Search: “reasoning”

FALAT: Tracing Failures in LLM Agent Trajectories via Dependency-Guided Search

arXiv:2606.00765v1 Announce Type: new Abstract: LLM-based agents increasingly solve complex tasks through long trajectories involving reasoning steps, tool calls, and inter-agent communication. Howeve

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HarnessForge: Joint Harness and Policy Evolution for Adaptive Agent Systems

DGX agent

arXiv:2606.01779v1 Announce Type: new Abstract: LLM agents are increasingly expected to operate across heterogeneous task regimes that require distinct execution paradigms. This challenges fixed agent

safetyarxiv-cs-cl

2 Jun 2026

InsightVQA: High-Dimensional Emotion-Cognitive Visual Question Answering Benchmark

model-releasesarxiv-cs-cv

arXiv:2606.02171v1 Announce Type: new Abstract: Visual emotion understanding requires models not only to recognize emotional states, but also to why they arise and perform higher-level cognitive reaso

Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning

arXiv:2606.01301v1 Announce Type: new Abstract: Hallucinations in medical large language models (LLMs) pose serious risks for clinical decision support, particularly when models must reason over compl

Mitigating Perceptual Judgment Bias in Multimodal LLM-as-a-Judge via Perceptual Perturbation and Reward Modeling

arXiv:2606.02578v1 Announce Type: cross Abstract: Recent multimodal large language models have demonstrated strong reasoning ability, yet their reliability as automated evaluators remains limited by a

OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents

arXiv:2606.02031v1 Announce Type: cross Abstract: Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despit

PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency

arXiv:2602.16745v2 Announce Type: replace-cross Abstract: Test-time scaling can improve model performance by aggregating stochastic reasoning trajectories. However, achieving sample-efficient test-tim

Prototype Transformer: Towards Language Model Architectures Interpretable by Design

arXiv:2602.11852v2 Announce Type: replace Abstract: While state-of-the-art language models (LMs) surpass most humans in certain domains, their reasoning remains largely opaque, reducing trust and incr

SDR: Set-Distance Rewards for Radiology Report Generation

arXiv:2606.00440v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has rapidly advanced reasoning in vision--language models. However, for chest X-ray report generation, th

SentGuard: Sentence-Level Streaming Guardrails for Large Language Models

arXiv:2606.02041v1 Announce Type: new Abstract: Large language models increasingly stream long, reasoning-intensive responses in real time, making when to moderate as critical as whether to moderate.

Temporal Evidence Routing with Structured Visual Evidence for TimeLogicQA

arXiv:2606.01106v1 Announce Type: new Abstract: TimeLogicQA evaluates whether video question answering systems can reason over temporal relations such as event existence, ordering, persistence, bounda

researcharxiv-cs-cv

Through the PRISM: Principle-Aware, Interpretable, and Multi-Scale Evaluation of Visual Designs

model-releasesarxiv-cs-cv

arXiv:2606.00592v1 Announce Type: new Abstract: Effective visual communication stems from the harmony of multiple design principles, such as readability, contrast, alignment, overlap, and coherence, w

Biases in the Blind Spot: Detecting What LLMs Fail to Mention

arXiv:2602.10117v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often provide chain-of-thought (CoT) reasoning traces that appear plausible, but may hide internal biases. We cal

Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies

arXiv:2512.19673v3 Announce Type: replace-cross Abstract: Existing reinforcement learning (RL) approaches treat large language models (LLMs) as a unified policy, overlooking their internal mechanisms.

Can LLM Teams Play What? Where? When?

arXiv:2605.30459v1 Announce Type: new Abstract: Large language models (LLMs) remain limited on tasks requiring indirect reasoning, cultural knowledge, and coordinated hypothesis testing. We investigat

Distilling LLM Feedback for Lean Theorem Proving

arXiv:2605.30861v1 Announce Type: new Abstract: Post-training for reasoning models typically combines supervised fine-tuning with reinforcement learning from verifiable rewards, most commonly with GRP

ElasticMem: Latent Memory as a Learnable Resource for LLM Agents

arXiv:2605.30690v1 Announce Type: new Abstract: Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. Howeve

Empirical Characterization of Inference-Time Elicited Probability Transformations in Large Language Models

arXiv:2603.19262v2 Announce Type: replace-cross Abstract: Large language models increasingly rely on inference-time procedures such as chain-of-thought reasoning, self-refinement, retrieval augmentati

ExpGraph: Model-Agnostic Experience Learning with Graph-Structured Memory for LLM Agents

arXiv:2605.30712v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scr

Fighting Numerical Hallucinations via Data-centric Compilation for Online Financial QA

arXiv:2605.31064v1 Announce Type: cross Abstract: Large Language Models (LLMs) have significantly advanced online data services, particularly in the domain of financial question answering (FinQA). How

agentsarxiv-cs-ai

ForecastCompass: Guiding Agentic Forecasting with Adaptive Factor Memory

model-releasesarxiv-cs-lg

arXiv:2605.30858v1 Announce Type: new Abstract: Agentic forecasting is important for decision-making in dynamic environments, but it remains challenging because agents must reason from incomplete, tim

IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving

arXiv:2605.31476v1 Announce Type: new Abstract: End-to-end autonomous driving has emerged as a compelling paradigm for learning planning directly from sensor observations, while recent world-model-bas

agentsarxiv-cs-ro

MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft

arXiv:2605.30931v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to susta

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection

arXiv:2605.31238v1 Announce Type: new Abstract: Endowing large language models with compositional reasoning over specialized documents requires multi-hop training data at scale, where such data rarely

SPM-Bench: Benchmarking Large Language Models for Scanning Probe Microscopy

arXiv:2602.22971v2 Announce Type: replace Abstract: As LLMs achieved breakthroughs in general reasoning, their proficiency in specialized scientific domains reveals pronounced gaps in existing benchma

Your Teacher Can't Help You Here: Combating Supervision Fidelity Decay in On-Policy Distillation

arXiv:2605.30833v1 Announce Type: cross Abstract: On-policy distillation transfers reasoning capabilities by training a student model on its own generated trajectories using token-level feedback from

AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection

arXiv:2605.30140v1 Announce Type: new Abstract: Benefiting from generalizability of vision-language models (VLMs) such as CLIP, many zero-/few-shot anomaly detection (AD) approaches have achieved impr

agentsarxiv-cs-cv

Causal-JEPA: Learning World Models through Object-Level Latent Masking

arXiv:2602.11389v2 Announce Type: replace Abstract: World models require robust relational understanding to support prediction, reasoning, and control. While object-centric representations provide a u

DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding

arXiv:2605.29879v1 Announce Type: new Abstract: Integrating open-vocabulary semantic information into dynamic 3D scene representations is essential for long-term embodied scene understanding. However,

agentsarxiv-cs-cv

Diagnosing Harmful Continuation in Answer-Correct Long-CoT Training Traces

arXiv:2605.29288v1 Announce Type: new Abstract: Long chain-of-thought (CoT) traces are widely used as supervision for reasoning-oriented LLM SFT, yet answer-correct traces can still lead to markedly d

Local Ai

DynaGraph: Lightweight Multi-Model Interaction Framework via Dynamic Topological Reconfiguration

arXiv:2605.29511v1 Announce Type: cross Abstract: Tackling complex reasoning tasks typically relies on massive monolithic LLMs, which suffer from severe computational redundancy. While task decomposit

local-aiarxiv-cs-cl

Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models

model-releasesarxiv-cs-cv

arXiv:2605.29074v1 Announce Type: new Abstract: Are current Vision Language Models (VLMs) ready to comprehend and reason about complex embodied interactions in 3D environments? We introduce Embodied3D

ESPO: Early-Stopping Proximal Policy Optimization

arXiv:2605.29860v1 Announce Type: cross Abstract: When a large language model under reinforcement learning commits a wrong reasoning step early in a trajectory, standard algorithms force it to keep ge

Getting to the Point: Pointing Improves LVLMs at Counting

arXiv:2603.21746v2 Announce Type: replace Abstract: Pointing-based methods decompose complex tasks as sequential grounding and reasoning steps. Given a query, the model first grounds the relevant obje

researcharxiv-cs-cv

Inferring Code Correctness from Specification

arXiv:2605.29822v1 Announce Type: cross Abstract: Large language models (LLMs) have become integral to modern software development, enabling automated code generation at scale. However, validating the

LaRA: Layer-wise Representation Analysis for Detecting Data Contamination in RL Post-Training

arXiv:2605.29888v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training has shown to improve reasoning in large language models (LLMs). However, there has been little exploration o

MechELK: A Mechanistic Interpretability Framework for Eliciting Latent Knowledge in Large Language Models

arXiv:2605.28825v1 Announce Type: new Abstract: Large language models (LLMs) frequently encode factual and reasoning knowledge in their internal representations that is not faithfully reflected in the

OISD: On-Policy Internal Self-Distillation of Language Models

arXiv:2605.29089v1 Announce Type: cross Abstract: Recent reinforcement learning (RL) post-training approaches primarily optimize the final output policy using sparse outcome-level rewards, while large

SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search

arXiv:2605.29796v1 Announce Type: new Abstract: Agentic search enables LLMs to solve complex multi-hop questions through iterative reasoning and external search. Despite the effectiveness, these syste

Small Agent Group is the Future of Digital Health

arXiv:2602.08013v2 Announce Type: replace Abstract: The rapid adoption of large language models (LLMs) in digital health has been driven by a 'scaling-first' philosophy, i.e., the assumption that clin

Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text

arXiv:2605.29076v1 Announce Type: cross Abstract: LLMs have advanced text classification, yet existing paradigms face a trade-off: supervised (label only) fine-tuning is scalable but offers limited re

SURGENT: A Surgical Multi-Agent Assistance System Across the Perioperative Workflow

arXiv:2605.29368v1 Announce Type: cross Abstract: The intricate nature of modern surgical care necessitates intelligent systems that can synthesize extensive patient records, support collaborative dec

Token Inflation: How Dishonest Providers Can Overcharge for Large Language Model Usage

arXiv:2605.30040v1 Announce Type: cross Abstract: Per-token billing is now the standard pricing model for commercial large language models (LLMs), so the honesty of reported token counts directly affe

Local Ai

Unifying Temporal and Structural Credit Assignment in LLM-Based Multi-Agent Prompt Optimization

arXiv:2605.30227v1 Announce Type: cross Abstract: While Multi-Agent Systems (MAS) empower Large Language Models to tackle complex reasoning tasks through collaborative interaction, optimizing their dy

local-aiarxiv-cs-ai

Xetrieval: Mechanistically Explaining Dense Retrieval

arXiv:2605.29507v1 Announce Type: new Abstract: Explaining why dense retrievers assign high relevance scores remains challenging because retrieval decisions are made through opaque high-dimensional em

ADWIN: Adaptive Windows for Horizon-Aware On-Policy Distillation

arXiv:2605.28396v1 Announce Type: cross Abstract: On-policy distillation (OPD) transfers reasoning behavior by training a student on teacher feedback along student-generated trajectories, but standard

28 May 2026

Beyond Binary Moral Judgment: Modeling Ethical Pluralism in AI

arXiv:2605.28707v1 Announce Type: new Abstract: Critical decision-making in socially consequential spaces is increasingly involving AI systems at varying capacities. Yet, despite the ubiquity of auton

28 May 2026

CPPO: Contrastive Perception Policy Optimization for VLM Agents