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2025-02-18T00:06:55.671000 | video-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model | 2 | {
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improve reasoning have been limited to solving mathematical problems and
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2025-02-17T23:51:50.821000 | Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems | 2 | {
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Multi-Agent Systems | Recent advancements in LLM-based multi-agent (LLM-MA) systems have shown
promise, yet significant challenges remain in managing communication and
refinement when agents collaborate on complex tasks. In this paper, we propose
Talk Structurally, Act Hierarchically (TalkHier), a novel framework
that introduces a structure... | 12 | 67b411e55e634139c0d86a4c | null | null | |
2025-02-17T23:37:16.770000 | One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs | 2 | {
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Reasoning in Mathematical LLMs | Leveraging mathematical Large Language Models (LLMs) for proof generation is
a fundamental topic in LLMs research. We argue that the ability of current LLMs
to prove statements largely depends on whether they have encountered the
relevant proof process during training. This reliance limits their deeper
understanding of... | 7 | 67b40e57bffd44cc85976f0e | null | null | |
2025-02-17T23:30:53.097000 | Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory Sharpening | 3 | {
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Trajectory Sharpening | We propose Diffusion-Sharpening, a fine-tuning approach that enhances
downstream alignment by optimizing sampling trajectories. Existing RL-based
fine-tuning methods focus on single training timesteps and neglect
trajectory-level alignment, while recent sampling trajectory optimization
methods incur significant inferen... | 16 | 67b40ce8d3c5f50aa9b71f9a | null | null | |
2025-02-17T23:29:29.396000 | HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation | 2 | {
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Generation | The remarkable success of the autoregressive paradigm has made significant
advancement in Multimodal Large Language Models (MLLMs), with powerful models
like Show-o, Transfusion and Emu3 achieving notable progress in unified image
understanding and generation. For the first time, we uncover a common
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2025-02-17T23:06:03.562000 | SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL | 2 | {
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Selection for Text-to-SQL | Text-to-SQL aims to convert natural language questions into executable SQL
queries. While previous approaches, such as skeleton-masked selection, have
demonstrated strong performance by retrieving similar training examples to
guide large language models (LLMs), they struggle in real-world scenarios where
such examples ... | 7 | 67b4069a3d0f54ab38159520 | null | null | |
2025-02-17T22:43:51.555000 | CRANE: Reasoning with constrained LLM generation | 2 | {
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produce outputs that are both syntactically and semantically correct.
Constrained LLM generation is a promising direction to enforce adherence to
formal grammar, but prior works have empirically observed that strict
enforcement of formal constrai... | 18 | 67b401e03995f28d45c21354 | null | null | |
2025-02-17T22:10:49.900000 | Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM's Nest | 2 | {
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annotations, have been carefully prepared to incubate advanced large language
models (LLMs). In contrast, for information extraction (IE), pre-training data,
such as BIO-tagged sequences, are hard to scale up. We show that IE models can
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2025-02-17T22:05:54.047000 | Building A Proof-Oriented Programmer That Is 64% Better Than GPT-4o Under Data Scarsity | 2 | {
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"use... | 2025-02-17T15:24:11 | Building A Proof-Oriented Programmer That Is 64% Better Than GPT-4o
Under Data Scarsity | Existing LMs struggle with proof-oriented programming due to data scarcity,
which manifest in two key ways: (1) a lack of sufficient corpora for
proof-oriented programming languages such as F*, and (2) the absence of
large-scale, project-level proof-oriented implementations that can teach the
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2025-02-17T17:09:38.653000 | The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks | 2 | {
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Agentic Tasks | Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving
capabilities, but their effectiveness in interactive environments can be
limited. This paper introduces and analyzes overthinking in LRMs. A phenomenon
where models favor extended internal reasoning chains over environmental
interaction. Throu... | 54 | 67b078cc1c879c0cbb785dbb | null | null | |
2025-02-17T12:27:43.231000 | Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models | 2 | {
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Language Models | Fine-tuning Large Language Models (LLMs) on specific datasets is a common
practice to improve performance on target tasks. However, this performance gain
often leads to overfitting, where the model becomes too specialized in either
the task or the characteristics of the training data, resulting in a loss of
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2025-02-17T10:18:04.718000 | CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages | 2 | {
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"us... | 2025-02-14T18:42:25 | CLaMP 3: Universal Music Information Retrieval Across Unaligned
Modalities and Unseen Languages | CLaMP 3 is a unified framework developed to address challenges of cross-modal
and cross-lingual generalization in music information retrieval. Using
contrastive learning, it aligns all major music modalities--including sheet
music, performance signals, and audio recordings--with multilingual text in a
shared representa... | 4 | 67b2e11ed2ee8e627dec1c25 | null | null | |
2025-02-17T09:25:39.949000 | Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding | 2 | {
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for 3D visual grounding. Conventional methods are difficult to meet the
requirements of real-time inference due to the two-stage or point-based
architecture. Inspired by the success of multi-level fully sparse convolutional
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2025-02-17T08:54:04.307000 | DarwinLM: Evolutionary Structured Pruning of Large Language Models | 7 | {
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NLP tasks. However, their massive computational costs limit their widespread
use, particularly in real-time applications. Structured pruning offers an
effective solution by compressing models and directly providing end-to-end
speed improvemen... | 17 | 67b33f642f3994b7d95b6eb1 | null | null | |
2025-02-17T08:41:41.933000 | ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation | 2 | {
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However, they struggle to generate rare or unseen concepts. To address this
challenge, we explore the usage of Retrieval-Augmented Generation (RAG) with
image generation models. We propose ImageRAG, a method that dynamically
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2025-02-17T08:29:25.102000 | Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages | 2 | {
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Small Multilingual Language Models for Low-Resource Languages | Low-resource languages (LRLs) face significant challenges in natural language
processing (NLP) due to limited data. While current state-of-the-art large
language models (LLMs) still struggle with LRLs, smaller multilingual models
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2025-02-17T07:24:28.545000 | Cluster and Predict Latents Patches for Improved Masked Image Modeling | 2 | {
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representation learning, however existing MIM models still lag behind the
state-of-the-art. In this paper, we systematically analyze target
representations, loss functions, and architectures, to introduce CAPI - a novel
pure-MIM framework that r... | 4 | 67b32a574d60b7d162dffdd4 | null | null | |
2025-02-17T05:36:23.051000 | AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting | 2 | {
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2025-02-17T04:28:55.526000 | We Can't Understand AI Using our Existing Vocabulary | 4 | {
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2025-02-17T03:06:17.932000 | Precise Parameter Localization for Textual Generation in Diffusion Models | 2 | {
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2025-02-17T02:03:05.624000 | MRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE Solvers | 2 | {
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2025-02-17T01:33:15.971000 | V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multi-Modal Large Language Models | 2 | {
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2025-02-17T00:04:19.389000 | Jailbreaking to Jailbreak | 2 | {
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2025-02-17T00:03:18.228000 | Large Language Diffusion Models | 9 | {
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language models (LLMs). We challenge this notion by introducing LLaDA, a
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2025-02-16T23:57:43.710000 | Diverse Inference and Verification for Advanced Reasoning | 3 | {
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progress in mathematics and coding, yet find challenging advanced tasks such as
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2025-02-16T23:07:53.170000 | FoNE: Precise Single-Token Number Embeddings via Fourier Features | 3 | {
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2025-02-16T22:51:55.408000 | MM-RLHF: The Next Step Forward in Multimodal LLM Alignment | 5 | {
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most state-of-the-art models have not undergone thorough alignment with human
preferences. This gap exists because current alignment research has primarily
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2025-02-16T22:50:38.622000 | Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model | 3 | {
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Video Foundation Model | We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model
with 30B parameters and the ability to generate videos up to 204 frames in
length. A deep compression Variational Autoencoder, Video-VAE, is designed for
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2025-02-16T22:22:08.102000 | Region-Adaptive Sampling for Diffusion Transformers | 3 | {
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2025-02-16T22:20:53.227000 | ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models | 5 | {
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images and, by some measures, have poorer spatial cognition than small children
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2025-02-16T21:31:11.459000 | STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning | 2 | {
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long-horizon tasks in dynamic environments while maintaining robust
decision-making and adaptability. To achieve this goal, we propose the
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2025-02-14T21:20:14.771000 | Latent Radiance Fields with 3D-aware 2D Representations | 2 | {
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2025-02-14T09:18:18.443000 | Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges | 2 | {
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2025-02-14T08:47:33.396000 | VFX Creator: Animated Visual Effect Generation with Controllable Diffusion Transformer | 2 | {
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2025-02-14T04:50:27.474000 | DexTrack: Towards Generalizable Neural Tracking Control for Dexterous Manipulation from Human References | 2 | {
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2025-02-14T02:58:25.756000 | Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights | 2 | {
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2025-02-14T02:50:35.108000 | CoSER: Coordinating LLM-Based Persona Simulation of Established Roles | 2 | {
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2025-02-14T02:35:53.718000 | SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models | 2 | {
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2025-02-14T02:27:45.749000 | Exploring the Potential of Encoder-free Architectures in 3D LMMs | 2 | {
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2025-02-14T01:34:58.800000 | MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency | 2 | {
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2025-02-14T01:29:44.233000 | Typhoon T1: An Open Thai Reasoning Model | 2 | {
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2025-02-14T00:16:30.034000 | CoT-Valve: Length-Compressible Chain-of-Thought Tuning | 2 | {
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2025-02-13T23:32:15.420000 | mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data | 2 | {
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2025-02-13T23:23:42.492000 | EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents | 2 | {
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2025-02-13T23:10:44.295000 | Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation | 2 | {
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2025-02-13T22:57:03.709000 | InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU | 6 | {
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2025-02-13T22:56:23.567000 | TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models | 3 | {
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2025-02-13T22:01:48.364000 | An Open Recipe: Adapting Language-Specific LLMs to a Reasoning Model in One Day via Model Merging | 4 | {
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2025-02-13T21:59:28.400000 | The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding | 3 | {
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2025-02-13T21:55:58.708000 | Logical Reasoning in Large Language Models: A Survey | 5 | {
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reasoning capabilities. However, their ability to perform rigorous logical
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2025-02-13T21:42:37.926000 | SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models | 2 | {
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Language Models | We introduce SelfCite, a novel self-supervised approach that aligns LLMs to
generate high-quality, fine-grained, sentence-level citations for the
statements in their generated responses. Instead of only relying on costly and
labor-intensive annotations, SelfCite leverages a reward signal provided by the
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2025-02-13T14:57:40.061000 | Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning | 2 | {
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Image Dense Contrastive Representation Learning | Dense contrastive representation learning (DCRL) has greatly improved the
learning efficiency for image-dense prediction tasks, showing its great
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2025-02-13T11:41:16.499000 | PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs | 2 | {
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significant real-world applications. We present PDE-Controller, a framework
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2025-02-13T05:48:33.939000 | LLM Modules: Knowledge Transfer from a Large to a Small Model using Enhanced Cross-Attention | 2 | {
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Enhanced Cross-Attention | In this work, we propose an architecture of LLM Modules that enables the
transfer of knowledge from a large pre-trained model to a smaller model using
an Enhanced Cross-Attention mechanism. In the proposed scheme, the Qwen2-1.5B
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2025-02-13T03:47:28.654000 | Ignore the KL Penalty! Boosting Exploration on Critical Tokens to Enhance RL Fine-Tuning | 2 | {
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Enhance RL Fine-Tuning | The ability to achieve long-term goals is a key challenge in the current
development of large language models (LLMs). To address this, pre-trained LLMs
can be fine-tuned with reinforcement learning (RL) to explore solutions that
optimize a given goal. However, exploration with LLMs is difficult, as a
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2025-02-13T03:45:43.646000 | Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance | 4 | {
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Environment Affordance | Recent character image animation methods based on diffusion models, such as
Animate Anyone, have made significant progress in generating consistent and
generalizable character animations. However, these approaches fail to produce
reasonable associations between characters and their environments. To address
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2025-02-13T03:34:47.873000 | BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models | 2 | {
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Language Models | Previous multilingual benchmarks focus primarily on simple understanding
tasks, but for large language models(LLMs), we emphasize proficiency in
instruction following, reasoning, long context understanding, code generation,
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2025-02-13T03:30:35.137000 | Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing | 2 | {
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tasks into a stronger one. However, parameter conflicts between models leads to
performance degradation in averaging. While model routing addresses this issue
by selecting individual models during inference, it imposes excessive storage
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2025-02-13T01:47:30.377000 | MetaSC: Test-Time Safety Specification Optimization for Language Models | 2 | {
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(LM) safety reasoning at inference time without modifying model weights.
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2025-02-13T01:39:08.775000 | WorldGUI: Dynamic Testing for Comprehensive Desktop GUI Automation | 4 | {
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grounding. However, planning remains highly challenging, especially due to
sensitivity to the initial state of the environment. Specifically, slight
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2025-02-13T00:06:04.056000 | Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey | 2 | {
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Models: A Survey | Retrieval-Augmented Generation (RAG) is an advanced technique designed to
address the challenges of Artificial Intelligence-Generated Content (AIGC). By
integrating context retrieval into content generation, RAG provides reliable
and up-to-date external knowledge, reduces hallucinations, and ensures relevant
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2025-02-13T00:04:29.194000 | NoLiMa: Long-Context Evaluation Beyond Literal Matching | 2 | {
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to 1M tokens. A popular method for evaluating these capabilities is the
needle-in-a-haystack (NIAH) test, which involves retrieving a "needle"
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2025-02-12T23:50:07.130000 | TextAtlas5M: A Large-scale Dataset for Dense Text Image Generation | 2 | {
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years and are processing increasingly longer and comprehensive text prompt. In
everyday life, dense and intricate text appears in contexts like
advertisements, infographics, and signage, where the integration of both text
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2025-02-12T23:47:56.223000 | Light-A-Video: Training-free Video Relighting via Progressive Light Fusion | 2 | {
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Fusion | Recent advancements in image relighting models, driven by large-scale
datasets and pre-trained diffusion models, have enabled the imposition of
consistent lighting. However, video relighting still lags, primarily due to the
excessive training costs and the scarcity of diverse, high-quality video
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2025-02-12T23:47:31.651000 | LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its Hybrid | 2 | {
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advantages like linear-time training and constant-memory inference over
sequence lengths. However, existing sequence parallelism (SP) methods are
either not optimized for the right-product-first feature of linear attention or
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2025-02-12T23:42:44.287000 | LLM Pretraining with Continuous Concepts | 4 | {
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language model pretraining. Representations are learned as a result of
optimizing for token-level perplexity. We propose Continuous Concept Mixing
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2025-02-12T23:41:41.281000 | Distillation Scaling Laws | 4 | {
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performance based on a compute budget and its allocation between the student
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2025-02-12T21:57:30.420000 | SARChat-Bench-2M: A Multi-Task Vision-Language Benchmark for SAR Image Interpretation | 4 | {
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2025-02-12T21:55:44.479000 | CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation | 2 | {
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2025-02-12T21:48:00.325000 | Next Block Prediction: Video Generation via Semi-Autoregressive Modeling | 2 | {
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2025-02-12T21:45:28.944000 | Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance | 5 | {
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2025-02-12T21:43:42.404000 | DPO-Shift: Shifting the Distribution of Direct Preference Optimization | 2 | {
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2025-02-12T21:41:19.791000 | TransMLA: Multi-head Latent Attention Is All You Need | 9 | {
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2025-02-12T18:40:34.235000 | Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models | 2 | {
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2025-02-12T13:41:46.312000 | Pippo: High-Resolution Multi-View Humans from a Single Image | 2 | {
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2025-02-12T10:35:08.488000 | Hypencoder: Hypernetworks for Information Retrieval | 2 | {
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2025-02-12T09:56:56.287000 | Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving | 2 | {
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2025-02-12T09:54:39.924000 | Sparse Autoencoders for Scientifically Rigorous Interpretation of Vision Models | 1 | {
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2025-02-12T07:51:18.930000 | CoS: Chain-of-Shot Prompting for Long Video Understanding | 2 | {
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2025-02-12T07:18:08.463000 | Gemstones: A Model Suite for Multi-Faceted Scaling Laws | 2 | {
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2025-02-12T07:10:24.189000 | Retrieval-augmented Large Language Models for Financial Time Series Forecasting | 2 | {
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2025-02-12T06:55:30.432000 | Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More | 2 | {
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2025-02-12T04:53:50.325000 | Skill Expansion and Composition in Parameter Space | 3 | {
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popular in the development of autonomous agents, as it develops systems that
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2025-02-12T04:25:54.558000 | Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents | 3 | {
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2025-02-12T02:51:41.003000 | Expect the Unexpected: FailSafe Long Context QA for Finance | 4 | {
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human-interface interactions in LLM-based query-answer systems within finance.
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2025-02-12T01:31:44.368000 | FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks | 2 | {
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self-supervised pretraining on massive datasets. Inspired by this success,
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2025-02-11T23:55:37.671000 | Teaching Language Models to Critique via Reinforcement Learning | 2 | {
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crucial for building systems that can iteratively improve, yet it is
fundamentally limited by the ability to provide accurate judgments and
actionable suggestions. In this work, we study LLM critics for code generation
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2025-02-11T23:27:13.769000 | Magic 1-For-1: Generating One Minute Video Clips within One Minute | 4 | {
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video generation model with optimized memory consumption and inference latency.
The key idea is simple: factorize the text-to-video generation task into two
separate easier tasks for diffusion step distillation, namely text-to-image
generation ... | 33 | 67ac23186def89f9aae56b69 | null | null | |
2025-02-11T23:22:50.454000 | Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon | 3 | {
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these high scores may mask an overreliance on dataset-specific surface cues
rather than true language understanding. We introduce the Chameleon Benchmark
Overfit Detector (C-BOD), a meta-evaluation framework that systematically
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2025-02-11T23:21:13.452000 | VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation | 3 | {
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enabling control over one or two visual elements, such as camera trajectory or
object motion. However, these methods are unable to offer control over multiple
visual elements due to limitations in data and network efficacy. In this paper,
we introduc... | 13 | 67ac21b2aa680a0f8782d3bd | null | null | |
2025-02-11T23:16:28.213000 | CAD-Editor: A Locate-then-Infill Framework with Automated Training Data Synthesis for Text-Based CAD Editing | 2 | {
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Synthesis for Text-Based CAD Editing | Computer Aided Design (CAD) is indispensable across various industries.
Text-based CAD editing, which automates the modification of CAD models
based on textual instructions, holds great potential but remains underexplored.
Existing methods primarily focus on design variation generation or text-based
CAD generation, eit... | 9 | 67ac206314d5fe7767e7ec98 | null | null | |
2025-02-11T23:14:10.293000 | Enhance-A-Video: Better Generated Video for Free | 2 | {
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"statusLastChangedAt": "2... | 2025-02-11T12:22:35 | Enhance-A-Video: Better Generated Video for Free | DiT-based video generation has achieved remarkable results, but research into
enhancing existing models remains relatively unexplored. In this work, we
introduce a training-free approach to enhance the coherence and quality of
DiT-based generated videos, named Enhance-A-Video. The core idea is enhancing
the cross-frame... | 21 | 67ac200ea6b5a26040fc9709 | null | null | |
2025-02-11T23:11:49.993000 | Auditing Prompt Caching in Language Model APIs | 3 | {
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timing variations: cached prompts are processed faster than non-cached prompts.
These timing differences introduce the risk of side-channel timing attacks. For
example, if the cache is shared across users, an attacker could identify cached
prompts... | 4 | 67ac1f7851c7f3b53ffc4e1b | null | null | |
2025-02-11T23:10:26.895000 | NatureLM: Deciphering the Language of Nature for Scientific Discovery | 2 | {
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artificial intelligence, significantly enhancing how machines comprehend and
generate human languages. Inspired by the success of these foundation models,
researchers have developed foundation models for individual scientific domains,
including small... | 19 | 67ac1eabc61306b0ac95d346 | null | null | |
2025-02-11T23:04:08.153000 | Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training | 2 | {
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Models through Continual Pre-Training | Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous
agents typically rely on complex prompting or extensive fine-tuning, which
often fails to introduce new capabilities while preserving strong
generalizability. We introduce Hephaestus-Forge, the first large-scale
pre-training corpus designed t... | 18 | 67ac1d46e6f1e95ccf6de419 | null | null | |
2025-02-11T23:03:08.578000 | Scaling Pre-training to One Hundred Billion Data for Vision Language Models | 4 | {
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... | 2025-02-11T15:05:33 | Scaling Pre-training to One Hundred Billion Data for Vision Language
Models | We provide an empirical investigation of the potential of pre-training
vision-language models on an unprecedented scale: 100 billion examples. We find
that model performance tends to saturate at this scale on many common
Western-centric classification and retrieval benchmarks, such as COCO Captions.
Nevertheless, tasks... | 29 | 67ac1d6ac29356f92ed77354 | null | null | |
2025-02-11T23:00:20.080000 | CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction | 3 | {
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"user": ... | 2025-02-11T07:26:50 | CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction | Reasoning is a fundamental capability of Large Language Models. While prior
research predominantly focuses on enhancing narrow skills like math or code
generation, improving performance on many other reasoning tasks remains
challenging due to sparse and fragmented training data. To address this issue,
we propose CodeI/... | 46 | 67ac0ab820e98bddc5c1a039 | null | null | |
2025-02-11T22:58:37.585000 | LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! | 2 | {
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content, is what matters! | Large reasoning models (LRMs) tackle complex reasoning problems by following
long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking,
and self-validation. However, the training techniques and data requirements to
elicit Long CoT remain poorly understood. In this work, we find that a Large
Language m... | 36 | 67ac1c6536464325ebe3c723 | null | null |