Search: “reasoning”

Hear Both Sides: Efficient Multi-Agent Debate via Diversity-Aware Message Retention

arXiv:2603.20640v2 Announce Type: replace Abstract: Multi-Agent Debate has emerged as a promising framework for improving the reasoning quality of large language models through iterative inter-agent c

agentsarxiv-cs-cl

15 Apr 2026

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Model Releases

LaV-CoT: Language-Aware Visual CoT with Multi-Aspect Reward Optimization for Real-World Multilingual VQA

DGX agent

arXiv:2509.10026v4 Announce Type: replace Abstract: As large vision language models (VLMs) advance, their capabilities in multilingual visual question answering (mVQA) have significantly improved. Cha

model-releasesarxiv-cs-cv

15 Apr 2026

Modality-Native Routing in Agent-to-Agent Networks: A Multimodal A2A Protocol Extension

arXiv:2604.12213v1 Announce Type: new Abstract: Preserving multimodal signals across agent boundaries is necessary for accurate cross-modal reasoning, but it is not sufficient. We show that modality-n

15 Apr 2026

Visual Diffusion Models are Geometric Solvers

arXiv:2510.21697v2 Announce Type: replace Abstract: In this paper we show that visual diffusion models can serve as effective geometric solvers: they can directly reason about geometric problems by wo

researcharxiv-cs-cv

15 Apr 2026

Beyond Fluency: Toward Reliable Trajectories in Agentic IR

arXiv:2604.04269v2 Announce Type: replace Abstract: Information Retrieval is shifting from passive document ranking toward autonomous agentic workflows that operate in multi-step Reason-Act-Observe lo

Do LLMs Build Spatial World Models? Evidence from Grid-World Maze Tasks

arXiv:2604.10690v1 Announce Type: new Abstract: Foundation models have shown remarkable performance across diverse tasks, yet their ability to construct internal spatial world models for reasoning and

HyperGraphPro: Progress-Aware Reinforcement Learning for Structure-Guided Hypergraph RAG

arXiv:2601.17755v2 Announce Type: replace Abstract: Graph Retrieval-Augmented Generation (GraphRAG) has emerged as a promising paradigm that organizes external knowledge into structured graphs of enti

safetyarxiv-cs-cl

LLM-as-Judge on a Budget

arXiv:2602.15481v2 Announce Type: replace Abstract: LLM-as-a-judge has emerged as a cornerstone technique for evaluating large language models by leveraging LLM reasoning to score prompt-response pair

safetyarxiv-cs-lg

MEMENTO: Teaching LLMs to Manage Their Own Context

arXiv:2604.09852v1 Announce Type: new Abstract: Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state. We introduce MEMENTO:

NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment

arXiv:2604.11543v1 Announce Type: cross Abstract: Novelty is a core requirement in academic publishing and a central focus of peer review, yet the growing volume of submissions has placed increasing p

Rethinking Token-Level Credit Assignment in RLVR: A Polarity-Entropy Analysis

arXiv:2604.11056v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved the reasoning ability of Large Language Models (LLMs). However, its s

Tutorials

Specificity-aware reinforcement learning for fine-grained open-world classification

arXiv:2603.03197v3 Announce Type: replace Abstract: Classifying fine-grained visual concepts under open-world settings, i.e., without a predefined label set, demands models to be both accurate and spe

tutorialsarxiv-cs-cv

Test-time Scaling over Perception: Resolving the Grounding Paradox in Thinking with Images

arXiv:2604.11025v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) have begun to support Thinking with Images by invoking visual tools such as zooming and cropping during

researcharxiv-cs-cv

TS-Haystack: A Multi-Scale Retrieval Benchmark for Time Series Language Models

model-releasesarxiv-cs-lg

arXiv:2602.14200v4 Announce Type: replace Abstract: Time Series Language Models (TSLMs) are emerging as unified models for reasoning over continuous signals in natural language. However, long-context

VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG

arXiv:2604.05418v2 Announce Type: replace-cross Abstract: Scaling multimodal large language models (MLLMs) to long videos is constrained by limited context windows. While retrieval-augmented generatio

Bayesian Social Deduction with Graph-Informed Language Models

arXiv:2506.17788v2 Announce Type: replace Abstract: Social reasoning - inferring unobservable beliefs and intentions from partial observations of other agents - remains a challenging task for large la

Tutorials

Decomposing the Delta: What Do Models Actually Learn from Preference Pairs?

arXiv:2604.08723v1 Announce Type: cross Abstract: Preference optimization methods such as DPO and KTO are widely used for aligning language models, yet little is understood about what properties of pr

tutorialsarxiv-cs-ai

HM-Bench: A Comprehensive Benchmark for Multimodal Large Language Models in Hyperspectral Remote Sensing

arXiv:2604.08884v1 Announce Type: cross Abstract: While multimodal large language models (MLLMs) have made significant strides in natural image understanding, their ability to perceive and reason over

Learning Vision-Language-Action World Models for Autonomous Driving

arXiv:2604.09059v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have recently achieved notable progress in end-to-end autonomous driving by integrating perception, reasoning, and

Before We Trust Them: Decision-Making Failures in Navigation of Foundation Models

arXiv:2601.05529v5 Announce Type: replace Abstract: High success rates on navigation-related tasks do not necessarily translate into reliable decision making by foundation models. To examine this gap,

PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents

arXiv:2512.14735v2 Announce Type: replace-cross Abstract: This paper proposes PyFi, a novel framework for pyramid-like financial image understanding that enables vision language models (VLMs) to reaso

Riemann-Bench: A Benchmark for Moonshot Mathematics

arXiv:2604.06802v1 Announce Type: new Abstract: Recent AI systems have achieved gold-medal-level performance on the International Mathematical Olympiad, demonstrating remarkable proficiency at competi

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts

arXiv:2604.08541v1 Announce Type: cross Abstract: Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon t

safetyarxiv-cs-cl

The Detection-Extraction Gap: Models Know the Answer Before They Can Say It

arXiv:2604.06613v2 Announce Type: cross Abstract: Modern reasoning models continue generating long after the answer is already determined. Across five model configurations, two families, and three ben

The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment

arXiv:2604.06377v1 Announce Type: cross Abstract: We investigate whether post-trained capabilities can be transferred across models without retraining, with a focus on transfer across different model

TOOLCAD: Exploring Tool-Using Large Language Models in Text-to-CAD Generation with Reinforcement Learning

arXiv:2604.07960v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) is an expert-level task that relies on long-horizon reasoning and coherent modeling actions. Large Language Models (LLMs)

agentsarxiv-cs-cl

Applications

Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment

arXiv:2604.08212v1 Announce Type: new Abstract: General-purpose vision-language models demonstrate strong performance in everyday domains but struggle with specialized technical fields requiring preci

applicationsarxiv-cs-cv

Bias Control and Injection for Multi-Agent Aggregation

arXiv:2610.21290v1 Announce Type: new Abstract: Multi-agent reasoning with large language models (LLMs) raises a central question for collective decision-making: when agents disagree, whose decision s

IneqAgent: Agentic Inequality Proving in Lean with Symbolic Computation

arXiv:2610.20681v1 Announce Type: new Abstract: Formal inequality proving requires mathematical reasoning and exact computation, often connected through intermediate lemmas and goal reformulations. We

The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock

arXiv:2610.20665v1 Announce Type: new Abstract: Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time. In continuously changing environments, an

Thinking with Images as Continuous Actions: Numerical Visual Chain-of-Thought

arXiv:2602.23959v2 Announce Type: replace Abstract: Recent MLLMs increasingly rely on visual chain-of-thought to perform region-grounded reasoning over images. However, existing approaches ground regi

safetyarxiv-cs-cv

TRACER: A Multimodal Harness for Patient Care in Hospitals

arXiv:2610.21266v1 Announce Type: cross Abstract: Continuous video and clinical records provide complementary information for understanding how patient behavior evolves over a hospital stay. A central

Large Decision Models: Repurposing Large Language Models as Single-Pass Decision Makers

arXiv:2610.15501v1 Announce Type: new Abstract: Large language models make decisions through parallel prefill and sequential decoding. Although effective, this approach incurs decoding costs and does

Self-Evolving Visual Agent via Margin Reward

arXiv:2610.15084v1 Announce Type: new Abstract: Visual agents that interleave reasoning with tool invocation have posed a promising direction for multimodal tasks. However, learning effective tool inv

Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching

model-releasesarxiv-cs-cl

arXiv:2610.19636v1 Announce Type: cross Abstract: Large language models (LLMs) hold broad knowledge in one set of weights, but lack interpretability and trustworthy reasoning, especially in high-stake

Local Ai

What We Talk About When We Talk About LLM Planning: Evidence for Two Distinct Planning Abilities

arXiv:2607.11197v2 Announce Type: replace Abstract: When LLMs exhibit uneven performance across planning tasks, these gaps are often attributed to task difficulty. We argue that this explanation is in

local-aiarxiv-cs-ai

LIFT: Likelihood-Based Intervention Faithfulness Training

model-releasesarxiv-cs-lg

arXiv:2509.13334v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning improves LLM accuracy on complex tasks, but CoTs are often unfaithful; that is, the model does not rely on th

14 Oct 2026

OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding

model-releasesarxiv-cs-cv

arXiv:2610.18829v1 Announce Type: new Abstract: With the rapid development of multimodal large language models (MLLMs), evaluating their video understanding capabilities has become increasingly import

14 Oct 2026

Cancerous Backdoor Attack: Gradual Activation via Invisible Tokens and A Stage Controller

arXiv:2610.12644v1 Announce Type: cross Abstract: Backdoor Attacks on Large Language Models (LLMs) typically follow a trigger-response pattern, where satisfying a trigger condition activates a single

MENTOR: Learning from Failure with On-Policy Pedagogical Intervention

arXiv:2610.13594v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) is a key driver for Large Reasoning Models, but it is vulnerable to 'zero-reward collapse', a trai

Pretraining and Test-Time Training Budgets Matter for ARC: A Controlled Study of VARC and TRM

arXiv:2610.13660v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC) is increasingly used to evaluate whether neural networks can infer task-specific transformations from few d

SOAR: Source-Aware Reinforcement Learning with Precise Credit Assignment for Search Agents

arXiv:2610.13157v1 Announce Type: new Abstract: Reinforcement learning (RL) for search agents often suffers from evidence fabrication, a failure mode where agents bypass external information (E_{ext})

safetyarxiv-cs-cl

Test-Time Memory Construction

arXiv:2610.13348v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to answer questions over long interaction histories, where relevant evidence may be scattered across distant sessi

researcharxiv-cs-cl

Local Ai

Trajectory-Aware Policy Optimization: Closing the State Distribution Gap in Long-Horizon LLM RL

arXiv:2610.13020v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. Yet long-horizon autoregressive rollouts introduc

local-aiarxiv-cs-lg

When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors

model-releasesarxiv-cs-cl

arXiv:2610.13910v1 Announce Type: new Abstract: Vision-Language Models (VLMs) increasingly analyze and reason about images, yet it remains unclear whether their internal representations faithfully enc

NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime

arXiv:2610.10787v1 Announce Type: cross Abstract: Language models trained with long-horizon agentic reinforcement learning can generalize knowledge through reasoning, express precise actions, and purs

9 Oct 2026

Proof-of-Use: Mitigating Tool-Call Hacking in Deep Research Agents

arXiv:2510.10931v3 Announce Type: replace Abstract: While reinforcement learning (RL) enhances their ability to plan and reason across retrieval steps, we identify a critical failure mode in this sett

9 Oct 2026

TaReD: Tool-Aware Recursive Decomposition for Long-Horizon Tasks

arXiv:2610.11268v1 Announce Type: new Abstract: Agents combine reasoning with tools to interact with external systems and complete real-world tasks. Early agents typically interleave reasoning and act