Search: “reasoning”

Why Does Reinforcement Learning Generalize? A Feature-Level Mechanistic Study of Post-Training in Large Language Models

arXiv:2604.25011v1 Announce Type: new Abstract: Reinforcement learning (RL)-based post-training often improves the reasoning performance of large language models (LLMs) beyond the training domain, whi

29 Apr 2026

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Can Compact Language Models Search Like Agents? Distillation-Guided Policy Optimization for Preserving Agentic RAG Capabilities

DGX agent

arXiv:2508.20324v4 Announce Type: replace Abstract: Reinforcement Learning has emerged as a dominant post-training approach to elicit agentic RAG behaviors such as search and planning from language mo

safetyarxiv-cs-cl

28 Apr 2026

ContextWeaver: Selective and Dependency-Structured Memory Construction for LLM Agents

arXiv:2604.23069v1 Announce Type: new Abstract: Large language model (LLM) agents often struggle in long-context interactions. As the agent accumulates more interaction history, context management app

agentsarxiv-cs-cl

DPEPO: Diverse Parallel Exploration Policy Optimization for LLM-based Agents

arXiv:2604.24320v1 Announce Type: new Abstract: Large language model (LLM) agents that follow the sequential 'reason-then-act' paradigm have achieved superior performance in many complex tasks.However

Guided Speculative Inference for Efficient Test-Time Alignment of LLMs

arXiv:2506.04118v3 Announce Type: replace Abstract: We propose Guided Speculative Inference (GSI), a novel algorithm for efficient reward-guided decoding in large language models. GSI combines soft be

safetyarxiv-cs-lg

Language Models Might Not Understand You: Evaluating Theory of Mind via Story Prompting

arXiv:2506.19089v5 Announce Type: replace-cross Abstract: We introduce StorySim, a programmable framework for synthetically generating stories to evaluate the theory of mind (ToM) and world modeling (

researcharxiv-cs-ai

Quantifying Divergence in Inter-LLM Communication Through API Retrieval and Ranking

arXiv:2604.22760v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly operate as autonomous agents that reason over external APIs to perform complex tasks. However, their reliabi

Quantum Knowledge Graph: Modeling Context-Dependent Triplet Validity

arXiv:2604.23972v1 Announce Type: cross Abstract: Knowledge graphs (KGs) are increasingly used to support large lan guage model (LLM) reasoning, but standard triplet-based KGs treat each relation as g

Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes

arXiv:2603.25562v2 Announce Type: replace-cross Abstract: On-policy distillation (OPD) is increasingly used in LLM post-training because it can leverage a teacher model to provide dense supervision on

S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA

arXiv:2604.23783v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds language models in external evidence, but multi-hop question answering remains difficult because iterativ

researcharxiv-cs-ai

SWE-QA: Can Language Models Answer Repository-level Code Questions?

arXiv:2509.14635v2 Announce Type: replace Abstract: Understanding and reasoning about entire software repositories is an essential capability for intelligent software engineering tools. While existing

Applications

VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs

arXiv:2604.23356v1 Announce Type: new Abstract: Large language models (LLMs) show promise in medical diagnosis, but real-world deployment remains challenging due to high-stakes clinical decisions and

applicationsarxiv-cs-cl

Your Students Don't Use LLMs Like You Wish They Did

arXiv:2604.23486v1 Announce Type: new Abstract: Educational NLP systems are typically evaluated using engagement metrics and satisfaction surveys, which are at best a proxy for meeting pedagogical goa

Towards Safe Mobility: A Unified Transportation Foundation Model enabled by Open-Ended Vision-Language Dataset

arXiv:2604.22260v1 Announce Type: cross Abstract: Urban transportation systems face growing safety challenges that require scalable intelligence for emerging smart mobility infrastructures. While rece

27 Apr 2026

AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models

arXiv:2604.20878v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Traffic Accident Detection (TAD) and Traffic Accident Understanding (TAU).

Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks

arXiv:2604.20987v1 Announce Type: new Abstract: Long horizon interactive environments are a testbed for evaluating agents skill usage abilities. These environments demand multi step reasoning, the cha

agentsarxiv-cs-ai

Geo-R1: Improving Few-Shot Geospatial Referring Expression Understanding with Reinforcement Fine-Tuning

arXiv:2509.21976v3 Announce Type: replace-cross Abstract: Referring expression understanding in remote sensing poses unique challenges, as it requires reasoning over complex object-context relationshi

Reinforcing 3D Understanding in Point-VLMs via Geometric Reward Credit Assignment

model-releasesarxiv-cs-cv

arXiv:2604.21160v1 Announce Type: new Abstract: Point-Vision-Language Models promise to empower embodied agents with executable spatial reasoning, yet they frequently succumb to geometric hallucinatio

Tutorials

Strategic Scaling of Test-Time Compute: A Bandit Learning Approach

arXiv:2506.12721v2 Announce Type: replace Abstract: Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models. However, existing methods typ

tutorialsarxiv-cs-ai

DRIV-EX: Counterfactual Explanations for Driving LLMs

arXiv:2603.00696v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving, yet their decision-making remains opaque. We propose

Efficient Test-Time Inference via Deterministic Exploration of Truncated Decoding Trees

arXiv:2604.20500v1 Announce Type: new Abstract: Self-consistency boosts inference-time performance by sampling multiple reasoning traces in parallel and voting. However, in constrained domains like ma

researcharxiv-cs-lg

Applications

Human-like Content Analysis for Generative AI with Language-Grounded Sparse Encoders

arXiv:2508.18236v4 Announce Type: replace Abstract: The rapid development of generative AI has transformed content creation, communication, and human development. However, this technology raises profo

applicationsarxiv-cs-cv

ONOTE: Benchmarking Omnimodal Notation Processing for Expert-level Music Intelligence

arXiv:2604.20719v1 Announce Type: cross Abstract: Omnimodal Notation Processing (ONP) represents a unique frontier for omnimodal AI due to the rigorous, multi-dimensional alignment required across aud

Cross-cloud infrastructure innovation for the agentic enterprise

model-releasesgoogle-cloud-ai

The era of agentic AI is accelerating from human- to machine-speed operations, while also creating profound stress on legacy technology infrastructure. This new reality pushes foundational systems to

Introducing Gemini Enterprise Agent Platform, powering the next wave of agents

model-releasesgoogle-cloud-ai

In the early days of generative AI, building safe and reliable business tools took massive engineering effort and a high tolerance for trial and error. We helped solve that with Vertex AI, our trusted

M^{2}GRPO: Mamba-based Multi-Agent Group Relative Policy Optimization for Biomimetic Underwater Robots Pursuit

arXiv:2604.19404v1 Announce Type: cross Abstract: Traditional policy learning methods in cooperative pursuit face fundamental challenges in biomimetic underwater robots, where long-horizon decision ma

Applications

Seeing Candidates at Scale: Multimodal LLMs for Visual Political Communication on Instagram

arXiv:2604.19489v1 Announce Type: new Abstract: This paper presents a computational case study that evaluates the capabilities of specialized machine learning models and emerging multimodal large lang

applicationsarxiv-cs-cv

Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning

arXiv:2601.18296v2 Announce Type: replace-cross Abstract: Temporal Knowledge Graph Question Answering (TKGQA) is inherently challenging, as it requires sophisticated reasoning over dynamic facts with

What’s new in the Agentic Data Cloud: Powering the System of Action

model-releasesgoogle-cloud-ai

Companies are shifting from gen AI that simply answers questions to autonomous agents that perceive, reason, and act on their behalf. Attempting to scale these agents on legacy stacks exposes structur

When Does Verification Pay Off? A Closer Look at LLMs as Solution Verifiers

arXiv:2512.02304v2 Announce Type: replace Abstract: Large language models (LLMs) can act as both problem solvers and solution verifiers, where the latter select high-quality answers from a pool of sol

DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models

arXiv:2604.16979v1 Announce Type: cross Abstract: High-quality and diverse multimodal data are essential for improving vision-language models (VLMs), yet existing datasets often contain noisy, redunda

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning

arXiv:2412.02904v2 Announce Type: replace Abstract: Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering cap

Jupiter-N Technical Report

arXiv:2604.17429v1 Announce Type: new Abstract: We present Jupiter-N, a hybrid reasoning model post-trained from Nemotron 3 Super, a fully open-source 120 billion parameter LLM. We target three object

Latent Phase-Shift Rollback: Inference-Time Error Correction via Residual Stream Monitoring and KV-Cache Steering

arXiv:2604.18567v1 Announce Type: cross Abstract: Large language models frequently commit unrecoverable reasoning errors mid-generation: once a wrong step is taken, subsequent tokens compound the mist

Medical thinking with multiple images

arXiv:2604.16506v1 Announce Type: cross Abstract: Large language models perform well on many medical QA benchmarks, but real clinical reasoning often requires integrating evidence across multiple imag

MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding

arXiv:2505.20715v2 Announce Type: replace-cross Abstract: Video temporal understanding is crucial for multimodal large language models (MLLMs) to reason over events in videos. Despite recent advances

PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment

arXiv:2508.05132v2 Announce Type: replace Abstract: As medical LLMs transition to clinical deployment, assessing their ethical reasoning capability becomes critical. While achieving high accuracy on k

RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation

arXiv:2604.17243v1 Announce Type: new Abstract: A robust Multimodal Large Language Model (MLLM) for Earth Observation should maintain consistent interpretation and reasoning under realistic input vari

safetyarxiv-cs-cv

Tutorials

RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models

arXiv:2604.17725v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong promise for mining Electronic Health Records (EHRs) by reasoning over longitudinal clinical information t

tutorialsarxiv-cs-cl

AgentV-RL: Scaling Reward Modeling with Agentic Verifier

arXiv:2604.16004v1 Announce Type: cross Abstract: Verifiers have been demonstrated to enhance LLM reasoning via test-time scaling (TTS). Yet, they face significant challenges in complex domains. Error

agentsarxiv-cs-ai

20 Apr 2026

PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research

arXiv:2604.15411v1 Announce Type: cross Abstract: The paradigm of agentic science requires AI systems to conduct robust reasoning and engage in long-horizon, autonomous exploration. However, current s

20 Apr 2026

SENSE: Stereo OpEN Vocabulary SEmantic Segmentation

arXiv:2604.15946v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation enables models to segment objects or image regions beyond fixed class sets, offering flexibility in dynamic enviro

agentsarxiv-cs-cv

20 Apr 2026

ConfLayers: Adaptive Confidence-based Layer Skipping for Self-Speculative Decoding

arXiv:2604.14612v1 Announce Type: cross Abstract: Self-speculative decoding is an inference technique for large language models designed to speed up generation without sacrificing output quality. It c

17 Apr 2026

TableNet A Large-Scale Table Dataset with LLM-Powered Autonomous

arXiv:2604.13041v1 Announce Type: cross Abstract: Table Structure Recognition (TSR) requires the logical reasoning ability of large language models (LLMs) to handle complex table layouts, but current

agentsarxiv-cs-ai

17 Apr 2026

BenGER: A Collaborative Web Platform for End-to-End Benchmarking of German Legal Tasks

arXiv:2604.13583v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for legal reasoning requires workflows that span task design, expert annotation, model execution, and metric-bas

16 Apr 2026

Training-Free Test-Time Contrastive Learning for Large Language Models

arXiv:2604.13552v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong reasoning capabilities, but their performance often degrades under distribution shift. Existing test-tim

agentsarxiv-cs-cl

16 Apr 2026

ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search

arXiv:2604.12762v1 Announce Type: cross Abstract: We introduce ARGOS, the first benchmark and framework that reformulates multi-camera person search as an interactive reasoning problem requiring an ag

15 Apr 2026

Designing Reliable LLM-Assisted Rubric Scoring for Constructed Responses: Evidence from Physics Exams

arXiv:2604.12227v1 Announce Type: new Abstract: Student responses in STEM assessments are often handwritten and combine symbolic expressions, calculations, and diagrams, creating substantial variation