Search: “reasoning”

BoxTuning: Directly Injecting the Object Box for Multimodal Model Fine-Tuning

arXiv:2604.11136v1 Announce Type: cross Abstract: Object-level spatial-temporal understanding is essential for video question answering, yet existing multimodal large language models (MLLMs) encode fr

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Research

Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning

DGX agent

arXiv:2604.10701v1 Announce Type: cross Abstract: Credit assignment is a central challenge in reinforcement learning (RL). Classical actor-critic methods address this challenge through fine-grained ad

researcharxiv-cs-ai

14 Apr 2026

Can Large Language Models Infer Causal Relationships from Real-World Text?

arXiv:2505.18931v4 Announce Type: replace Abstract: Understanding and inferring causal relationships from texts is a core aspect of human cognition and is essential for advancing large language models

Tutorials

Controlling Multimodal Conversational Agents with Coverage-Enhanced Latent Actions

arXiv:2601.07516v2 Announce Type: replace-cross Abstract: Vision-language models are increasingly employed as multimodal conversational agents (MCAs) for diverse conversational tasks. Recently, reinfo

tutorialsarxiv-cs-ai

DecepGPT: Schema-Driven Deception Detection with Multicultural Datasets and Robust Multimodal Learning

arXiv:2603.23916v2 Announce Type: replace-cross Abstract: Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security. In these high-sta

Detecting Corporate AI-Washing via Cross-Modal Semantic Inconsistency Learning

arXiv:2604.09644v1 Announce Type: cross Abstract: Corporate AI-washing-the strategic misrepresentation of AI capabilities via exaggerated or fabricated cross-channel disclosures-has emerged as a syste

Diffusion-CAM: Faithful Visual Explanations for dMLLMs

arXiv:2604.11005v1 Announce Type: new Abstract: While diffusion Multimodal Large Language Models (dMLLMs) have recently achieved remarkable strides in multimodal generation, the development of interpr

Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations

arXiv:2604.11322v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated impressive capabilities in utilizing external tools. In practice, however, LLMs are often exposed to to

Doc-PP: Document Policy Preservation Benchmark for Large Vision-Language Models

arXiv:2601.03926v2 Announce Type: replace Abstract: The deployment of Large Vision-Language Models (LVLMs) for real-world document question answering is often constrained by dynamic, user-defined poli

dTRPO: Trajectory Reduction in Policy Optimization of Diffusion Large Language Models

arXiv:2603.18806v2 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) introduce a new paradigm for language generation, which in turn presents new challenges for aligning them wi

Agents

El Agente Estructural: An Artificially Intelligent Molecular Editor

arXiv:2602.04849v2 Announce Type: replace-cross Abstract: We present El Agente Estructural, a multimodal, natural-language-driven geometry-generation and manipulation agent for autonomous chemistry an

agentsarxiv-cs-ai

FinTrace: Holistic Trajectory-Level Evaluation of LLM Tool Calling for Long-Horizon Financial Tasks

arXiv:2604.10015v1 Announce Type: new Abstract: Recent studies demonstrate that tool-calling capability enables large language models (LLMs) to interact with external environments for long-horizon fin

GenProve: Learning to Generate Text with Fine-Grained Provenance

arXiv:2601.04932v2 Announce Type: replace Abstract: Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as

safetyarxiv-cs-cl

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs

arXiv:2511.11653v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasonin

Agents

Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering

arXiv:2604.11430v1 Announce Type: cross Abstract: AI agents that pay for resources via the x402 protocol embed payment metadata - resource URLs, descriptions, and reason strings - in every HTTP paymen

agentsarxiv-cs-ai

How LLMs Might Think

arXiv:2604.09674v1 Announce Type: new Abstract: Do large language models (LLMs) think? Daniel Stoljar and Zhihe Vincent Zhang have recently developed an argument from rationality for the claim that LL

Human-like Working Memory Interference in Large Language Models

arXiv:2604.09670v1 Announce Type: cross Abstract: Intelligent systems must maintain and manipulate task-relevant information online to adapt to dynamic environments and changing goals. This capacity,

Infusing Theory of Mind into Socially Intelligent LLM Agents

arXiv:2509.22887v2 Announce Type: replace Abstract: Theory of Mind (ToM)-an understanding of the mental states of others-is a key aspect of human social intelligence, yet, chatbots and LLM-based socia

KCS: Diversify Multi-hop Question Generation with Knowledge Composition Sampling

arXiv:2508.20567v2 Announce Type: replace Abstract: Multi-hop question answering faces substantial challenges due to data sparsity, which increases the likelihood of language models learning spurious

Large Language Model as An Operator: An Experience-Driven Solution for Distribution Network Voltage Control

arXiv:2507.14800v2 Announce Type: replace-cross Abstract: With the advanced reasoning, contextual understanding, and information synthesis capabilities of large language models (LLMs), a novel paradig

Tutorials

Learning and Enforcing Context-Sensitive Control for LLMs

arXiv:2604.10667v1 Announce Type: cross Abstract: Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limit

tutorialsarxiv-cs-ai

LLMs for Text-Based Exploration and Navigation Under Partial Observability

arXiv:2604.09604v1 Announce Type: new Abstract: Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language m

Agents

MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens

arXiv:2603.23516v2 Announce Type: replace-cross Abstract: Long-term memory is a cornerstone of human intelligence. Enabling AI to process lifetime-scale information remains a long-standing pursuit in

agentsarxiv-cs-ai

Nano-EmoX: Unifying Multimodal Emotional Intelligence from Perception to Empathy

arXiv:2603.02123v3 Announce Type: replace Abstract: The development of affective multimodal language models (MLMs) has long been constrained by a gap between low-level perception and high-level intera

Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding

model-releasesarxiv-cs-cv

arXiv:2604.11415v1 Announce Type: new Abstract: Remote sensing understanding inherently requires multi-resolution observation, since different targets and application tasks demand different levels of

Persona Non Grata: Single-Method Safety Evaluation Is Incomplete for Persona-Imbued LLMs

arXiv:2604.11120v1 Announce Type: new Abstract: Personality imbuing customizes LLM behavior, but safety evaluations almost always study prompt-based personas alone. We show this is incomplete: prompti

Pioneer Agent: Continual Improvement of Small Language Models in Production

arXiv:2604.09791v1 Announce Type: new Abstract: Small language models are attractive for production deployment due to their low cost, fast inference, and ease of specialization. However, adapting them

Preference Learning Unlocks LLMs' Psycho-Counseling Skills

arXiv:2502.19731v2 Announce Type: replace Abstract: Applying large language models (LLMs) to assist in psycho-counseling is an emerging and meaningful approach, driven by the significant gap between p

PRISM Risk Signal Framework: Hierarchy-Based Red Lines for AI Behavioral Risk

arXiv:2604.11070v1 Announce Type: new Abstract: Current approaches to AI safety define red lines at the case level: specific prompts, specific outputs, specific harms. This paper argues that red lines

Relax: An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale

arXiv:2604.11554v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training has proven effective at unlocking reasoning, self-reflection, and tool-use capabilities in large language mode

Tutorials

Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning

arXiv:2604.11407v1 Announce Type: cross Abstract: We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external

tutorialsarxiv-cs-ai

Applications

Retrieving to Recover: Towards Incomplete Audio-Visual Question Answering via Semantic-consistent Purification

arXiv:2604.10695v1 Announce Type: new Abstract: Recent Audio-Visual Question Answering (AVQA) methods have advanced significantly. However, most AVQA methods lack effective mechanisms for handling mis

applicationsarxiv-cs-cv

Self-Calibrating Language Models via Test-Time Discriminative Distillation

arXiv:2604.09624v1 Announce Type: new Abstract: Large language models (LLMs) are systematically overconfident: they routinely express high certainty on questions they often answer incorrectly. Existin

Semantic Manipulation Localization

arXiv:2604.10132v1 Announce Type: cross Abstract: Image Manipulation Localization (IML) aims to identify edited regions in an image. However, with the increasing use of modern image editing and genera

Sign Language Recognition in the Age of LLMs

arXiv:2604.11225v1 Announce Type: cross Abstract: Recent Vision Language Models (VLMs) have demonstrated strong performance across a wide range of multimodal reasoning tasks. This raises the question

Solver-Independent Automated Problem Formulation via LLMs for High-Cost Simulation-Driven Design

arXiv:2512.18682v2 Announce Type: replace Abstract: In the high-cost simulation-driven design domain, translating ambiguous design requirements into a mathematical optimization formulation is a bottle

SpatialScore: Towards Comprehensive Evaluation for Spatial Intelligence

arXiv:2505.17012v3 Announce Type: replace-cross Abstract: Existing evaluations of multimodal large language models (MLLMs) on spatial intelligence are typically fragmented and limited in scope. In thi

The Myth of Expert Specialization in MoEs: Why Routing Reflects Geometry, Not Necessarily Domain Expertise

arXiv:2604.09780v1 Announce Type: new Abstract: Mixture of Experts (MoEs) are now ubiquitous in large language models, yet the mechanisms behind their 'expert specialization' remain poorly understood.

Variance-Aware Prior-Based Tree Policies for Monte Carlo Tree Search

arXiv:2512.21648v2 Announce Type: replace-cross Abstract: Monte Carlo Tree Search (MCTS) has profoundly influenced reinforcement learning (RL) by integrating planning and learning in tasks requiring l

VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation

arXiv:2603.08715v2 Announce Type: replace-cross Abstract: Rapid advances in language models (LMs) have created new opportunities for automated code generation while complicating trade-offs between mod

VP-VLA: Visual Prompting as an Interface for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2603.22003v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models typically map visual observations and linguistic instructions directly to robotic control signals. This 'black-b

4D-RGPT: Toward Region-level 4D Understanding via Perceptual Distillation

model-releasesarxiv-cs-cv

arXiv:2512.17012v3 Announce Type: replace Abstract: Despite advances in Multimodal LLMs (MLLMs), their ability to reason over 3D structures and temporal dynamics remains limited, constrained by weak 4

ASPECT:Analogical Semantic Policy Execution via Language Conditioned Transfer

arXiv:2604.08355v2 Announce Type: replace Abstract: Reinforcement Learning (RL) agents often struggle to generalize knowledge to new tasks, even those structurally similar to ones they have mastered.

Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine

arXiv:2603.06665v2 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) often benefit from chain-of-thought (CoT) prompting in general domains, yet its efficacy in medical vision

Applications

Confident in a Confidence Score: Investigating the Sensitivity of Confidence Scores to Supervised Fine-Tuning

arXiv:2604.08974v1 Announce Type: new Abstract: Uncertainty quantification is a set of techniques that measure confidence in language models. They can be used, for example, to detect hallucinations or

applicationsarxiv-cs-cl

Facet-Level Tracing of Evidence Uncertainty and Hallucination in RAG

arXiv:2604.09174v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) aims to reduce hallucination by grounding answers in retrieved evidence, yet hallucinated answers remain common eve

Litmus (Re)Agent: A Benchmark and Agentic System for Predictive Evaluation of Multilingual Models

arXiv:2604.08970v1 Announce Type: cross Abstract: We study predictive multilingual evaluation: estimating how well a model will perform on a task in a target language when direct benchmark results are

Applications

LLM-Rosetta: A Hub-and-Spoke Intermediate Representation for Cross-Provider LLM API Translation

arXiv:2604.09360v1 Announce Type: cross Abstract: The rapid proliferation of Large Language Model (LLM) providers--each exposing proprietary API formats--has created a fragmented ecosystem where appli

applicationsarxiv-cs-ai