Search: “research”

Red Teaming Large Reasoning Models

arXiv:2512.00412v4 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, offering enhanced transparency and logical

Hardware

Scalable Trajectory Generation for Whole-Body Mobile Manipulation

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arXiv:2604.12565v1 Announce Type: cross Abstract: Robots deployed in unstructured environments must coordinate whole-body motion -- simultaneously moving a mobile base and arm -- to interact with the

hardwarearxiv-cs-cv

15 Apr 2026

SEW: Self-Evolving Agentic Workflows for Automated Code Generation

arXiv:2505.18646v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated effectiveness in code generation tasks. To enable LLMs to address more complex coding challenge

Siamese Foundation Models for Crystal Structure Prediction

arXiv:2503.10471v2 Announce Type: replace-cross Abstract: Predicting crystal structures from chemical compositions is a fundamental challenge in materials discovery, complicated by complex 3D geometri

tutorialsarxiv-cs-ai

SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks

model-releasesarxiv-cs-cv

arXiv:2506.14512v4 Announce Type: replace Abstract: Large Language Models (LLMs) have undergone rapid progress, largely attributed to reinforcement learning on complex reasoning tasks. In contrast, wh

Thermodynamic Liquid Manifold Networks: Physics-Bounded Deep Learning for Solar Forecasting in Autonomous Off-Grid Microgrids

arXiv:2604.11909v1 Announce Type: cross Abstract: The stable operation of autonomous off-grid photovoltaic systems requires solar forecasting algorithms that respect atmospheric thermodynamics. Contem

TRUST Agents: A Collaborative Multi-Agent Framework for Fake News Detection, Explainable Verification, and Logic-Aware Claim Reasoning

arXiv:2604.12184v1 Announce Type: new Abstract: TRUST Agents is a collaborative multi-agent framework for explainable fact verification and fake news detection. Rather than treating verification as a

Uncertainty Quantification on Graph Learning: A Survey

arXiv:2404.14642v4 Announce Type: replace Abstract: Graphical models have demonstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustwor

applicationsarxiv-cs-lg

WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces

arXiv:2603.05295v3 Announce Type: replace Abstract: We introduce WebChain, the largest open-source dataset of human-annotated trajectories on real-world websites, designed to accelerate reproducible r

Why Did Apple Fall: Evaluating Curiosity in Large Language Models

arXiv:2510.20635v2 Announce Type: replace-cross Abstract: Curiosity serves as a pivotal conduit for human beings to discover and learn new knowledge. Recent advancements of large language models (LLMs

tutorialsarxiv-cs-ai

ACE-Bench: A Lightweight Benchmark for Evaluating Azure SDK Usage Correctness

arXiv:2604.09564v1 Announce Type: cross Abstract: We present ACE-Bench (Azure SDK Coding Evaluation Benchmark), an execution-free benchmark that provides fast, reproducible pass or fail signals for wh

Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations

arXiv:2604.09584v1 Announce Type: new Abstract: Flow physics and more broadly physical phenomena governed by partial differential equations (PDEs), are inherently continuous, high-dimensional and ofte

AI Integrity: A New Paradigm for Verifiable AI Governance

arXiv:2604.11065v1 Announce Type: new Abstract: AI systems increasingly shape high-stakes decisions in healthcare, law, defense, and education, yet existing governance paradigms -- AI Ethics, AI Safet

Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements

arXiv:2604.09628v1 Announce Type: cross Abstract: Explainable AI (XAI) has evolved in response to expectations and regulations, such as the EU AI Act, which introduces regulatory requirements on AI-po

Automatic Uncertainty-Aware Synthetic Data Bootstrapping for Historical Map Segmentation

arXiv:2511.15875v2 Announce Type: replace Abstract: The automated analysis of historical documents, particularly maps, has drastically benefited from advances in deep learning and its success across v

BEM: Training-Free Background Embedding Memory for False-Positive Suppression in Real-Time Fixed-Background Camera

arXiv:2604.11714v1 Announce Type: new Abstract: Pretrained detectors perform well on benchmarks but often suffer performance degradation in real-world deployments due to distribution gaps between trai

Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables

arXiv:2604.11305v1 Announce Type: new Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality r

applicationsarxiv-cs-lg

Beyond Message Passing: A Semantic View of Agent Communication Protocols

arXiv:2604.02369v3 Announce Type: replace-cross Abstract: Agent communication protocols are becoming critical infrastructure for large language model (LLM) systems that must use tools, coordinate with

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation

arXiv:2604.10950v1 Announce Type: new Abstract: Fully supervised Video Semantic Segmentation (VSS) relies heavily on densely annotated video data, limiting practical applicability. Alternatively, appl

C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts

arXiv:2604.11796v1 Announce Type: cross Abstract: Recently, large language models (LLMs) are capable of generating highly fluent textual content. While they offer significant convenience to humans, th

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

CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python

arXiv:2502.07432v2 Announce Type: replace Abstract: CapyMOA is an open-source Python library for efficient machine learning on data streams and online continual learning. It provides a structured fram

tutorialsarxiv-cs-lg

Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models

model-releasesarxiv-cs-cl

arXiv:2503.21380v3 Announce Type: replace Abstract: The rapid advancement of large reasoning models has saturated existing math benchmarks, underscoring the urgent need for more challenging evaluation

ChatInject: Abusing Chat Templates for Prompt Injection in LLM Agents

arXiv:2509.22830v3 Announce Type: replace Abstract: The growing deployment of large language model (LLM) based agents that interact with external environments has created new attack surfaces for adver

agentsarxiv-cs-cl

Comparative Analysis of Large Language Models in Healthcare

model-releasesarxiv-cs-cl

arXiv:2604.10316v1 Announce Type: new Abstract: Background: Large Language Models (LLMs) are transforming artificial intelligence applications in healthcare due to their ability to understand, generat

Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers

arXiv:2604.11507v1 Announce Type: cross Abstract: Artificial intelligence (AI) is moving increasingly beyond prediction to support decisions in complex, uncertain, and dynamic environments. This shift

DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain

model-releasesarxiv-cs-cv

arXiv:2604.10425v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain rem

Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky

arXiv:2507.03336v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly tasked with invoking enterprise APIs, yet they routinely falter when near-duplicate tools vie for the

Distributionally Robust PAC-Bayesian Control

arXiv:2604.10588v1 Announce Type: new Abstract: We present a distributionally robust PAC-Bayesian framework for certifying the performance of learning-based finite-horizon controllers. While existing

safetyarxiv-cs-lg

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

model-releasesarxiv-cs-cl

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

DocRevive: A Unified Pipeline for Document Text Restoration

model-releasesarxiv-cs-cv

arXiv:2604.10077v1 Announce Type: new Abstract: In Document Understanding, the challenge of reconstructing damaged, occluded, or incomplete text remains a critical yet unexplored problem. Subsequent d

Does Your VFM Speak Plant? The Botanical Grammar of Vision Foundation Models for Object Detection

arXiv:2604.09920v1 Announce Type: new Abstract: Vision foundation models (VFMs) offer the promise of zero-shot object detection without task-specific training data, yet their performance in complex ag

Domain-Specific Data Generation Framework for RAG Adaptation

arXiv:2510.11217v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models (LLMs) with external ret

applicationsarxiv-cs-ai

DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation

arXiv:2508.15452v3 Announce Type: replace-cross Abstract: Numerous deep learning-based solutions have been developed for the automatic recognition of breast cancer using mammography images. However, t

Dual-Margin Embedding for Fine-Grained Long-Tailed Plant Taxonomy

arXiv:2512.18994v2 Announce Type: replace Abstract: Taxonomic classification of ecological families, genera, and species underpins biodiversity monitoring and conservation. Existing computer vision me

EDFNet: Early Fusion of Edge and Depth for Thin-Obstacle Segmentation in UAV Navigation

arXiv:2604.09694v1 Announce Type: new Abstract: Autonomous Unmanned Aerial Vehicles (UAVs) must reliably detect thin obstacles such as wires, poles, and branches to navigate safely in real-world envir

agentsarxiv-cs-cv

Empowering Video Translation using Multimodal Large Language Models

arXiv:2604.11283v1 Announce Type: new Abstract: Recent developments in video translation have further enhanced cross-lingual access to video content, with multimodal large language models (MLLMs) play

safetyarxiv-cs-cv

Evaluating Cooperation in LLM Social Groups through Elected Leadership

arXiv:2604.11721v1 Announce Type: cross Abstract: Governing common-pool resources requires agents to develop enduring strategies through cooperation and self-governance to avoid collective failure. Wh

Examining EAP Students' AI Disclosure Intention: A Cognition-Affect-Conation Perspective

arXiv:2604.10991v1 Announce Type: cross Abstract: The growing use of generative artificial intelligence (AI) in academic writing has raised increasing concerns regarding transparency and academic inte

Explainability and Certification of AI-Generated Educational Assessments

arXiv:2604.09622v1 Announce Type: cross Abstract: The rapid adoption of generative artificial intelligence (AI) in educational assessment has created new opportunities for scalable item creation, pers

Exploring the impact of fairness-aware criteria in AutoML

arXiv:2604.10224v1 Announce Type: cross Abstract: Machine Learning (ML) systems are increasingly used to support decision-making processes that affect individuals. However, these systems often rely on

Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation

arXiv:2510.10925v2 Announce Type: replace-cross Abstract: Training student models on synthetic data generated by strong teacher models is a promising way to distilling the capabilities of teachers. Ho

tutorialsarxiv-cs-cl

From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences

arXiv:2604.10332v1 Announce Type: new Abstract: We present the progress of the GPT family from GPT-3 through GPT-3.5, GPT-4, GPT-4 Turbo, GPT-4o, GPT-4.1, and the GPT-5 family. Our work is comparative

From Helpful to Trustworthy: LLM Agents for Pair Programming

arXiv:2604.10300v1 Announce Type: cross Abstract: LLM-based coding agents are increasingly used to generate code, tests, and documentation. Still, their outputs can be plausible yet misaligned with de

GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents

arXiv:2603.24329v2 Announce Type: replace-cross Abstract: Multimodal LLMs are increasingly deployed as perceptual backbones for autonomous agents in 3D environments, from robotics to virtual worlds. T

General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging

arXiv:2604.09609v1 Announce Type: new Abstract: Human behavior models are essential as behavior references and for simulating human agents in virtual safety assessment of automated vehicles (AVs), yet

GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning

arXiv:2505.17022v2 Announce Type: replace-cross Abstract: Visual generation models have made remarkable progress in creating realistic images from text prompts, yet struggle with complex prompts that

How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities

arXiv:2603.02578v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors, ranging from misalign