Search: “framework”

Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty

arXiv:2604.19677v1 Announce Type: cross Abstract: Reinforcement learning-based control policies have been frequently demonstrated to be more effective than analytical techniques for many manipulation

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Model Releases

LiveVLN: Breaking the Stop-and-Go Loop in Vision-Language Navigation

DGX agent

arXiv:2604.19536v1 Announce Type: new Abstract: Recent navigation systems achieve strong benchmark results, yet real-world deployment often remains visibly stop-and-go. This bottleneck arises because

model-releasesarxiv-cs-ro

22 Apr 2026

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

safetyarxiv-cs-ai

MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping

arXiv:2603.22650v2 Announce Type: replace Abstract: Active mapping aims to determine how an agent should move to efficiently reconstruct unknown environments. Most existing approaches rely on greedy n

agentsarxiv-cs-cv

Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras

arXiv:2604.18744v1 Announce Type: new Abstract: Event cameras have recently shown promising capabilities in instantaneous motion estimation due to their robustness to low light and fast motions. Howev

researcharxiv-cs-cv

Mesh Memory Protocol: Semantic Infrastructure for Multi-Agent LLM Systems

arXiv:2604.19540v1 Announce Type: cross Abstract: Teams of LLM agents increasingly collaborate on tasks spanning days or weeks: multi-day data-generation sprints where generator, reviewer, and auditor

agentsarxiv-cs-ai

Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving

arXiv:2604.19368v1 Announce Type: new Abstract: Predicting driver intention from neurophysiological signals offers a promising pathway for enhancing proactive safety in advanced driver assistance syst

Tutorials

Modelling and Analysing Behaviours and Emotions via Complex User Interactions

arXiv:1902.07683v1 Announce Type: cross Abstract: Over the past 15 years, the volume, richness and quality of data collected from the combined social networking platforms has increased beyond all expe

tutorialsarxiv-cs-ai

Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge

arXiv:2603.11665v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-a-Judges due to their strong alignment with human judgment across vario

safetyarxiv-cs-cl

NemeSys: Toward Online Underwater Exploration with Remote Operator-in-the-loop Adaptive Autonomy

model-releasesarxiv-cs-ro

arXiv:2507.11889v2 Announce Type: replace Abstract: Adaptive mission control and dynamic parameter reconfiguration are essential for autonomous underwater vehicles (AUVs) operating in GPS-denied, comm

One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization

arXiv:2601.18572v2 Announce Type: replace Abstract: Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfair outcomes ac

safetyarxiv-cs-cl

Local Ai

PolarQuant: Optimal Gaussian Weight Quantization via Hadamard Rotation for LLM Compression

arXiv:2603.29078v2 Announce Type: replace Abstract: We present PolarQuant, a post-training weight quantization method for large language models (LLMs) that exploits the distributional structure of neu

local-aiarxiv-cs-cl

PuzzleWorld: A Benchmark for Multimodal, Open-Ended Reasoning in Puzzlehunts

arXiv:2506.06211v2 Announce Type: replace-cross Abstract: Puzzlehunts are a genre of complex, multi-step puzzles lacking well-defined problem definitions. In contrast to conventional reasoning benchma

Reducing the Offline-Streaming Gap for Unified ASR Transducer with Consistency Regularization

arXiv:2604.19079v1 Announce Type: cross Abstract: Unification of automatic speech recognition (ASR) systems reduces development and maintenance costs, but training a single model to perform well in bo

Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots

arXiv:2604.19104v1 Announce Type: cross Abstract: Developing bipedal football robots in dynamiccombat environments presents challenges related to motionstability and deep coupling of multiple tasks, a

Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients

arXiv:2505.06335v2 Announce Type: replace-cross Abstract: Federated Learning (FL) has the potential for simultaneous global learning amongst a large number of parallel agents, enabling emerging AI suc

agentsarxiv-cs-ai

Local Ai

Self-Improving Tabular Language Models via Iterative Group Alignment

arXiv:2604.18966v1 Announce Type: cross Abstract: While language models have been adapted for tabular data generation, two fundamental limitations remain: (1) static fine-tuning produces models that c

local-aiarxiv-cs-ai

SimDiff: Depth Pruning via Similarity and Difference

arXiv:2604.19520v1 Announce Type: new Abstract: Depth pruning improves the deployment efficiency of large language models (LLMs) by identifying and removing redundant layers. A widely accepted standar

SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Model

arXiv:2604.19710v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models offer a promising autonomous driving paradigm for leveraging world knowledge and reasoning capabilities, especially

StepFly: Agentic Troubleshooting Guide Automation for Incident Diagnosis

arXiv:2510.10074v2 Announce Type: replace Abstract: Effective incident management in large-scale IT systems relies on troubleshooting guides (TSGs), but their manual execution is slow and error-prone.

Tutorials

TabEmb: Joint Semantic-Structure Embedding for Table Annotation

arXiv:2604.18939v1 Announce Type: new Abstract: Table annotation is crucial for making web and enterprise tables usable in downstream NLP applications. Unlike textual data where learning semantically

tutorialsarxiv-cs-lg

TabXEval: Why this is a Bad Table? An eXhaustive Rubric for Table Evaluation

arXiv:2505.22176v3 Announce Type: replace Abstract: Evaluating tables qualitatively and quantitatively poses a significant challenge, as standard metrics often overlook subtle structural and content-l

Talking to a Know-It-All GPT or a Second-Guesser Claude? How Repair reveals unreliable Multi-Turn Behavior in LLMs

arXiv:2604.19245v1 Announce Type: cross Abstract: Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we inv

The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification

arXiv:2604.18728v1 Announce Type: cross Abstract: Many neural network (NN) verification systems represent the network's input-output relation as a constraint program. Sound and complete, representatio

The Rise of Verbal Tics in Large Language Models: A Systematic Analysis Across Frontier Models

arXiv:2604.19139v1 Announce Type: cross Abstract: As Large Language Models (LLMs) continue to evolve through alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) and Constitu

The 'Small World of Words' German Free-Association Norms

arXiv:2604.19620v1 Announce Type: new Abstract: Free-association norms provide essential empirical data for investigating linguistic, semantic, and cultural phenomena in the cognitive sciences. Althou

researcharxiv-cs-cl

Time Series Augmented Generation for Financial Applications

arXiv:2604.19633v1 Announce Type: new Abstract: Evaluating the reasoning capabilities of Large Language Models (LLMs) for complex, quantitative financial tasks is a critical and unsolved challenge. St

TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards

arXiv:2512.07761v3 Announce Type: replace Abstract: Large language models have seen widespread adoption, yet they remain vulnerable to multi-turn jailbreak attacks, threatening their safe deployment.

safetyarxiv-cs-ai

Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length

arXiv:2603.22608v2 Announce Type: replace Abstract: Users often rely on Large Language Models (LLMs) for processing multiple documents or performing analysis over a number of instances. For example, a

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving

arXiv:2411.18275v2 Announce Type: replace Abstract: Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities. However, these models remain

VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation

arXiv:2507.21563v4 Announce Type: replace-cross Abstract: Recommendation systems often suffer from data sparsity caused by limited user-item interactions, which degrade their performance and amplify p

safetyarxiv-cs-lg

WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent

arXiv:2604.17821v2 Announce Type: replace Abstract: Recent advancements in large language models (LLMs) have empowered autonomous web agents to execute natural language instructions directly on real-w

agentsarxiv-cs-ai

When Active Learning Falls Short: An Empirical Study on Chemical Reaction Extraction

arXiv:2604.19335v1 Announce Type: new Abstract: The rapid growth of chemical literature has generated vast amounts of unstructured data, where reaction information is particularly valuable for applica

researcharxiv-cs-lg

When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification

arXiv:2602.11199v2 Announce Type: replace Abstract: Large language models (LLMs) often respond even when prompts omit critical details or include misleading information, leading to hallucinations or r

A High-Accuracy Optical Music Recognition Method Based on Bottleneck Residual Convolutions

arXiv:2604.16446v1 Announce Type: new Abstract: Optical Music Recognition (OMR) aims to convert printed or handwritten music score images into editable symbolic representations. This paper presents an

A Sensitivity Approach to Causal Inference Under Limited Overlap

arXiv:2511.22003v2 Announce Type: replace-cross Abstract: Limited overlap between treated and control groups is a key challenge in observational analysis. Standard approaches like trimming importance

safetyarxiv-cs-lg

A Sugeno Integral View of Binarized Neural Network Inference

arXiv:2604.17967v1 Announce Type: cross Abstract: In this article, we establish a precise connection between binarized neural networks (BNNs) and Sugeno integrals. The advantage of the Sugeno integral

researcharxiv-cs-lg

Tutorials

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions

arXiv:2604.17312v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a powerful post-training paradigm for enhancing the reasoning capabilities of large language models (LLMs). H

tutorialsarxiv-cs-lg

Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization

arXiv:2602.20743v2 Announce Type: replace Abstract: Anonymizing textual documents is a highly context-sensitive problem: the appropriate balance between privacy protection and utility preservation var

Adversarial Arena: Crowdsourcing Data Generation through Interactive Competition

arXiv:2604.17803v1 Announce Type: cross Abstract: Post-training Large Language Models requires diverse, high-quality data which is rare and costly to obtain, especially in low resource domains and for

safetyarxiv-cs-lg

Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis

model-releasesarxiv-cs-cv

arXiv:2604.16729v1 Announce Type: new Abstract: State-of-the-art large language models (LLMs) show high performance in general visual question answering. However, a fundamental limitation remains: cur

Local Ai

An Edge-Host-Cloud Architecture for Robot-Agnostic, Caregiver-in-the-Loop Personalized Cognitive Exercise: Multi-Site Deployment in Dementia Care

arXiv:2604.16408v1 Announce Type: new Abstract: We present Speaking Memories, a distributed, stakeholder-in-the-loop robotic interaction platform for personalized cognitive exercise support. Rather th

local-aiarxiv-cs-ro

Applications

AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models

arXiv:2604.17787v1 Announce Type: new Abstract: Precision-critical manipulation requires both global trajectory organization and local execution correction, yet most vision-language-action (VLA) polic

applicationsarxiv-cs-ro

Answer Only as Precisely as Justified: Calibrated Claim-Level Specificity Control for Agentic Systems

arXiv:2604.17487v1 Announce Type: new Abstract: Agentic systems often fail not by being entirely wrong, but by being too precise: a response may be generally useful while particular claims exceed what

agentsarxiv-cs-cl

Arch: An AI-Native Hardware Description Language for Register-Transfer Clocked Hardware Design

arXiv:2604.05983v2 Announce Type: replace-cross Abstract: We present Arch (AI-native Register-transfer Clocked Hardware), a hardware description language for micro-architecture specification and AI-as

safetyarxiv-cs-cl

Attention-ResUNet for Automated Fetal Head Segmentation

arXiv:2604.18148v1 Announce Type: new Abstract: Automated fetal head segmentation in ultrasound images is critical for accurate biometric measurements in prenatal care. While existing deep learning ap

researcharxiv-cs-cv

Auditing Support Strategies in LLMs through Grounded Multi-Turn Social Simulation

arXiv:2604.17079v1 Announce Type: new Abstract: When users seek social support from chatbots, they disclose their situation gradually, yet most evaluations of supportive LLMs rely on single-turn, full

Authenticated Contradictions from Desynchronized Provenance and Watermarking

arXiv:2603.02378v2 Announce Type: replace-cross Abstract: Cryptographic provenance standards such as C2PA and invisible watermarking are positioned as complementary defenses for content authentication

researcharxiv-cs-cv