Search: “reasoning”

GUI Agents with Reinforcement Learning: Toward Digital Inhabitants

arXiv:2604.27955v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents have emerged as a promising paradigm for intelligent systems that perceive and interact with graphical interfaces

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HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation

DGX agent

arXiv:2604.28196v1 Announce Type: new Abstract: Driving world models serve as a pivotal technology for autonomous driving by simulating environmental dynamics. However, existing approaches predominant

model-releasesarxiv-cs-cv

1 May 2026

In Line with Context: Repository-Level Code Generation via Context Inlining

arXiv:2601.00376v2 Announce Type: replace-cross Abstract: Repository-level code generation has attracted growing attention in recent years. Unlike function-level code generation, it requires the model

researcharxiv-cs-ai

Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions

arXiv:2604.27763v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) offers a transformative interface for Web3, yet existing benchmarks fail to capture the complexity of tran

JI-ADF: Joint-Individual Learning with Adaptive Decision Fusion for Multimodal Skin Lesion Classification

arXiv:2604.27343v1 Announce Type: new Abstract: Skin lesion classification is essential for early dermatological diagnosis, yet many existing computer-aided systems rely primarily on dermoscopic image

Judge, Then Drive: A Critic-Centric Vision Language Action Framework for Autonomous Driving

arXiv:2604.27366v1 Announce Type: new Abstract: Recent advances in vision language action (VLA) models have shown remarkable potential for autonomous driving by directly mapping multimodal inputs to c

Language Models Refine Mechanical Linkage Designs Through Symbolic Reflection and Modular Optimisation

arXiv:2604.27962v1 Announce Type: new Abstract: Designing mechanical linkages involves combinatorial topology selection and continuous parameter fitting. We show that language models can systematicall

Learning-to-Explain through 20Q Gaming: An Explainable Recommender for Cybersecurity Education

arXiv:2604.26964v1 Announce Type: cross Abstract: The growing sophistication of contemporary cyber threats necessitates a more effective and adaptive approach to cybersecurity training. Intuitive and

Linguistically Informed Multimodal Fusion for Vietnamese Scene-Text Image Captioning: Dataset, Graph Framework, and Phonological Attention

arXiv:2604.27712v1 Announce Type: cross Abstract: Scene-text image captioning requires fusing three information streams -- visual features, OCR-detected text, and linguistic knowledge -- to generate d

safetyarxiv-cs-cl

LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis

arXiv:2604.28178v1 Announce Type: new Abstract: Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging.

researcharxiv-cs-ai

Machine Collective Intelligence for Explainable Scientific Discovery

arXiv:2604.27297v1 Announce Type: new Abstract: Deriving governing equations from empirical observations is a longstanding challenge in science. Although artificial intelligence (AI) has demonstrated

Mapping how LLMs debate societal issues when shadowing human personality traits, sociodemographics and social media behavior

arXiv:2604.27624v1 Announce Type: cross Abstract: Large Language Models (LLMs) can strongly shape social discourse, yet datasets investigating how LLM outputs vary across controlled social and context

METASYMBO: Multi-Agent Language-Guided Metamaterial Discovery via Symbolic Latent Evolution

arXiv:2604.27300v1 Announce Type: new Abstract: Metamaterial discovery seeks microstructured materials whose geometry induces targeted mechanical behavior. Existing inverse-design methods can efficien

Local Ai

MoCapAnything V2: End-to-End Motion Capture for Arbitrary Skeletons

arXiv:2604.28130v1 Announce Type: new Abstract: Recent methods for arbitrary-skeleton motion capture from monocular video follow a factorized pipeline, where a Video-to-Pose network predicts joint pos

local-aiarxiv-cs-cv

On the Proper Treatment of Units in Surprisal Theory

arXiv:2604.28147v1 Announce Type: new Abstract: Surprisal theory links human processing effort to the predictability of an upcoming linguistic unit, but empirical work often leaves the notion of a uni

researcharxiv-cs-cl

Ponder This Challenge - May 2026 - The Powers of a Binary Matrix

This IBM Research challenge problem likely involves analyzing mathematical properties of binary matrices (matrices containing only 0s and 1s), potentially exploring topics such as matrix operations, e

researchibm-research

Pragmos: A Process Agentic Modeling System

arXiv:2604.27311v1 Announce Type: cross Abstract: The advent of Large Language Models (LLMs) has significantly transformed tasks across Software Engineering. In the context of Business Process Managem

Reinforced Agent: Inference-Time Feedback for Tool-Calling Agents

arXiv:2604.27233v1 Announce Type: new Abstract: Tool-calling agents are evaluated on tool selection, parameter accuracy, and scope recognition, yet LLM trajectory assessments remain inherently post-ho

Rethinking Pulmonary Embolism Segmentation: A Study of Current Approaches and Challenges with an Open Weight Model

arXiv:2509.18308v3 Announce Type: replace Abstract: Pulmonary Embolism (PE) is a life-threatening condition for which accurate and timely detection is critical to patient care. However, our systematic

researcharxiv-cs-cv

Applications

Robot Planning and Situation Handling with Active Perception

arXiv:2604.26988v1 Announce Type: new Abstract: Current robots are capable of computing plans to accomplish complex tasks. However, real-world environments are inherently open and dynamic, and unfores

applicationsarxiv-cs-ro

Semantic Variational Bayes Based on Semantic Information G Theory for Solving Latent Variables

arXiv:2408.13122v2 Announce Type: replace-cross Abstract: The Variational Bayesian method (VB) is used to solve the probability distributions of latent variables with the minimum free energy criterion

SpatialGrammar: A Domain-Specific Language for LLM-Based 3D Indoor Scene Generation

arXiv:2604.27555v1 Announce Type: new Abstract: Automatically generating interactive 3D indoor scenes from natural language is crucial for virtual reality, gaming, and embodied AI. However, existing L

SpecVQA: A Benchmark for Spectral Understanding and Visual Question Answering in Scientific Images

arXiv:2604.28039v1 Announce Type: new Abstract: Spectra are a prevalent yet highly information-dense form of scientific imagery, presenting substantial challenges to multimodal large language models (

Step-level Optimization for Efficient Computer-use Agents

arXiv:2604.27151v1 Announce Type: new Abstract: Computer-use agents provide a promising path toward general software automation because they can interact directly with arbitrary graphical user interfa

The Effects of Visual Priming on Cooperative Behavior in Vision-Language Models

arXiv:2604.27953v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) become increasingly integrated into decision-making systems, it is essential to understand how visual inputs influence

To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems

arXiv:2604.28053v1 Announce Type: cross Abstract: Responsible AI research typically focuses on examining the use and impacts of deployed AI systems. Yet, there is currently limited visibility into the

Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling

arXiv:2604.28185v1 Announce Type: new Abstract: Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still str

What Suppresses Nash Equilibrium Play in Large Language Models? Mechanistic Evidence and Causal Control

arXiv:2604.27167v1 Announce Type: cross Abstract: LLM agents are known to deviate from Nash equilibria in strategic interactions, but nobody has looked inside the model to understand why, or asked whe

When Roles Fail: Epistemic Constraints on Advocate Role Fidelity in LLM-Based Political Statement Analysis

arXiv:2604.27228v1 Announce Type: new Abstract: Democratic discourse analysis systems increasingly rely on multi-agent LLM pipelines in which distinct evaluator models are assigned adversarial roles t

WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments

arXiv:2604.27776v1 Announce Type: new Abstract: While GUI agents have shown impressive capabilities in common computer-use tasks such as OSWorld, current benchmarks mainly focus on isolated and single

A Multimodal Pre-trained Network for Integrated EEG-Video Seizure Detection

arXiv:2604.26379v1 Announce Type: new Abstract: Reliable seizure detection in mouse models is essential for preclinical epilepsy research, yet manual review of synchronized video-EEG recordings is lab

safetyarxiv-cs-cv

A Multistage Extraction Pipeline for Long Scanned Financial Documents: An Empirical Study in Industrial KYC Workflows

arXiv:2604.26462v1 Announce Type: new Abstract: Structured information extraction from long, multilingual scanned financial documents is a core requirement in industrial KYC and compliance workflows.

A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?

arXiv:2505.10924v4 Announce Type: replace-cross Abstract: Recently, AI-driven interactions with computing devices have advanced from basic prototype tools to sophisticated, LLM-based systems that emul

A Systematic Comparison of Prompting and Multi-Agent Methods for LLM-based Stance Detection

model-releasesarxiv-cs-cl

arXiv:2604.26319v1 Announce Type: new Abstract: Stance detection identifies the attitude of a text author toward a given target. Recent studies have explored various LLM-based strategies for this task

Accelerating RL Post-Training Rollouts via System-Integrated Speculative Decoding

arXiv:2604.26779v1 Announce Type: cross Abstract: RL post-training of frontier language models is increasingly bottlenecked by autoregressive rollout generation, making rollout acceleration a central

safetyarxiv-cs-cl

Local Ai

Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL

arXiv:2603.19470v2 Announce Type: replace-cross Abstract: Off-policy problems such as policy staleness and training--inference mismatch have become a major bottleneck for training stability and furthe

local-aiarxiv-cs-ai

Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests

model-releasesarxiv-cs-cl

arXiv:2601.17617v3 Announce Type: replace-cross Abstract: LLM-powered search agents are increasingly being used for multi-step information seeking tasks, yet the IR community lacks empirical understan

Hardware

AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving

arXiv:2604.26103v1 Announce Type: cross Abstract: All current LLM serving systems place the GPU at the center, from production-level attention-FFN disaggregation to NVIDIA's Rubin GPU-LPU heterogeneou

hardwarearxiv-cs-ai

Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations

arXiv:2604.26805v1 Announce Type: new Abstract: Operating and maintaining (O&M) large-scale online engine systems (search, recommendation, advertising) demands substantial human effort for release mon

CacheRAG: A Semantic Caching System for Retrieval-Augmented Generation in Knowledge Graph Question Answering

arXiv:2604.26176v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) has significantly advanced Knowledge Graph Question Answerin

researcharxiv-cs-cl

Calibrated Surprise: An Information-Theoretic Account of Creative Quality

arXiv:2604.26269v1 Announce Type: cross Abstract: The essence of good creative writing is calibrated surprise: when constraints from all relevant dimensions act together, the feasible solution space c

CoQuant: Joint Weight-Activation Subspace Projection for Mixed-Precision LLMs

model-releasesarxiv-cs-lg

arXiv:2604.26378v1 Announce Type: new Abstract: Post-training quantization (PTQ) has become an important technique for reducing the inference cost of Large Language Models (LLMs). While recent mixed-p

DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training

arXiv:2604.26256v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time --

safetyarxiv-cs-lg

Efficient, VRAM-Constrained xLM Inference on Clients

model-releasesarxiv-cs-lg

arXiv:2604.26334v1 Announce Type: cross Abstract: To usher in the next round of client AI innovation, there is an urgent need to enable efficient, lossless inference of high-accuracy large language mo

Entropy Centroids as Intrinsic Rewards for Test-Time Scaling

arXiv:2604.26173v1 Announce Type: cross Abstract: An effective way to scale up test-time compute of large language models is to sample multiple responses and then select the best one, as in Grok Heavy

Evaluation Revisited: A Taxonomy of Evaluation Concerns in Natural Language Processing

arXiv:2604.25923v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have prompted a growing body of work that questions the methodology of prevailing evaluation practices.

researcharxiv-cs-cl

Glance-or-Gaze: Incentivizing LMMs to Adaptively Focus Search via Reinforcement Learning

arXiv:2601.13942v2 Announce Type: replace-cross Abstract: Large Multimodal Models (LMMs) have achieved remarkable success in visual understanding, yet they struggle with knowledge-intensive queries in

Graph-based Semantic Calibration Network for Unaligned UAV RGBT Image Semantic Segmentation and A Large-scale Benchmark

arXiv:2604.26893v1 Announce Type: new Abstract: Fine-grained RGBT image semantic segmentation is crucial for all-weather unmanned aerial vehicle (UAV) scene understanding. However, UAV RGBT semantic s