Search: “agents”

k-server-bench: Automating Potential Discovery for the k-Server Conjecture

arXiv:2604.07240v1 Announce Type: cross Abstract: We introduce a code-based challenge for automated, open-ended mathematical discovery based on the k-server conjecture, a central open problem in com

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Learning Who Disagrees: Demographic Importance Weighting for Modeling Annotator Distributions with DiADEM

DGX agent

arXiv:2604.08425v1 Announce Type: cross Abstract: When humans label subjective content, they disagree, and that disagreement is not noise. It reflects genuine differences in perspective shaped by anno

safetyarxiv-cs-cl

10 Apr 2026

LINE: LLM-based Iterative Neuron Explanations for Vision Models

arXiv:2604.08039v1 Announce Type: new Abstract: Interpreting the concepts encoded by individual neurons in deep neural networks is a crucial step towards understanding their complex decision-making pr

LLM-based Schema-Guided Extraction and Validation of Missing-Person Intelligence from Heterogeneous Data Sources

arXiv:2604.06571v1 Announce Type: cross Abstract: Missing-person and child-safety investigations rely on heterogeneous case documents, including structured forms, bulletin-style posters, and narrative

MDP modeling for multi-stage stochastic programs

arXiv:2509.22981v2 Announce Type: replace Abstract: We study a class of multi-stage stochastic programs, which incorporate modeling features from Markov decision processes (MDPs). This class includes

safetyarxiv-cs-lg

MedDialBench: Benchmarking LLM Diagnostic Robustness under Parametric Adversarial Patient Behaviors

arXiv:2604.06846v1 Announce Type: cross Abstract: Interactive medical dialogue benchmarks have shown that LLM diagnostic accuracy degrades significantly when interacting with non-cooperative patients,

MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices

arXiv:2604.07780v1 Announce Type: cross Abstract: Objective: To develop a robust and compact deep learning model for automated knee cartilage segmentation on point-of-care ultrasound (POCUS) devices.

Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search

arXiv:2604.06771v1 Announce Type: cross Abstract: Conversational Query Rewriting (CQR) aims to rewrite ambiguous queries to achieve more efficient conversational search. Early studies have predominant

Not All Tokens See Equally: Perception-Grounded Policy Optimization for Large Vision-Language Models

arXiv:2604.01840v2 Announce Type: replace Abstract: While Reinforcement Learning from Verifiable Rewards (RLVR) has advanced reasoning in Large Vision-Language Models (LVLMs), prevailing frameworks su

OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks

arXiv:2604.08539v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has emerged as the de facto Reinforcement Learning (RL) objective driving recent advancements in Multimodal

PeReGrINE: Evaluating Personalized Review Fidelity with User Item Graph Context

arXiv:2604.07788v1 Announce Type: cross Abstract: We introduce PeReGrINE, a benchmark and evaluation framework for personalized review generation grounded in graph-structured user--item evidence. PeRe

Research

Phantasia: Context-Adaptive Backdoors in Vision Language Models

arXiv:2604.08395v1 Announce Type: new Abstract: Recent advances in Vision-Language Models (VLMs) have greatly enhanced the integration of visual perception and linguistic reasoning, driving rapid prog

researcharxiv-cs-cv

Applications

PIArena: A Platform for Prompt Injection Evaluation

arXiv:2604.08499v1 Announce Type: cross Abstract: Prompt injection attacks pose serious security risks across a wide range of real-world applications. While receiving increasing attention, the communi

applicationsarxiv-cs-cl

Plug-and-Play Logit Fusion for Heterogeneous Pathology Foundation Models

arXiv:2604.07779v1 Announce Type: new Abstract: Pathology foundation models (FMs) have become central to computational histopathology, offering strong transfer performance across a wide range of diagn

ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework

arXiv:2604.07506v1 Announce Type: cross Abstract: Reward Models (RMs) are critical components in the Reinforcement Learning from Human Feedback (RLHF) pipeline, directly determining the alignment qual

Reset-Free Reinforcement Learning for Real-World Agile Driving: An Empirical Study

arXiv:2604.07672v1 Announce Type: new Abstract: This paper presents an empirical study of reset-free reinforcement learning (RL) for real-world agile driving, in which a physical 1/10-scale vehicle le

safetyarxiv-cs-ro

Hardware

RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMs

arXiv:2510.19225v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become essential for unlocking advanced reasoning capabilities in large language models (LLMs). RL workflows i

hardwarearxiv-cs-lg

RoSHI: A Versatile Robot-oriented Suit for Human Data In-the-Wild

arXiv:2604.07331v1 Announce Type: cross Abstract: Scaling up robot learning will likely require human data containing rich and long-horizon interactions in the wild. Existing approaches for collecting

SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments

arXiv:2604.06254v1 Announce Type: cross Abstract: With the rapid growth of interconnected devices in Industrial and Medical Internet of Things (IIoT and MIoT) ecosystems, ensuring timely and accurate

Self-Debias: Self-correcting for Debiasing Large Language Models

arXiv:2604.08243v1 Announce Type: new Abstract: Although Large Language Models (LLMs) demonstrate remarkable reasoning capabilities, inherent social biases often cascade throughout the Chain-of-Though

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP

arXiv:2509.26036v3 Announce Type: replace Abstract: While Contrastive Language-Image Pretraining (CLIP) excels at zero-shot tasks by aligning image and text embeddings, its performance in few-shot cla

SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds

arXiv:2604.08544v1 Announce Type: cross Abstract: Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve in

Tutorials

Steering the Verifiability of Multimodal AI Hallucinations

arXiv:2604.06714v1 Announce Type: new Abstract: AI applications driven by multimodal large language models (MLLMs) are prone to hallucinations and pose considerable risks to human users. Crucially, su

tutorialsarxiv-cs-ai

Applications

Stop Listening to Me! How Multi-turn Conversations Can Degrade LLM Diagnostic Reasoning

arXiv:2603.11394v2 Announce Type: replace Abstract: Patients and clinicians are increasingly using chatbots powered by large language models (LLMs) for healthcare inquiries. While state-of-the-art LLM

applicationsarxiv-cs-cl

SymptomWise: A Deterministic Reasoning Layer for Reliable and Efficient AI Systems

arXiv:2604.06375v1 Announce Type: new Abstract: AI-driven symptom analysis systems face persistent challenges in reliability, interpretability, and hallucination. End-to-end generative approaches ofte

SYN-DIGITS: A Synthetic Control Framework for Calibrated Digital Twin Simulation

arXiv:2604.07513v1 Announce Type: cross Abstract: AI-based persona simulation -- often referred to as digital twin simulation -- is increasingly used for market research, recommender systems, and soci

The AI Skills Shift: Mapping Skill Obsolescence, Emergence, and Transition Pathways in the LLM Era

arXiv:2604.06906v1 Announce Type: cross Abstract: As Large Language Models reshape the global labor market, policymakers and workers need empirical data on which occupational skills may be most suscep

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training

arXiv:2604.07754v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) raises significant ethical and safety concerns. While LLM alignment techniques are adopted to improve m

The Sustainability Gap in Robotics: A Large-Scale Survey of Sustainability Awareness in 50,000 Research Articles

arXiv:2604.07921v1 Announce Type: new Abstract: We present a large-scale survey of sustainability communication and motivation in robotics research. Our analysis covers nearly 50,000 open-access paper

safetyarxiv-cs-ro

Towards the Development of an LLM-Based Methodology for Automated Security Profiling in Compliance with Ukrainian Cybersecurity Regulations

arXiv:2604.06274v1 Announce Type: cross Abstract: In recent years, the pace of development of information technology in various areas has increased drastically, forcing cybersecurity specialists to co

TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation

arXiv:2604.07894v1 Announce Type: new Abstract: Personalized large language models (PLLMs) have garnered significant attention for their ability to align outputs with individual's needs and preference

WASD: Locating Critical Neurons as Sufficient Conditions for Explaining and Controlling LLM Behavior

arXiv:2603.18474v2 Announce Type: replace Abstract: Precise behavioral control of large language models (LLMs) is critical for complex applications. However, existing methods often incur high training

What Drives Representation Steering? A Mechanistic Case Study on Steering Refusal

arXiv:2604.08524v1 Announce Type: cross Abstract: Applying steering vectors to large language models (LLMs) is an efficient and effective model alignment technique, but we lack an interpretable explan

Hardware

Architecting for Hardware Diversity: Meta's End-to-End Co-Design for High-Performance AI Training on AMD GPUs

arXiv:2610.22230v1 Announce Type: cross Abstract: Meta deploys a heterogeneous fleet comprising NVIDIA GPUs, AMD GPUs, and proprietary MTIA accelerators to reduce costs and mitigate vendor lock-in. Ho

hardwarearxiv-cs-lg

CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty

arXiv:2610.22292v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming prevalent in scenarios where honesty is critical. However, despite alignment training, these models still ex

Hardware

CertEx: Exact Visual Retrieval at Scale by Certified Exclusion

arXiv:2610.21325v1 Announce Type: new Abstract: Exact k-nearest-neighbor (kNN) search over image descriptors and embeddings underlies visual retrieval, near-duplicate detection, the evaluation of lear

hardwarearxiv-cs-cv

Local Ai

Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning

arXiv:2610.20836v1 Announce Type: new Abstract: Evaluator-guided reasoning presents a local control problem at every step: trust the current answer, return a fallback, or spend more inference. We form

local-aiarxiv-cs-ai

From Long Context to Selective Memory:A Survey of Memory-Centric Compression for Large Language Models

arXiv:2610.21165v1 Announce Type: new Abstract: Long-context capability has become essential for large language models (LLMs) to handle complex tasks, yet continuously expanding the context window doe

Local Ai

IKMo: Image-Keyframed Motion Generation with Trajectory-Pose Conditioned Motion Diffusion Model

arXiv:2505.21146v2 Announce Type: replace-cross Abstract: While text-to-motion generation is mainstream, integrating explicit spatial constraints has become a standard solution for precise animation.

local-aiarxiv-cs-cv

Learning to Reason with Selective Internal Feedback

arXiv:2610.21392v1 Announce Type: new Abstract: Reinforcement learning from internal feedback can improve language models without ground-truth answers or external verifiers. However, confidence and co

Memory as Weights: Internalizing Long-Term History for Streaming Videos

arXiv:2610.22070v1 Announce Type: cross Abstract: Streaming vision-language models (VLMs) must continuously integrate information from the past while remaining responsive to current observations and u

Mine, Sample, Render: Test-Time Diversity Alignment Of Generative Models

arXiv:2610.21185v1 Announce Type: new Abstract: Modern generative models produce high-quality individual outputs but struggle to sample diversely, even when explicitly instructed to do so. We introduc

Research

Mixture of Multi-Sequence Feature Difference for Contrast-Enhanced MRI Synthesis

arXiv:2610.21703v1 Announce Type: new Abstract: Contrast-enhanced magnetic resonance imaging (CE-MRI) is crucial for accurately diagnosing and segmenting brain tumors. However, the acquisition of CE-M

researcharxiv-cs-cv

MoCaP: Mobile Code-as-Policies for Zero-Shot Mobile Manipulation from Passive Human Videos

arXiv:2610.20998v1 Announce Type: new Abstract: Passive human videos of everyday activity offer robots access to a massive amount of human experience without requiring a demonstration for each task. H

safetyarxiv-cs-ro

OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling

arXiv:2610.22355v1 Announce Type: new Abstract: Generative visual verifiers play a crucial role in advancing multimodal intelligence. To enhance the robustness of visual verifiers in complex multimoda

Research

OnBodyAI - Why the Next AI Needs a Body

arXiv:2610.21137v1 Announce Type: cross Abstract: The next AI will not win by knowing more about the world. It will win because it knows you: because it lives on your body, works for you and answers t

researcharxiv-cs-ai

PEDBench: Can Speech Models Detect Phonetic Errors?

arXiv:2610.20830v1 Announce Type: new Abstract: Phonetic error detection (PED) aims to identify phoneme-level substitutions, deletions, and insertions and is essential for educational and clinical spe

Prism-1: Position- and Rival-Independent Scoring for System One Models

arXiv:2610.22066v1 Announce Type: new Abstract: Given a state and a set of options, such as intents, tools or interface elements, System One decision models return a distribution over the options in o