Search: “reasoning”

LASER: Loss-Aware Singular-value Decomposition and Rank Allocation for Efficient Low-Precision Vision-Language Models

model-releasesarxiv-cs-lg

arXiv:2606.00573v1 Announce Type: new Abstract: Vision-language models (VLMs) deliver strong multimodal reasoning capabilities, but their large computational cost and high parameter counts make deploy

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Model Releases

MASER: Modality-Adaptive Specialist Routing for Embodied 3D Spatial Intelligence

DGX agent

arXiv:2606.02463v1 Announce Type: cross Abstract: In 3D environments, Embodied Agents answer spatially relevant questions through reasoning from a mixture of modalities including natural language, RGB

model-releasesarxiv-cs-ai

2 Jun 2026

Moment-Video: Diagnosing Temporal Fidelity of Video MLLMs on Momentary Visual Events

arXiv:2606.02522v1 Announce Type: cross Abstract: Video multimodal large language models (MLLMs) have made rapid progress on general and long-form video understanding, yet their ability to preserve br

Optimal Bayesian Stopping for Efficient Inference of Consistent LLM Answers

arXiv:2602.05395v2 Announce Type: replace-cross Abstract: A simple strategy for improving LLM accuracy, especially in math and reasoning problems, is to sample multiple responses and submit the answer

Applications

PaCo-VLA: Passivity-Shielded Compliance Prior for Contact-Rich Vision-Language-Action Manipulation

arXiv:2606.00515v1 Announce Type: cross Abstract: Contact-rich manipulation demands both high-level semantic reasoning and the safe regulation of high-frequency contact dynamics. While Vision-Language

applicationsarxiv-cs-ai

PaperVoyager : Building Interactive Web with Visual Language Models

arXiv:2603.22999v3 Announce Type: replace Abstract: Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding. However, exist

POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems

arXiv:2606.02282v1 Announce Type: new Abstract: Orchestrating Large Language Models into Multi-Agent Systems (LLM-MAS) has unlocked remarkable reasoning capabilities, yet emergent failures and halluci

Post-Deterministic Distributed Systems: A New Foundation for Trustworthy Autonomous Infrastructure

arXiv:2606.01722v1 Announce Type: cross Abstract: For decades, distributed systems have typically assumed that correct participants execute protocol-specified behavior with stable, externally defined,

REBot: From RAG to CatRAG with Semantic Enrichment and Graph Routing

arXiv:2510.01800v3 Announce Type: replace Abstract: Academic regulation advising is essential for helping students interpret and comply with institutional policies, yet building effective systems requ

SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents

arXiv:2602.12984v2 Announce Type: replace Abstract: Scientific reasoning inherently demands integrating sophisticated toolkits to navigate domain-specific knowledge. Yet, current benchmarks largely ov

agentsarxiv-cs-cl

Symbolic Neural Generation with Applications to Lead Discovery in Drug Design

arXiv:2510.23379v2 Announce Type: replace-cross Abstract: We investigate a relatively under-explored class of hybrid neurosymbolic models that integrate symbolic learning with neural reasoning to cons

TVIR: Building Deep Research Agents Towards Text--Visual Interleaved Report Generation

arXiv:2606.02320v1 Announce Type: new Abstract: Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form report generation, but existing benchmar

Unified Context Evolution for LLM Agents

arXiv:2606.02304v1 Announce Type: new Abstract: LLM-based agents can solve multi-step interactive tasks by combining reasoning with environment feedback, yet each episode starts from the same fixed co

agentsarxiv-cs-cl

Before Parc Ferme: RL-Time Pruning for Efficient Embodied LLMs in Autonomous Driving

arXiv:2605.31256v1 Announce Type: new Abstract: Embodied Large Language Models (LLMs) are increasingly used as reasoning modules in robotic control pipelines to improve human-robot interaction, but th

agentsarxiv-cs-ro

COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents

arXiv:2605.30838v1 Announce Type: new Abstract: LLM-powered search agents enable multi-step reasoning and tool use. However, these capabilities introduce retrieval-induced safety degradation, as harmf

Decoding the Surgical Scene: A Scoping Review of Scene Graphs in Surgery

arXiv:2509.20941v2 Announce Type: replace Abstract: As surgical AI transitions from pixel-level detection to complex reasoning, Scene Graphs (SGs) offer the structured, relational representations nece

safetyarxiv-cs-cv

Depth-Dependent Indirect Prompt Injection in Tool-Calling ReAct Agents: Injection Depth, Payload Framing, and Turn-Budget Sensitivity

arXiv:2605.30686v1 Announce Type: cross Abstract: ReAct agents that interleave chain-of-thought reasoning with tool calls are increasingly deployed for real tasks such as scheduling, file retrieval, a

Diversity Matters: Revisiting Test-Time Compute in Vision-Language Models

arXiv:2605.30713v1 Announce Type: cross Abstract: Test-time compute (TTC) strategies have emerged as a lightweight approach to boost reasoning in large language models (LLMs). However, their applicati

researcharxiv-cs-cv

EchoRL: Reinforcement Learning via Rollout Echoing

arXiv:2605.31228v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards is an effective route for post-training to strengthen the reasoning capability of large language models

HypoAgent: An Agentic Framework for Interactive Abductive Hypothesis Generation over Knowledge Graphs

arXiv:2605.31370v1 Announce Type: new Abstract: Abductive reasoning over knowledge graphs aims to generate logical hypotheses that explain observed entities or facts. Existing controllable hypothesis

Mellum2 Technical Report

arXiv:2605.31268v1 Announce Type: new Abstract: We present Mellum 2, an open-weight 12B-parameter Mixture-of-Experts (MoE) language model with 2.5B active parameters per token. Mellum 2 is a general-p

Applications

Positional versus Symbolic Attention Heads: Learning Dynamics, RoPE Geometry, and Length Generalization

arXiv:2605.31558v1 Announce Type: cross Abstract: Transformer-based language models are widespread in today's society. As such, understanding the mechanisms by which they solve structured tasks and pr

applicationsarxiv-cs-ai

Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approach

arXiv:2511.04393v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as 'agents' for decision-making (DM) in interactive and dynamic environments. Yet, since they

Thinking in Structures: Evaluating Spatial Intelligence in Constraint-Governed Spaces

model-releasesarxiv-cs-cv

arXiv:2602.07864v2 Announce Type: replace Abstract: Spatial intelligence is crucial for vision--language models (VLMs), yet many scene-centric benchmarks evaluate unconstrained environments where a si

4DPC^2hat: Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping

arXiv:2602.03890v2 Announce Type: replace Abstract: Point clouds provide a compact and expressive representation of 3D objects, and have recently been integrated into multimodal large language models

researcharxiv-cs-cv

AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

arXiv:2602.23258v2 Announce Type: replace Abstract: While Multi-Agent Systems (MAS) excel in complex reasoning, they suffer from the cascading impact of erroneous information from individual agents. C

Audio Jailbreaks in Large Audio-Language Models: Taxonomy, Attack-Defense Analysis, and Cost-Aware Evaluation

arXiv:2605.30031v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) expand jailbreak risks from token-level prompting to the full speech perception-to-reasoning pipeline, where unsaf

Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting

arXiv:2509.23571v3 Announce Type: replace-cross Abstract: As cyber threats continue to grow in scale and sophistication, blue team defenders increasingly require advanced tools to proactively detect a

Better Later Than Sooner: Neuro-Symbolic Knowledge Graph Construction via Ontology-grounded Post-extraction Correction

arXiv:2605.29168v1 Announce Type: new Abstract: Question answering (QA) is a core challenge in AI, particularly for complex queries requiring multi-hop reasoning across documents, or symbolic operatio

Beyond Normalization: Rethinking the Partition Function as a Difficulty Scheduler for RLVR

arXiv:2602.12642v2 Announce Type: replace-cross Abstract: Reward-maximizing RL methods have shown to be capable of enhancing the reasoning performance of LLMs, but often lead to reduced generation div

Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models

arXiv:2605.28866v1 Announce Type: cross Abstract: Token-based time series large language models (TS-LLMs) have emerged as a promising direction for time series analysis and reasoning. However, prior s

Do Language Models Track Entities Across State Changes?

arXiv:2605.30233v1 Announce Type: cross Abstract: Entity tracking (ET), the ability to keep track of states, is a fundamental skill that underlies complex reasoning. An increasing amount of work inves

Do not be greedy, Think Twice: Sampling and Selection for Document-level Information Extraction

arXiv:2601.18395v2 Announce Type: replace Abstract: Document-level Information Extraction (DocIE) aims to produce an output template with the entities, relations, and events of interest occurring in t

researcharxiv-cs-cl

E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing

arXiv:2512.03109v2 Announce Type: replace-cross Abstract: Agentic AI systems execute a sequence of actions, such as reasoning steps or tool calls, in response to a user prompt. To evaluate the success

Enhancing Multi-Agent Communication through Attention Steering with Context Relevance

arXiv:2605.30136v1 Announce Type: new Abstract: LLM-based multi-agent systems have demonstrated remarkable performance on complex tasks through collaborative reasoning. However, these systems tend to

EvA: An Evidence-First Audio Understanding Paradigm for LALMs

arXiv:2603.27667v2 Announce Type: replace-cross Abstract: Large Audio Language Models (LALMs) still struggle in complex acoustic scenes because they often fail to preserve task-relevant acoustic evide

EvoMD-LLM: Learning the Language of Species Evolution in Reactive Molecular Dynamics

arXiv:2605.29394v1 Announce Type: new Abstract: While large language models (LLMs) excel at static scientific reasoning, they struggle to model the temporal structure of dynamic physical processes. We

Governing Technical Debt in Agentic AI Systems

arXiv:2605.29129v1 Announce Type: new Abstract: Agentic AI systems are increasingly being explored as production infrastructure: they reason over multiple steps, call tools, act through workflows, and

GrepSeek: Training Search Agents for Direct Corpus Interaction

arXiv:2605.29307v1 Announce Type: cross Abstract: Large Language Model (LLM) search agents have shown strong promise for knowledge-intensive language tasks through multiple rounds of reasoning and inf

Guidance Contrastive Token Credit Assignment for Discrete Policy Optimization

arXiv:2605.29198v1 Announce Type: new Abstract: Group-advantage-based reinforcement learning methods, such as GRPO and DAPO, have demonstrated strong performance across diverse domains, including math

safetyarxiv-cs-cv

HEART-Bench: Do LLM Agents Exhibit Human-like Psychology?

arXiv:2605.30058v1 Announce Type: new Abstract: While LLM agents have demonstrated remarkable task-oriented abilities such as planning, reasoning, and action, few works have treated them as complete h

Micro-Macro Retrieval: Reducing Long-Form Hallucination in Large Language Models

arXiv:2605.28828v1 Announce Type: cross Abstract: Large Language Models (LLMs) achieve impressive performance across many tasks but remain prone to hallucination, especially in long-form generation wh

Mixing Mechanisms: How Language Models Retrieve Bound Entities In-Context

arXiv:2510.06182v2 Announce Type: replace Abstract: A key component of in-context reasoning is the ability of language models (LMs) to bind entities for later retrieval. For example, an LM might repre

researcharxiv-cs-cl

PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data

arXiv:2508.15180v3 Announce Type: replace Abstract: High-quality mathematical and logical datasets with verifiable answers are essential for strengthening the reasoning capabilities of large language

Redundant or Necessary? A Benchmark for Detecting Redundant Steps in Agent Trajectories

arXiv:2605.29893v1 Announce Type: new Abstract: LLM-based agents have demonstrated strong capabilities in solving complex tasks through multi-step reasoning and tool use. However, existing evaluation

SCoOP: Semantic Consistent Opinion Pooling for Uncertainty Quantification in Multiple Vision-Language Model Systems

arXiv:2603.23853v3 Announce Type: replace Abstract: Combining multiple Vision-Language Models (VLMs) can enhance multimodal reasoning and robustness, but aggregating heterogeneous models' outputs ampl

Skill-Pro: Learning Reusable Skills from Experience via Non-Parametric PPO for LLM Agents

arXiv:2602.01869v3 Announce Type: replace Abstract: LLM-driven agents excel at sequential decision-making but often rely on on-the-fly reasoning, re-deriving solutions even in recurring scenarios. Thi

Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies

arXiv:2605.29712v1 Announce Type: cross Abstract: Grounded claim factuality checking is important for large language model (LLM) applications such as retrieval-augmented generation, as it helps users