Search: “reasoning”

Using Machine Mental Imagery for Representing Common Ground in Situated Dialogue

arXiv:2604.21144v1 Announce Type: cross Abstract: Situated dialogue requires speakers to maintain a reliable representation of shared context rather than reasoning only over isolated utterances. Curre

24 Apr 2026

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DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories

DGX agent

arXiv:2604.20443v1 Announce Type: cross Abstract: Large Language Models (LLMs) have been shown to possess Theory of Mind (ToM) abilities. However, it remains unclear whether this stems from robust rea

model-releasesarxiv-cs-ai

23 Apr 2026

Epistemic Constitutionalism Or: how to avoid coherence bias

arXiv:2601.14295v3 Announce Type: replace Abstract: Large language models increasingly function as artificial reasoners: they evaluate arguments, assign credibility, and express confidence. Yet their

Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback

arXiv:2604.20730v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown promising capabilities in generating Scalable Vector Graphics (SVG) via direct code synthesis. Howev

tutorialsarxiv-cs-cv

Task-Dependent Evaluation of LLM Output Homogenization: A Taxonomy-Guided Framework

arXiv:2509.21267v3 Announce Type: replace Abstract: Large language models often generate homogeneous outputs, but whether this is problematic depends on the specific task. For objective math tasks, re

researcharxiv-cs-cl

The Tool-Overuse Illusion: Why Does LLM Prefer External Tools over Internal Knowledge?

arXiv:2604.19749v1 Announce Type: new Abstract: Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon

Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models

arXiv:2604.18612v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, while recent prompting strategies such as Chain-of-Thou

BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search

arXiv:2601.11037v2 Announce Type: replace Abstract: RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances

MATA: Multi-Agent Framework for Reliable and Flexible Table Question Answering

arXiv:2602.09642v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have significantly improved table understanding tasks such as Table Question Answering (TableQ

agentsarxiv-cs-ai

MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling

model-releasesarxiv-cs-cl

arXiv:2511.11793v3 Announce Type: replace Abstract: We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike

VimRAG: Navigating Massive Visual Context in Retrieval-Augmented Generation via Multimodal Memory Graph

arXiv:2602.12735v2 Announce Type: replace-cross Abstract: Effectively retrieving, reasoning, and understanding multimodal information remains a critical challenge for agentic systems. Traditional Retr

DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking

model-releasesarxiv-cs-cv

arXiv:2507.20879v3 Announce Type: replace Abstract: The advent of Vision-Language Models (VLMs) has significantly advanced end-to-end autonomous driving, demonstrating powerful reasoning abilities for

DVAR: Adversarial Multi-Agent Debate for Video Authenticity Detection

arXiv:2604.16987v1 Announce Type: new Abstract: The rapid evolution of video generation technologies poses a significant challenge to media forensics, as conventional detection methods often fail to g

agentsarxiv-cs-cv

Learning to Correct: Calibrated Reinforcement Learning for Multi-Attempt Chain-of-Thought

arXiv:2604.17912v1 Announce Type: new Abstract: State-of-the-art reasoning models utilize long chain-of-thought (CoT) to solve increasingly complex problems using more test-time computation. In this w

researcharxiv-cs-lg

MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation

arXiv:2512.20626v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to dynamically access external information, which is powerful for an

researcharxiv-cs-cl

MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization

arXiv:2602.11182v2 Announce Type: replace Abstract: Existing memory systems enable Large Language Models (LLMs) to support long-horizon human-LLM interactions by persisting historical interactions bey

tutorialsarxiv-cs-cl

SaFeR-Steer: Evolving Multi-Turn MLLMs via Synthetic Bootstrapping and Feedback Dynamics

arXiv:2604.16358v1 Announce Type: cross Abstract: MLLMs are increasingly deployed in multi-turn settings, where attackers can escalate unsafe intent through the evolving visual-text history and exploi

VCORE: Variance-Controlled Optimization-based Reweighting for Chain-of-Thought Supervision

model-releasesarxiv-cs-cl

arXiv:2510.27462v2 Announce Type: replace Abstract: Supervised fine-tuning (SFT) on long chain-of-thought (CoT) trajectories has emerged as a crucial technique for enhancing the reasoning abilities of

Waking Up Blind: Cold-Start Optimization of Supervision-Free Agentic Trajectories for Grounded Visual Perception

arXiv:2604.17475v1 Announce Type: cross Abstract: Small Vision-Language Models (SVLMs) are efficient task controllers but often suffer from visual brittleness and poor tool orchestration. They typical

Fragile Thoughts: How Large Language Models Handle Chain-of-Thought Perturbations

arXiv:2603.03332v3 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the ro

20 Apr 2026

Does RL Expand the Capability Boundary of LLM Agents? A PASS@(k,T) Analysis

arXiv:2604.14877v1 Announce Type: new Abstract: Does reinforcement learning genuinely expand what LLM agents can do, or merely make them more reliable? For static reasoning, recent work answers the se

agentsarxiv-cs-lg

DySCO: Dynamic Attention-Scaling Decoding for Long-Context Language Models

arXiv:2602.22175v2 Announce Type: replace Abstract: Understanding and reasoning over long contexts is a crucial capability for language models (LMs). Although recent models support increasingly long c

researcharxiv-cs-cl

EchoAgent: Towards Reliable Echocardiography Interpretation with 'Eyes','Hands' and 'Minds'

arXiv:2604.05541v2 Announce Type: replace Abstract: Reliable interpretation of echocardiography (Echo) is crucial for assessing cardiac function, which demands clinicians to synchronously orchestrate

agentsarxiv-cs-cv

Hardware

Model Capability Dominates: Inference-Time Optimization Lessons from AIMO 3

arXiv:2603.27844v2 Announce Type: replace Abstract: Majority voting over multiple LLM attempts improves mathematical reasoning, but correlated errors limit the effective sample size. A natural fix is

hardwarearxiv-cs-cl

SPaCe: Unlocking Sample-Efficient Large Language Models Training With Self-Pace Curriculum Learning

arXiv:2508.05015v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL). However, such methods requir

researcharxiv-cs-lg

SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments

model-releasesarxiv-cs-cl

arXiv:2604.14144v1 Announce Type: cross Abstract: Spatial reasoning over three-dimensional scenes is a core capability for embodied intelligence, yet continuous model improvement remains bottlenecked

Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets?

model-releasesarxiv-cs-cv

arXiv:2511.17792v2 Announce Type: replace Abstract: While recent video world models can generate highly realistic videos, their ability to perform semantic reasoning and planning remains unclear and u

Local Ai

Unleashing Implicit Rewards: Prefix-Value Learning for Distribution-Level Optimization

arXiv:2604.13197v1 Announce Type: new Abstract: Process reward models (PRMs) provide fine-grained reward signals along the reasoning process, but training reliable PRMs often requires step annotations

local-aiarxiv-cs-cl

Why MLLMs Struggle to Determine Object Orientations

arXiv:2604.13321v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) struggle with tasks that require reasoning about 2D object orientation in images, as documented in prior work.

safetyarxiv-cs-cv

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

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

model-releasesarxiv-cs-cv

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

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

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

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

agentsarxiv-cs-ai

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

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

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

agentsarxiv-cs-ai

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