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Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining

arXiv:2605.14747v1 Announce Type: cross Abstract: Recent advances in multimodal large language models have driven growing interest in graphical user interface (GUI) agents, yet their generalization re

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Human DGX agent

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ViMU: Benchmarking Video Metaphorical Understanding

DGX agent

arXiv:2605.14607v1 Announce Type: new Abstract: Any new medium, once it emerges, is used for more than the transmission of overt content alone. The information it carries typically operates on two lev

model-releasesarxiv-cs-cv

15 May 2026

Vision-Based Runtime Monitoring under Varying Specifications using Semantic Latent Representations

model-releasesarxiv-cs-cv

arXiv:2605.13923v1 Announce Type: cross Abstract: We study certified runtime monitoring of past-time signal temporal logic (ptSTL) from visual observations under partial observability. The monitor mus

Vision-Based Water Level and Flow Estimation

arXiv:2605.14645v1 Announce Type: cross Abstract: With the rapid evolution of computer vision, vision-based methodologies for water level and river surface velocity estimation have reached significant

Vision-Core Guided Contrastive Learning for Balanced Multi-modal Prognosis Prediction of Stroke

arXiv:2605.14710v1 Announce Type: cross Abstract: Deep learning and multi-modal fusion have demonstrated transformative potential in medical diagnosis by integrating diverse data sources. However, acc

safetyarxiv-cs-ai

Vision-LLMs for Spatiotemporal Traffic Forecasting

arXiv:2510.11282v2 Announce Type: replace Abstract: Accurate spatiotemporal traffic forecasting is a critical prerequisite for proactive resource management in dense urban mobile networks. While large

safetyarxiv-cs-lg

Viverra: Text-to-Code with Guarantees

arXiv:2605.14972v1 Announce Type: cross Abstract: A fundamental limitation of Text-to-Code is that no guarantee can be obtained about the correctness of the generated code. Therefore, to ensure its co

safetyarxiv-cs-ai

VLRS-Bench: A Vision-Language Reasoning Benchmark for Remote Sensing

arXiv:2602.07045v2 Announce Type: replace-cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have enabled complex reasoning. However, existing remote sensing (RS) benchmar

VMU-Diff: A Coarse-to-fine Multi-source Data Fusion Framework for Precipitation Nowcasting

arXiv:2605.14597v1 Announce Type: new Abstract: Precipitation nowcasting is a vital spatio-temporal prediction task for meteorological applications but faces challenges due to the chaotic property of

researcharxiv-cs-cv

Wahkon: A Statistically Principled Deep RKHS Superposition Network

arXiv:2605.14041v1 Announce Type: cross Abstract: Deep learning excels at prediction but often lacks finite-sample guarantees and calibrated uncertainty; RKHS (Reproducing Kernel Hilbert Space)-based

WARD: Adversarially Robust Defense of Web Agents Against Prompt Injections

arXiv:2605.15030v1 Announce Type: cross Abstract: Web agents can autonomously complete online tasks by interacting with websites, but their exposure to open web environments makes them vulnerable to p

WarmPrior: Straightening Flow-Matching Policies with Temporal Priors

arXiv:2605.13959v1 Announce Type: cross Abstract: Generative policies based on diffusion and flow matching have become a dominant paradigm for visuomotor robotic control. We show that replacing the st

Warp-as-History: Generalizable Camera-Controlled Video Generation from One Training Video

arXiv:2605.15182v1 Announce Type: new Abstract: Camera-controlled video generation has made substantial progress, enabling generated videos to follow prescribed viewpoint trajectories. However, existi

safetyarxiv-cs-cv

Watch your neighbors: Training statistically accurate chaotic systems with local phase space information

arXiv:2605.14405v1 Announce Type: new Abstract: Chaotic systems pose fundamental challenges for data-driven dynamics discovery, as small modeling errors lead to exponentially growing trajectory discre

local-aiarxiv-cs-lg

Watermarking Game-Playing Agents in Perfect-Information Extensive-Form Games

arXiv:2605.14283v1 Announce Type: cross Abstract: Watermarking techniques for large language models (LLMs), which encode hidden information in the output so its source can be verified, have gained sig

Wavelet-Based Observables for Koopman Analysis: An Extended Dynamic Mode Decomposition Framework

arXiv:2605.14224v1 Announce Type: cross Abstract: We present an in-depth analysis of the Koopman semigroup via wavelet transform. Towards this goal, we start by introducing the wavelet-based observabl

Web Agents Should Adopt the Plan-Then-Execute Paradigm

arXiv:2605.14290v1 Announce Type: cross Abstract: ReAct has become the default architecture across LLM agents, and many existing web agents follow this paradigm. We argue that it is the wrong default

What Do AI Agents Talk About? Discourse and Architectural Constraints in the First AI-Only Social Network

model-releasesarxiv-cs-cl

arXiv:2603.07880v5 Announce Type: replace Abstract: Moltbook is the first large-scale social network built for autonomous AI agent-to-agent interaction. Early studies on Moltbook have interpreted its

Tutorials

What Do EEG Foundation Models Capture from Human Brain Signals?

arXiv:2605.11410v2 Announce Type: replace Abstract: Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, e.g., band power, connectivity, complexit

tutorialsarxiv-cs-ai

Applications

What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions

arXiv:2605.14422v1 Announce Type: new Abstract: Time series forecasting has become increasingly critical in real-world scenarios, where future sequences are influenced not only by historical patterns

applicationsarxiv-cs-lg

Applications

What Makes Words Hard? Sakura at BEA 2026 Shared Task on Vocabulary Difficulty Prediction

arXiv:2605.14257v1 Announce Type: new Abstract: We describe two types of models for vocabulary difficulty prediction: a high-accuracy black-box model, which achieved the top shared task result in the

applicationsarxiv-cs-cl

When Answers Stray from Questions: Hallucination Detection via Question-Answer Orthogonal Decomposition

arXiv:2605.14449v1 Announce Type: cross Abstract: Hallucination detection in large language models (LLMs) requires balancing accu racy, efficiency, and robustness to distribution shift. Black-box cons

When Are Two Networks the Same? Tensor Similarity for Mechanistic Interpretability

arXiv:2605.15183v1 Announce Type: new Abstract: Mechanistic interpretability aims to break models into meaningful parts; verifying that two such parts implement the same computation is a prerequisite.

When Evidence Conflicts: Uncertainty and Order Effects in Retrieval-Augmented Biomedical Question Answering

arXiv:2605.14115v1 Announce Type: new Abstract: Biomedical retrieval-augmented large language models (LLMs) often face evidence that is incomplete, misleading, or internally contradictory, yet evaluat

researcharxiv-cs-cl

When Retrieval Hurts Code Completion: A Diagnostic Study of Stale Repository Context

arXiv:2605.14478v1 Announce Type: cross Abstract: Context: Retrieval-augmented code generation relies on cross-file repository context, but retrieved snippets may come from obsolete project states. Ob

When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution

arXiv:2605.14504v1 Announce Type: new Abstract: Long-horizon household tasks demand robust high-level planning and sustained reasoning capabilities, which are largely overlooked by existing embodied A

Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement

arXiv:2605.14368v1 Announce Type: cross Abstract: Continuous diffusion language models lag behind autoregressive transformers, partly because diffusion is applied in spaces poorly suited to language d

Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control

arXiv:2512.06471v2 Announce Type: replace-cross Abstract: Goal-conditioned reinforcement learning (RL) concerns the problem of training an agent to maximize the probability of reaching target goal sta

Why Neighborhoods Matter: Traversal Context and Provenance in Agentic GraphRAG

arXiv:2605.15109v1 Announce Type: new Abstract: Retrieval-Augmented Generation can improve factuality by grounding answers in external evidence, but Agentic GraphRAG complicates what it means for cita

Why Retrieval-Augmented Generation Fails: A Graph Perspective

arXiv:2605.14192v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become a powerful and widely used approach for improving large language models by grounding generation in ret

Widening the Gap: Exploiting LLM Quantization via Outlier Injection

arXiv:2605.15152v1 Announce Type: cross Abstract: LLM quantization has become essential for memory-efficient deployment. Recent work has shown that quantization schemes can pose critical security risk

local-aiarxiv-cs-ai

WikiCLIP: An Efficient Contrastive Baseline for Open-domain Visual Entity Recognition

arXiv:2603.09921v3 Announce Type: replace Abstract: Open-domain visual entity recognition (VER) seeks to associate images with entities in encyclopedic knowledge bases such as Wikipedia. Recent genera

researcharxiv-cs-cv

Winning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm

arXiv:2605.13979v1 Announce Type: cross Abstract: Quantum machine learning (QML) aims to accelerate machine learning tasks by exploiting quantum computation. Previous work studied a QML algorithm for

Hardware

Woodelf++: A Fast and Unified Partial Dependence Plot Algorithm for Decision Tree Ensembles

arXiv:2605.14578v1 Announce Type: new Abstract: Partial Dependence Plots (PDPs) visualize how changes in a single feature affect the average model prediction. They are widely used in practice to inter

hardwarearxiv-cs-lg

XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles

arXiv:2605.13922v1 Announce Type: cross Abstract: During the last few years, the term Mechanistic Interpretability, a specific area, under the umbrella of explainable artificial intelligence (XAI), ha

XDomainBench: Diagnosing Reasoning Collapse in High-Dimensional Scientific Knowledge Composition

arXiv:2605.14754v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed for knowledge synthesis, yet their capacity for compositional generalization in scientific knowle

Hardware

XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference

arXiv:2605.14844v1 Announce Type: cross Abstract: We introduce XFP, a dynamic weight quantizer for LLM inference that inverts the conventional workflow: the operator specifies reconstruction quality f

hardwarearxiv-cs-ai

XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations

arXiv:2511.02776v2 Announce Type: replace Abstract: Recent progress in large-scale robotic datasets and vision-language models (VLMs) has advanced research on vision-language-action (VLA) models. Howe

safetyarxiv-cs-ro

You Only Landmark Once: Lightweight U-Net Face Super Resolution with YOLO-World Landmark Heatmaps

arXiv:2605.14166v1 Announce Type: new Abstract: Face image super-resolution aims to recover high-resolution facial images from severely degraded inputs. Under extreme upscaling factors, fine facial de

local-aiarxiv-cs-cv

Your CLIP has 164 dimensions of noise: Exploring the embeddings covariance eigenspectrum of contrastively pretrained vision-language transformers

arXiv:2605.14893v1 Announce Type: cross Abstract: Contrastively pre-trained Vision-Language Models (VLMs) serve as powerful feature extractors. Yet, their shared latent spaces are prone to structural

3D Primitives are a Spatial Language for VLMs

arXiv:2605.12586v1 Announce Type: cross Abstract: Vision-language models (VLMs) exhibit a striking paradox: they can generate executable code that reconstructs a 3D scene from geometric primitives wit

3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots

arXiv:2605.12689v1 Announce Type: new Abstract: In this paper, we present a novel hybrid approach that combines Reinforcement Learning (RL) with Dynamic Window Approach (DWA) for adaptive 3D local nav

local-aiarxiv-cs-ro

3D-UIR: 3D Gaussian for Underwater 3D Scene Reconstruction via Physics Based Appearance-Medium Decoupling

arXiv:2505.21238v3 Announce Type: replace Abstract: Novel view synthesis for underwater scene reconstruction presents unique challenges due to complex light-media interactions. Optical scattering and

researcharxiv-cs-cv

A Benchmark for Multi-Party Negotiation Games from Real Negotiation Data

arXiv:2603.14066v2 Announce Type: replace-cross Abstract: Many real-world multi-party negotiations unfold as sequences of binding, action-level commitments rather than a single final outcome, yet this

A Constraint Programming Approach for n-Day Lookahead Playoff Clinching

arXiv:2605.13142v1 Announce Type: new Abstract: In professional sports, a team has clinched the playoffs if they are guaranteed a postseason spot, regardless of the outcomes of any remaining games. As

A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline

arXiv:2605.12608v1 Announce Type: new Abstract: Object detection in adverse weather is critical for the safety of autonomous vehicles; however, the scarcity of labelled, real-world foggy data remains

safetyarxiv-cs-cv

A Faster Generalized Two-Stage Approximate Top-K

arXiv:2506.04165v3 Announce Type: replace Abstract: We consider the Top-K selection problem, which aims to identify the largest K elements in an array. Top-K selection arises in many machine learning

A Five-Layer MLOps Architecture for Connected Automated Driving

arXiv:2605.12719v1 Announce Type: cross Abstract: The continual assurance of safety and performance of automated driving systems (ADSs) poses significant challenges. ADSs operate in complex, dynamic,

safetyarxiv-cs-lg