence and nature of malicious third-party agent plugins. | arXiv | | **[Subgraph Reconstruction Attacks on Graph RAG Deployments with Practical Defenses](https://arxiv.org/pdf/2602.06495v1)** - Investigates whether attackers can reconstruct knowledge graphs from Graph RAG outputs through multi-turn probing. | arXiv | | **[Zero-Trust Runtime Verification for Agentic Payment Protocols](https://arxiv.org/pdf/2602.06345v1)** - Proposes consume-once mandate semantics for AI agent payment protocols to prevent replay and redirect attacks in autonomous transactions. | arXiv | | **[Identifying Adversary Tactics and Techniques in Malware Binaries with an LLM Agent](https://arxiv.org/pdf/2602.06325v1)** - Explores using an LLM agent to identify attack techniques in stripped malware binaries through incremental context retrieval. | arXiv | | **[Agent2Agent Threats in Safety-Critical LLM Assistants: A Human-Centric Taxonomy](https://arxiv.org/pdf/2602.05877v1)** - Maps attack paths in agent-to-agent communication protocols for automotive LLM assistants, from driver distraction to unauthorized vehicle control. | arXiv | | **[Learning to Inject: Automated Prompt Injection via Reinforcement Learning](https://arxiv.org/pdf/2602.05746v1)** - Explores using reinforcement learning to auto-generate prompt injection attacks that transfer across multiple frontier LLM models. | arXiv | | **[A Dual-Loop Agent Framework for Automated Vulnerability Reproduction](https://arxiv.org/pdf/2602.05721v1)** - Proposes an LLM agent with dual feedback loops for strategy and code to automate vulnerability reproduction from CVE descriptions. | arXiv | | **[Human Society-Inspired Approaches to Agentic AI Security: The 4C Framework](https://arxiv.org/pdf/2602.01942v1)** - Organizes agentic security risks into four layers (Core, Connection, Cognition, Compliance) to address trust and governance issues beyond prompt injection. | arXiv | | **[MAGIC: A Co-Evolving Attacker-Defender Adversarial Game for Robust LLM Safety](https://arxiv.org/pdf/2602.01539v2)** - Proposes a co-evolving RL game between an attacker and defender agent to stress-test safety alignment against novel attack patterns. | arXiv | | **[TxRay: Agentic Postmortem of Live Blockchain Attacks](https://arxiv.org/pdf/2602.01317v4)** - Introduces an LLM agentic system that reconstructs blockchain exploit lifecycles from limited evidence and generates runnable proof-of-concept reproductions. | arXiv | | **[To Defend Against Cyber Attacks, We Must Teach AI Agents to Hack](https://arxiv.org/pdf/2602.02595v1)** - Argues that AI-agent-driven cyber attacks are inevitable and proposes building frontier offensive AI capabilities responsibly as essential defensive infrastructure. | arXiv | | **[SMCP: Secure Model Context Protocol](https://arxiv.org/pdf/2602.01129v1)** - Proposes protocol-level security improvements for the Model Context Protocol including unified identity management, mutual authentication, and fine-grained policy enforcement. | arXiv | | **[Persuasion Propagation in LLM Agents](https://arxiv.org/pdf/2602.00851v1)** - Investigates how user persuasion during conversation can carry over and change how autonomous AI agents perform later tasks. | arXiv | | **[When Agents "Misremember" Collectively: Exploring the Mandela Effect in LLM-based Multi-Agent Systems](https://arxiv.org/pdf/2602.00428v1)** - Explores how collective false memories form in LLM-based multi-agent systems and proposes defenses including cognitive anchoring and alignment-based approaches. | arXiv | | **["Someone Hid It": Query-Agnostic Black-Box Attacks on LLM-Based Retrieval](https://arxiv.org/pdf/2602.00364v2)** - Proposes a black-box attack method that generates transferable adversarial tokens to manipulate LLM-based retrieval systems without needing access to the target's queries or model. | arXiv | | **[From Similarity to Vulnerability: Key Collision Attack on LLM Semantic Caching](https://arxiv.org/pdf/2601.23088v1)** - Introduces CacheAttack, a black-box framework that exploits the trade-off between locality and collision resistance in semantic caching to hijack LLM responses and manipulate agent behavior. | arXiv | | **[TessPay: Verify-then-Pay Infrastructure for Trusted Agentic Commerce](https://arxiv.org/pdf/2602.00213v1)** - Proposes a verify-then-pay infrastructure for agent transactions that locks funds in escrow, requires cryptographic proof of task execution, and releases payment only after verification. | arXiv | | **[Whispers of Wealth: Red-Teaming Google's Agent Payments Protocol via Prompt Injection](https://arxiv.org/pdf/2601.22569v1)** - Red-teams Google's Agent Payments Protocol via prompt injection attacks that manipulate product ranking and extract sensitive user data in agent-led purchase flows. | arXiv | | **[StepShield: When, Not Whether to Intervene on Rogue Agents](https://arxiv.org/pdf/2601.22136v1)** - Introduces a benchmark for evaluating when agent violations are detected during execution rather than just whether, with temporal metrics for early intervention and tokens saved. | arXiv | | **[Delegation Without Living Governance](https://arxiv.org/pdf/2601.21226v1)** - Argues that static compliance-based governance is insufficient for agentic AI at machine speed and proposes runtime governance to preserve human relevance in agent-driven decision-making. | arXiv | | **[DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning](https://arxiv.org/pdf/2601.20615v3)** - Introduces an adversarial attack that poisons retrieval contexts in RAG-based code generation to force longer outputs, increasing GPU latency and energy consumption. | arXiv | | **[Securing AI Agents in Cyber-Physical Systems: A Survey of Environmental Interactions, Deepfake Threats, and Defenses](https://arxiv.org/pdf/2601.20184v1)** - Surveys security threats targeting AI agents in cyber-physical systems, covering deepfake attacks, MCP-mediated vulnerabilities, and defense-in-depth architectures. | arXiv | | **[Multimodal Multi-Agent Ransomware Analysis Using AutoGen](https://arxiv.org/pdf/2601.20346v1)** - Explores AutoGen-based multi-agent coordination with specialized agents for static, dynamic, and network-level ransomware family classification using confidence-aware decisions. | arXiv | | **[SHIELD: An Auto-Healing Agentic Defense Framework for LLM Resource Exhaustion Attacks](https://arxiv.org/pdf/2601.19174v1)** - Introduces a multi-agent auto-healing defense framework with semantic similarity retrieval, pattern matching, and an evolving knowledgebase for defending LLMs against resource exhaustion attacks. | arXiv | | **[AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection](https://arxiv.org/pdf/2601.19138v1)** - Explores agentic AI for pre-commit secure code review that uses autonomous decision-making, tool invocation, and security-focused semantic memories to detect immature vulnerabilities. | arXiv | | **[AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security](https://arxiv.org/pdf/2601.18491v1)** - Introduces a three-dimensional taxonomy for agentic risks and a diagnostic guardrail framework that monitors agent trajectories with fine-grained root cause analysis beyond binary safety labels. | arXiv | | **[When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents](https://arxiv.org/pdf/2601.17887v1)** - Examines how benign personal memories in personalized agents can bias intent inference and cause models to legitimize harmful queries through a previously unexplored safety vector. | arXiv | | **[Multi-Agent Collaborative Intrusion Detection for LAE-IoT](https://arxiv.org/pdf/2601.17817v1)** - Proposes a multi-agent collaborative framework with specialized LLM-enhanced agents for intelligent data processing and adaptive intrusion classification in aerial IoT networks. | arXiv | | **[Faramesh: A Protocol-Agnostic Execution Control Plane for Autonomous Agent Systems](https://arxiv.org/pdf/2601.17744v1)** - Introduces a protocol-agnostic execution control plane for autonomous agents that enforces authorization boundaries with canonical action representation and deterministic policy evaluation. | arXiv | | **[A Systemic Evaluation of Multimodal RAG Privacy](https://arxiv.org/pdf/2601.17644v2)** - Examines privacy risks in multimodal RAG pipelines through inclusion inference and metadata leakage attacks during standard model prompting. | arXiv | | **[Breaking the Protocol: Security Analysis of the Model Context Protocol Specification](https://arxiv.org/pdf/2601.17549v1)** - Presents the first security analysis of the Model Context Protocol specification, identifying three protocol-level vulnerabilities and proposing backward-compatible security extensions. | arXiv | | **[Prompt Injection Attacks on Agentic Coding Assistants: A Systematic Analysis](https://arxiv.org/pdf/2601.17548v1)** - Surveys 78 studies to systematize prompt injection attacks on agentic coding assistants with a three-dimensional taxonomy across delivery vectors, modalities, and propagation. | arXiv | | **[Connect the Dots: Knowledge Graph-Guided Crawler Attack on Retrieval-Augmented Generation Systems](https://arxiv.org/pdf/2601.15678v2)** - Introduces RAGCrawler, a knowledge graph-guided attack that adaptively steals RAG corpus content through targeted queries to maximize coverage under a query budget. | arXiv | | **[Securing LLM-as-a-Service for Small Businesses: An Industry Case Study of a Distributed Chatbot Deployment Platform](https://arxiv.org/pdf/2601.15528v1)** - Presents a multi-tenant chatbot deployment platform with container-based isolation and platform-level defenses against prompt injection attacks in RAG-based systems. | arXiv | | **[Interoperable Architecture for Digital Identity Delegation for AI Agents with Blockchain Integration](https://arxiv.org/pdf/2601.14982v1)** - Introduces delegation grants and a canonical verification context for bounded, auditable identity delegation across human users and AI agents in heterogeneous identity ecosystems. | arXiv | | **[INFA-Guard: Mitigating Malicious Propagation via Infection-Aware Safeguarding in LLM-Based Multi-Agent Systems](https://arxiv.org/pdf/2601.14667v1)** - Proposes an infection-aware defense framework for multi-agent systems that distinguishes infected agents from attackers and applies topological constraints to halt malicious propagation. | arXiv | | **[Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems](https://arxiv.org/pdf/2601.14662v1)** - Proposes AGEA, an agentic framework using novelty-guided exploration and graph memory to steal latent entity-relation graphs from GraphRAG systems under strict query budgets. | arXiv | | **[NeuroFilter: Privacy Guardrails for Conversational LLM Agents](https://arxiv.org/pdf/2601.14660v1)** - Introduces activation-space guardrails that detect privacy-violating intent in LLM agents through linear separation of internal representations, including drift detection across multi-turn conversations. | arXiv | | **[VirtualCrime: Evaluating Criminal Potential of Large Language Models via Sandbox Simulation](https://arxiv.org/pdf/2601.13981v1)** - Proposes a three-agent sandbox simulation framework with 40 crime tasks across 13 objectives to evaluate the criminal capabilities of LLM agents in realistic scenarios. | arXiv | | **[PINA: Prompt Injection Attack against Navigation Agents](https://arxiv.org/pdf/2601.13612v1)** - Introduces an adaptive prompt injection framework targeting navigation agents under black-box, long-context, and action-executable constraints across indoor and outdoor environments. | arXiv | | **[Prompt Injection Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching](https://arxiv.org/pdf/2601.13186v1)** - Explores a multi-agent defense pipeline combining semantic similarity caching, nested learning, and observability-aware evaluation to mitigate prompt injection attacks while reducing computational costs. | arXiv | | **[CODE: A Contradiction-Based Deliberation Extension Framework for Overthinking Attacks on Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.13112v1)** - Introduces an overthinking attack framework for RAG systems with reasoning models, using multi-agent-constructed poisoning samples that cause excessive reasoning token consumption without degrading task accuracy. | arXiv | | **[AgenTRIM: Tool Risk Mitigation for Agentic AI](https://arxiv.org/pdf/2601.12449v1)** - Introduces a framework for detecting and mitigating tool-driven agency risks through offline interface verification and runtime per-step least-privilege tool access with adaptive filtering. | arXiv | | **[Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryption](https://arxiv.org/pdf/2601.12331v1)** - Proposes a privacy-preserving RAG framework using conditional approximate distance-comparison-preserving encryption that enables similarity computation on encrypted embeddings in untrusted cloud environments. | arXiv | | **[Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework](https://arxiv.org/pdf/2601.11893v1)** - Proposes a mandatory access control framework for LLM agent systems that monitors agent-tool interactions via information flow graphs and enforces attribute-based policies against privilege escalation. | arXiv | | **[Institutional AI: Governing LLM Collusion in Multi-Agent Cournot Markets via Public Governance Graphs](https://arxiv.org/pdf/2601.11369v2)** - Introduces governance graphs as public, immutable manifests with enforceable sanctions and restorative paths to govern multi-agent LLM coordination and prevent harmful collusion. | arXiv | | **[SD-RAG: A Prompt-Injection-Resilient Framework for Selective Disclosure in Retrieval-Augmented Generation](https://arxiv.org/pdf/2601.11199v1)** - Proposes a prompt-injection-resilient RAG framework that decouples security enforcement from generation by applying sanitization and policy-aware disclosure controls during the retrieval phase. | arXiv | | **[Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agents](https://arxiv.org/pdf/2601.10955v1)** - Introduces a stealthy multi-turn economic DoS attack exploiting the agent-tool communication loop through MCP-compatible tool server modifications that inflate costs by up to 658x. | arXiv | | **[Hidden-in-Plain-Text: A Benchmark for Social-Web Indirect Prompt Injection in RAG](https://arxiv.org/pdf/2601.10923v2)** - Introduces a benchmark and harness for evaluating web-facing RAG systems under indirect prompt injection and retrieval poisoning attacks with standardized end-to-end evaluation from ingestion to generation. | arXiv | | **[Breaking Up with Normatively Monolithic Agency with GRACE: A Reason-Based Neuro-Symbolic Architecture for Safe and Ethical AI Alignment](https://arxiv.org/pdf/2601.10520v2)** - Introduces a neuro-symbolic containment architecture that decouples normative reasoning from instrumental decision-making through a Moral Module, Decision-Making Module, and compliance Guard for agent safety. | arXiv | | **[AgentGuardian: Learning Access Control Policies to Govern AI Agent Behavior](https://arxiv.org/pdf/2601.10440v1)** - Presents a security framework that learns context-aware access-control policies from monitored execution traces to govern AI agent operations and detect malicious inputs while preserving normal functionality. | arXiv | | **[Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale](https://arxiv.org/pdf/2601.10338v1)** - Analyzes 42,447 agent skills from two major marketplaces to study the prevalence and types of security vulnerabilities spanning prompt injection, data exfiltration, privilege escalation, and supply chain risks. | arXiv | | **[CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents](https://arxiv.org/pdf/2601.09923v1)** - Proposes single-shot planning for Computer Use Agents that provides provable control flow integrity against prompt injection while preserving agent capability. | arXiv | | **[Blue Teaming Function-Calling Agents](https://arxiv.org/pdf/2601.09292v1)** - Tests open-source function-calling LLMs against multiple attack types with various defenses to study the readiness of current models and mitigations for production deployment. | arXiv | | **[Too Helpful to Be Safe: User-Mediated Attacks on Planning and Web-Use Agents](https://arxiv.org/pdf/2601.10758v1)** - Examines how commercial planning and web-use agents handle user-mediated attacks where the user themselves provides adversarial instructions without explicit safety requests. | arXiv | | **[Semantic Laundering in AI Agent Architectures: Why Tool Boundaries Do Not Confer Epistemic Warrant](https://arxiv.org/pdf/2601.08333v1)** - Formalizes how propositions gain unwarranted trust by crossing architecturally trusted interfaces in agent systems, studying whether circular epistemic justification is inevitable under standard assumptions. | arXiv | | **[Towards Verifiably Safe Tool Use for LLM Agents](https://arxiv.org/pdf/2601.08012v1)** - Proposes applying System-Theoretic Process Analysis to identify hazards in agent tool-use workflows, deriving formal safety specifications enforced through a capability-enhanced Model Context Protocol framework. | arXiv | | **[MCP-ITP: An Automated Framework for Implicit Tool Poisoning in MCP](https://arxiv.org/pdf/2601.07395v1)** - Introduces an automated framework for implicit tool poisoning in MCP where a poisoned tool remains uninvoked but its metadata manipulates the agent into performing malicious operations through legitimate tools. | arXiv | | **[Overcoming the Retrieval Barrier: Indirect Prompt Injection in the Wild for LLM Systems](https://arxiv.org/pdf/2601.07072v1)** - Proposes a black-box attack that decomposes indirect prompt injection into trigger and attack fragments to study end-to-end IPI exploits under natural queries across RAG and agentic systems. | arXiv | | **[MemTrust: A Zero-Trust Architecture for Unified AI Memory System](https://arxiv.org/pdf/2601.07004v1)** - Proposes a hardware-backed zero-trust architecture for AI memory systems that applies TEE protection across five functional layers with a cross-application sharing protocol for agent memory. | arXiv | | **[SafePro: Evaluating the Safety of Professional-Level AI Agents](https://arxiv.org/pdf/2601.06663v2)** - Introduces a benchmark for evaluating safety alignment of AI agents performing professional-level tasks across diverse domains, uncovering new unsafe behaviors in complex professional contexts. | arXiv | | **[Agentic LLMs as Powerful Deanonymizers: Re-identification of Participants in the Anthropic Interviewer Dataset](https://arxiv.org/pdf/2601.05918v1)** - Demonstrates that off-the-shelf LLM agents with web search can re-identify participants in anonymized qualitative datasets using only natural-language prompts, lowering the technical barrier for re-identification attacks. | arXiv | | **[Toward Safe and Responsible AI Agents: A Three-Pillar Model for Transparency, Accountability, and Trustworthiness](https://arxiv.org/pdf/2601.06223v1)** - Proposes a conceptual and operational framework for safe AI agent development grounded in transparency, accountability, and trustworthiness, with progressive validation analogous to autonomous driving stages. | arXiv | | **[VIGIL: Defending LLM Agents Against Tool Stream Injection via Verify-Before-Commit](https://arxiv.org/pdf/2601.05755v2)** - Proposes a verify-before-commit protocol for defending LLM agents against tool stream injection, using speculative hypothesis generation and intent-grounded verification to balance security with reasoning flexibility. | arXiv | | **[Memory Poisoning Attack and Defense on Memory Based LLM-Agents](https://arxiv.org/pdf/2601.05504v2)** - Evaluates memory poisoning attacks on memory-augmented LLM agents and proposes two defense mechanisms: input/output moderation with composite trust scoring and memory sanitization with trust-aware retrieval. | arXiv | | **[STELP: Secure Transpilation and Execution of LLM-Generated Programs](https://arxiv.org/pdf/2601.05467v3)** - Proposes a secure transpiler and executor for LLM-generated code that detects vulnerabilities and safely executes code snippets in autonomous production AI systems without relying on human review. | arXiv | | **[Conformity and Social Impact on AI Agents](https://arxiv.org/pdf/2601.05384v1)** - Investigates conformity bias in AI agents under social pressure using adapted visual experiments from social psychology, studying sensitivity to group size, unanimity, task difficulty, and source characteristics. | arXiv | | **[Defense Against Indirect Prompt Injection via Tool Result Parsing](https://arxiv.org/pdf/2601.04795v1)** - Proposes a tool result parsing method for defending LLM agents against indirect prompt injection by providing precise data while filtering out injected malicious code. | arXiv | | **[Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries](https://arxiv.org/pdf/2601.04583v1)** - Surveys agent-blockchain interoperability patterns and threat models for agent-driven transaction pipelines, covering custody models, policy enforcement, and multi-agent workflows. | arXiv | | **[BackdoorAgent: A Unified Framework for Backdoor Attacks on LLM-based Agents](https://arxiv.org/pdf/2601.04566v2)** - Proposes a stage-aware framework for analyzing backdoor attacks across planning, memory, and tool-use stages of LLM agent workflows with cross-stage trigger propagation. | arXiv | | **[HoneyTrap: Deceiving LLM Attackers with Resilient Multi-Agent Defense](https://arxiv.org/pdf/2601.04034v1)** - Proposes a deceptive defense framework using collaborative defender agents to counter multi-turn jailbreak attacks by strategically wasting attacker resources. | arXiv | | **[SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems](https://arxiv.org/pdf/2601.03979v1)** - Systematizes privacy risks, mitigation techniques, and evaluation strategies in RAG systems through a comprehensive literature review with a taxonomy and process diagram. | arXiv | | **[AgentMark: Utility-Preserving Behavioral Watermarking for Agents](https://arxiv.org/pdf/2601.03294v1)** - Proposes a behavioral watermarking framework that embeds multi-bit identifiers into agent planning decisions for IP protection and regulatory provenance while preserving utility. | arXiv | | **[Structural Representations for Cross-Attack Generalization in AI Agent Threat Detection](https://arxiv.org/pdf/2601.01723v1)** - Proposes structural tokenization that encodes execution-flow patterns instead of conversational content to improve cross-attack generalization in AI agent threat detection. | arXiv | | **[Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage](https://arxiv.org/pdf/2601.01685v1)** - Introduces a cognitive collusion attack where colluding agents steer victim beliefs using only truthful evidence fragments distributed through public channels without covert communication. | arXiv | | **[MCP-SandboxScan: WASM-based Secure Execution and Runtime Analysis for MCP Tools](https://arxiv.org/pdf/2601.01241v1)** - Proposes a lightweight framework that safely executes untrusted MCP tools inside a WebAssembly sandbox and produces auditable reports of external-to-sink exposures. | arXiv | | **[Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.ai](https://arxiv.org/pdf/2601.01090v2)** - Analyzes toxicity adoption dynamics among LLM-driven agents on a fully AI-driven social platform, studying how cumulative toxic exposure affects the probability of toxic responses. | arXiv | | **[Trajectory Guard: A Lightweight, Sequence-Aware Model for Real-Time Anomaly Detection in Agentic AI](https://arxiv.org/pdf/2601.00516v1)** - Proposes a Siamese Recurrent Autoencoder with hybrid contrastive-reconstruction loss for real-time anomaly detection in agent action trajectories. | arXiv | | **[Mapping Human Anti-collusion Mechanisms to Multi-agent AI](https://arxiv.org/pdf/2601.00360v1)** - Maps human anti-collusion mechanisms including sanctions, leniency, monitoring, and market design to potential interventions for multi-agent AI systems. | arXiv | | **[Making Theft Useless: Adulteration-Based Protection of Proprietary Knowledge Graphs in GraphRAG Systems](https://arxiv.org/pdf/2601.00274v1)** - Proposes a data adulteration framework that pre-emptively injects plausible but false entries into knowledge graphs to make stolen GraphRAG KGs unusable to adversaries. | arXiv | | **[When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agents](https://arxiv.org/pdf/2601.00240v2)** - Examines intergroup bias in LLM agents under minimal group cues and formalizes a Belief Poisoning Attack that manipulates agent identity beliefs to induce outgroup bias toward humans. | arXiv |
## Contributing We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. - Submit new papers via PR - Suggest category improvements ## License MIT License - see [LICENSE](LICENSE) This is a curated list. Papers listed here are created and published by their respective authors, not by us. We curate papers relevant to the AI agent ecosystem and do not audit, endorse, or guarantee the correctness of listed research. If you find an issue with a listed paper or want a paper removed, please [open an issue](https://github.com/VoltAgent/awesome-ai-agent-papers/issues) and we'll take care of it promptly. [codex-badge]: https://img.shields.io/github/stars/VoltAgent/awesome-codex-subagents?style=classic&label=Codex%20Subagents&color=000000&logo=data:image/svg%2bxml;base64,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 [codex-link]: https://github.com/VoltAgent/awesome-codex-subagents