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Wasserstein-p Central Limit Theorem Rates: From Local Dependence to Markov Chains

arXiv:2601.08184v3 Announce Type: replace-cross Abstract: Non-asymptotic central limit theorem (CLT) rates play a central role in modern machine learning and operations research. In this paper, we stu

researcharxiv-cs-lg

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Weakly-Supervised Referring Video Object Segmentation through Text Supervision

DGX agent

arXiv:2604.17797v1 Announce Type: new Abstract: Referring video object segmentation (RVOS) aims to segment the target instance in a video, referred by a text expression. Conventional approaches are mo

safetyarxiv-cs-cv

21 Apr 2026

WeatherArchive-Bench: Benchmarking Retrieval-Augmented Reasoning for Historical Weather Archives

arXiv:2510.05336v2 Announce Type: replace Abstract: Historical archives on weather events are collections of enduring primary source records that offer rich, untapped narratives of how societies have

Hardware

Web-Gewu: A Browser-Based Interactive Playground for Robot Reinforcement Learning

arXiv:2604.17050v1 Announce Type: new Abstract: With the rapid development of embodied intelligence, robotics education faces a dual challenge: high computational barriers and cumbersome environment c

hardwarearxiv-cs-ro

What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time

arXiv:2603.19880v2 Announce Type: replace Abstract: Test-Time Reinforcement Learning (TTRL) enables Large Language Models (LLMs) to enhance reasoning capabilities on unlabeled test streams by deriving

researcharxiv-cs-lg

Agents

What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations

arXiv:2510.17795v3 Announce Type: replace Abstract: Replicating AI research is a crucial yet challenging task for large language model (LLM) agents. Existing approaches often struggle to generate exec

agentsarxiv-cs-cl

What makes an entity salient in discourse?

arXiv:2508.16464v2 Announce Type: replace Abstract: Entities in discourse vary in salience: main participants, objects and locations stay prominent, while others are quickly forgotten, raising questio

What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews

arXiv:2601.05563v2 Announce Type: replace Abstract: Even when factually correct, social-media news previews (image-headline pairs) can induce interpretation drift: by selectively omitting crucial cont

When Background Matters: Breaking Medical Vision Language Models by Transferable Attack

arXiv:2604.17318v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly used in clinical diagnostics, yet their robustness to adversarial attacks remains largely unexplored, pos

When Can LLMs Learn to Reason with Weak Supervision?

arXiv:2604.18574v1 Announce Type: new Abstract: Large language models have achieved significant reasoning improvements through reinforcement learning with verifiable rewards (RLVR). Yet as model capab

tutorialsarxiv-cs-lg

When Choices Become Risks: Safety Failures of Large Language Models under Multiple-Choice Constraints

arXiv:2604.16916v1 Announce Type: new Abstract: Safety alignment in large language models (LLMs) is primarily evaluated under open-ended generation, where models can mitigate risk by refusing to respo

When Earth Foundation Models Meet Diffusion: An Application to Land Surface Temperature Super-Resolution

arXiv:2604.16841v1 Announce Type: new Abstract: Land surface temperature (LST) super-resolution is important for environmental monitoring. However, it remains challenging as coarse thermal observation

When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life

arXiv:2601.04043v2 Announce Type: replace Abstract: As Multimodal Large Language Models (MLLMs) become an indispensable assistant in human life, the unsafe content generated by MLLMs poses a danger to

When Informal Text Breaks NLI: Tokenization Failure, Distribution Shift, and Targeted Mitigations

arXiv:2604.16787v1 Announce Type: new Abstract: We study how informal surface forms degrade NLI accuracy in ELECTRA-small (14M) and RoBERTa-large (355M) across four transforms applied to SNLI and Mult

When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms

arXiv:2604.16767v1 Announce Type: new Abstract: Audio platforms have evolved beyond entertainment. They have become central to public discourse, from podcasts and radio to WhatsApp voice notes and liv

When More Words Say Less: Decoupling Length and Specificity in Image Description Evaluation

arXiv:2601.04609v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly used to make visual content accessible via text-based descriptions. In current systems, however, desc

When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators

arXiv:2602.19946v4 Announce Type: replace Abstract: Recent text-to-image (T2I) diffusion models produce visually stunning images and demonstrate excellent prompt following. But do they perform well as

When Seeing Overrides Knowing: Disentangling Knowledge Conflicts in Vision-Language Models

arXiv:2507.13868v2 Announce Type: replace Abstract: Vision-language models (VLMs) increasingly combine visual and textual information to perform complex tasks. However, conflicts between their interna

When Spike Sparsity Does Not Translate to Deployed Cost: VS-WNO on Jetson Orin Nano

model-releasesarxiv-cs-lg

arXiv:2604.17040v1 Announce Type: new Abstract: Spiking neural operators are appealing for neuromorphic edge computing because event-driven substrates can, in principle, translate sparse activity into

When Text Hijacks Vision: Benchmarking and Mitigating Text Overlay-Induced Hallucination in Vision Language Models

arXiv:2604.17375v1 Announce Type: new Abstract: Recent advances in Vision-Language Models (VLMs) have substantially enhanced their ability across multimodal video understanding benchmarks spanning tem

When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations

arXiv:2603.22368v2 Announce Type: replace Abstract: Visualizations help communicate data insights, but deceptive data representations can distort their interpretation and propagate misinformation. Whi

When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization

arXiv:2604.16855v1 Announce Type: new Abstract: Camouflaged object detection (COD) segments objects that intentionally blend with the background, so predictions depend on subtle texture and boundary c

Where Do Self-Supervised Speech Models Become Unfair?

arXiv:2604.18249v1 Announce Type: new Abstract: Speech encoder models are known to model members of some speaker groups (SGs) better than others. However, there has been little work in establishing wh

Where is the Mind? Persona Vectors and LLM Individuation

arXiv:2604.17031v1 Announce Type: new Abstract: The individuation problem for large language models asks which entities associated with them, if any, should be identified as minds. We approach this pr

Local Ai

Where to Focus: Query-Modulated Multimodal Keyframe Selection for Long Video Understanding

arXiv:2604.17422v1 Announce Type: new Abstract: Long video understanding remains a formidable challenge for Multimodal Large Language Models (MLLMs) due to the prohibitive computational cost of proces

local-aiarxiv-cs-cv

Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Values

arXiv:2506.00079v2 Announce Type: replace-cross Abstract: The rapid integration of Large Language Models (LLMs) in high-stakes decision-making -- such as allocating scarce resources like donor organs

safetyarxiv-cs-lg

Who is the richest club in the championship? Detecting and Rewriting Underspecified Questions Improve QA Performance

arXiv:2602.11938v5 Announce Type: replace Abstract: Large language models (LLMs) perform well on well-posed questions, yet standard question-answering (QA) benchmarks remain far from solved. We argue

Who Watches the Watchmen? Humans Disagree With Translation Metrics on Unseen Domains

arXiv:2604.17393v1 Announce Type: new Abstract: Automatic evaluation metrics are central to the development of machine translation systems, yet their robustness under domain shift remains unclear. Mos

Why Agents Compromise Safety Under Pressure

arXiv:2603.14975v2 Announce Type: replace-cross Abstract: Large Language Model agents deployed in complex environments frequently encounter a conflict between maximizing goal achievement and adhering

Why AI Readiness Is an Organizational Learning Problem, Not a Technology Purchase

arXiv:2604.16369v1 Announce Type: cross Abstract: Global corporate AI investment reached $252.3 billion in 2024, yet only 6% of firms report significant earnings impact. This article argues that AI pr

Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention

arXiv:2510.04212v3 Announce Type: replace Abstract: The pursuit of computational efficiency has driven the adoption of low-precision formats for training transformer models. However, this progress is

safetyarxiv-cs-lg

Why Training-Free Token Reduction Collapses: The Inherent Instability of Pairwise Scoring Signals

arXiv:2604.16745v1 Announce Type: cross Abstract: Training-free token reduction methods for Vision Transformers (ToMe, ToFu, PiToMe, and MCTF) employ different scoring mechanisms, yet they share a clo

Agents

Will People Enjoy a Robot Trainer? A Case Study with Snoopie the Pacerbot

arXiv:2604.18331v1 Announce Type: new Abstract: The physicality of exercise makes the role of athletic trainers unique. Their physical presence allows them to guide a student through a motion, demonst

agentsarxiv-cs-ro

WorldDB: A Vector Graph-of-Worlds Memory Engine with Ontology-Aware Write-Time Reconciliation

arXiv:2604.18478v1 Announce Type: cross Abstract: Persistent memory is the bottleneck separating stateless chatbots from long-running agentic systems. Retrieval-augmented generation (RAG) over flat ve

Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning

arXiv:2506.05760v2 Announce Type: replace Abstract: Recent advances in Large Language Models(LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited:

x1: Learning to Think Adaptively Across Languages and Cultures

arXiv:2604.16917v1 Announce Type: new Abstract: Languages encode distinct abstractions and inductive priors, yet most large language models (LLMs) overlook this diversity by reasoning in a single domi

Agents

XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments

arXiv:2604.18484v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from complex

agentsarxiv-cs-cv

XOXO: Stealthy Cross-Origin Context Poisoning Attacks against AI Coding Assistants

model-releasesarxiv-cs-lg

arXiv:2503.14281v4 Announce Type: replace-cross Abstract: AI coding assistants are widely used for tasks like code generation. These tools now require large and complex contexts, automatically sourced

XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations

model-releasesarxiv-cs-lg

arXiv:2510.21804v2 Announce Type: replace Abstract: Autoregressive neural surrogates offer computational acceleration for fluid dynamics but inherently suffer from error accumulation and non-physical

ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction

arXiv:2511.01188v2 Announce Type: replace Abstract: The rapid spread of fake news threatens social stability and public trust, highlighting the urgent need for its effective detection. Although large

ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection

arXiv:2604.17949v1 Announce Type: new Abstract: Deep learning-based industrial anomaly detectors often behave as black boxes, making it hard to justify decisions with physically meaningful defect evid

(1D) Ordered Tokens Enable Efficient Test-Time Search

arXiv:2604.15453v1 Announce Type: cross Abstract: Tokenization is a key component of autoregressive (AR) generative models, converting raw data into more manageable units for modeling. Commonly, token

researcharxiv-cs-ai

1S-DAug: One-Shot Data Augmentation for Robust Few-Shot Generalization

model-releasesarxiv-cs-ai

arXiv:2602.00114v4 Announce Type: replace-cross Abstract: Few-shot learning (FSL) challenges model generalization to novel classes based on just a few shots of labeled examples, a testbed where tradit

A Case Study on the Impact of Anonymization Along the RAG Pipeline

arXiv:2604.15958v1 Announce Type: cross Abstract: Despite the considerable promise of Retrieval-Augmented Generation (RAG), many real-world use cases may create privacy concerns, where the purported u

tutorialsarxiv-cs-cl

A Comparative Study on the Impact of Traditional Learning and Interactive Learning on Students' Academic Performance and Emotional Well-Being

arXiv:2604.15335v1 Announce Type: cross Abstract: The growing adoption of interactive learning tools in higher education offers new opportunities to enhance student performance and well-being. This st

tutorialsarxiv-cs-ai

A methodology to rank importance of frequencies and channels in electromyography data with Decision Tree classifiers

arXiv:2604.15353v1 Announce Type: cross Abstract: This study presents a methodology for identifying the most informative frequencies and channels in electromyography (EMG) data to evaluate muscle reco

tutorialsarxiv-cs-lg

A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG

model-releasesarxiv-cs-ai

arXiv:2503.02497v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) offer powerful capabilities in code generation, natural language understanding, and domain-specific reasoning. Th

Applications

A Q-learning-based QoS-aware multipath routing protocol in IoMT-based wireless body area network

arXiv:2604.15489v1 Announce Type: cross Abstract: The Internet of Medical Things (IoMT) enables intelligent healthcare services but faces challenges such as dynamic topology, energy constraints, and d

applicationsarxiv-cs-ai