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Weave Forcing: Compositional Memory Routing for Interactive Long Video Generation

arXiv:2610.03510v1 Announce Type: cross Abstract: Recent advances in autoregressive video generation have improved temporal consistency over extended durations, yet interactive storytelling requires m

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Model Releases

WebFovea: When the Model Is Right but the Click Is Wrong -- Reliable Round Trips for Vision-Based Web Agents on Live Websites

DGX agent

arXiv:2610.03036v1 Announce Type: cross Abstract: We present WebFovea, a vision-based web agent that placed 2nd in the WebRetriever Challenge 2026 with a final score of 57.0 out of 100. The challenge

model-releasesarxiv-cs-ai

5 Oct 2026

WebUIProof: Benchmarking WebUI Code Generators with UI-Agent Execution Harness

arXiv:2610.02617v1 Announce Type: cross Abstract: Evaluating WebUI code generation at scale is difficult: outputs may compile and look plausible yet fail under user interaction, and prior benchmarks l

What Does a Token Cost? A Mixture-of-Agents Measurement of Sufficient Per-Token Compute

arXiv:2610.02491v1 Announce Type: new Abstract: Large language models spend the same amount of computation on every token they generate, regardless of how difficult each token is to produce. Methods s

What Is Lost in Post-Training? Default Collapse and the Loss of In-Context Steerability Across Diverse Perspectives

arXiv:2610.02614v1 Announce Type: new Abstract: AI models serving a heterogeneous population must act on the principles appropriate to each user and context. While post-training has been shown to narr

What Should World Models Forget? Stratified Retention for Continual Adaptation

arXiv:2610.03713v1 Announce Type: cross Abstract: Continual learning treats degradation on previously seen data as evidence of failure, a convention inherited from settings with a stationary predictio

researcharxiv-cs-ai

When a Correct Reward Is Not Enough: Diagnosing and Guiding PPO in an Analytically Solved Broker-Trader Game

arXiv:2610.03598v1 Announce Type: cross Abstract: Reinforcement learning (RL) is increasingly used for financial optimal-control problems when complex dynamics make analytical strategies difficult to

When Can We Trust the Matching Principle? Robust Deployment Geometry Under Finite-Sample and Model Uncertainty

arXiv:2610.02894v1 Announce Type: cross Abstract: Match only geometry you can identify; otherwise spread the penalty. We quantify that decision by the trust ratio tau = epsilon / gamma (estimation unc

When Does Pooling Pay? Credibility and Resolution under Forgetting in Intermittent-Demand Forecasting

arXiv:2511.12749v3 Announce Type: replace-cross Abstract: Forecasting many sparse series requires two choices: how much of each series' own past to retain, and how much to borrow from other series. We

When Does Synthetic Relational Data Teach Models to Use Relations? Tracing Predictive Structure from Pretraining Data to Model Behavior

arXiv:2610.03057v1 Announce Type: new Abstract: Relational foundation models are increasingly pretrained on synthetic databases, yet downstream benchmarks reveal little about why one synthetic corpus

local-aiarxiv-cs-lg

When History Fails to Become Experience: Action Calibration in Language Agents

arXiv:2610.02769v1 Announce Type: cross Abstract: Language agents should draw on prior attempts and environmental feedback to improve subsequent decisions within the same task. However, providing addi

tutorialsarxiv-cs-ai

When Is Accuracy Evidence? A Unified Theory of Generalisation, Validation, and Information Fusion

arXiv:2610.03465v1 Announce Type: cross Abstract: K-fold cross-validation (CV) is widely used as evidence of out-of-sample performance, although folds are neither independent experiments nor equally i

When May a Bandit Leave Its Anchor? E-Process-Authorized Thompson Sampling under Non-stationarity

arXiv:2610.03646v1 Announce Type: new Abstract: Stationarity rewards memory, but after a change the same history can mislead. We ask when forgetting should be permitted. E-process-authorized Thompson

When Normalization Selects the Sign: Auditing Robustness Ablations in Quantum Attention

arXiv:2610.02641v1 Announce Type: cross Abstract: Removing an input-scaling module changes both a classifier and the perturbations reaching its encoder. A robustness difference can therefore reflect t

Agents

When Numbers Start Talking: Numerical Signalling and Strategic Behaviour Among LLMs

arXiv:2610.03033v1 Announce Type: new Abstract: Large language model (LLM)-based agents increasingly operate in multi-agent systems (MAS) characterised by strategic interaction. However, little is kno

agentsarxiv-cs-ai

When Predicting Nothing Beats SAM 3: Revisiting Evaluation in Video Object Segmentation

model-releasesarxiv-cs-cv

arXiv:2610.02946v1 Announce Type: new Abstract: Video Object Segmentation (VOS) in complex and long videos is increasingly important for real-world applications, where target objects often appear only

When Terminal-Agent Training Stalls: Demystifying Data Generation and Verification Challenge

arXiv:2610.02405v1 Announce Type: new Abstract: Using a frontier model like Claude Opus as a meta-agent to generate terminal tasks and verifiers for RL training is increasingly common. Yet a runnable

Agents

When to Compile a Computer-Use Agent? Measuring Payback and Making Compilation Decisions for Token Efficiency

arXiv:2610.02932v1 Announce Type: new Abstract: Compiling GUI procedures that agents execute repeatedly into programs can reduce their token costs. However, measuring payback and deciding when to comp

agentsarxiv-cs-ai

Where Quantum Fourier Sampling Stops Short: A Three-Gate Audit Protocol for Delay-PUF Security Models

arXiv:2610.02636v1 Announce Type: cross Abstract: Quantum Fourier sampling may help audit the spectral learnability of delay-based physical unclonable functions (PUFs). We ask whether that promise sur

safetyarxiv-cs-lg

Where to Look Is Not How to Fix: Pre-Denoising Diagnostics and Modality-Dependent Control in Diffusion Composition

arXiv:2610.03068v1 Announce Type: new Abstract: Understanding compositional failures in text-to-image diffusion requires identifying both where stress is detectable and how intervention changes the ou

tutorialsarxiv-cs-cv

Who Went Where When on the Lunar Surface: Forensic Trajectory Analysis to Identify Byzantine Rovers

arXiv:2610.02694v1 Announce Type: new Abstract: Future planetary surface missions are likely to involve multiple independently operated rovers sharing the same deployment region, raising the need to v

safetyarxiv-cs-ro

Why Does Adaptive Batching Help LLM Pretraining? A Perspective from Unbounded Variance

arXiv:2610.02355v1 Announce Type: new Abstract: Increasing the batch size during training is a common practice in large language model (LLM) pretraining, yet the theoretical justification behind its s

Hardware

Windfoil: Closed-Form Coverage for Real-Time and Differentiable Vector Graphics

arXiv:2610.02468v1 Announce Type: cross Abstract: We present Windfoil, a GPU-friendly algorithm that treats rasterisation and differentiable vector graphics as two sides of the same problem by evaluat

hardwarearxiv-cs-cv

World Action Learning via Interaction-Centric Spectral Latent Guidance

arXiv:2610.03607v1 Announce Type: new Abstract: Learning general-purpose robot policies requires large-scale real-world interaction data, yet collecting robot demonstrations remains expensive and diff

tutorialsarxiv-cs-ro

World Action Modeling with Progressive Visual Planning

arXiv:2610.02508v1 Announce Type: new Abstract: World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an ini

World-Calibrated Proposal-to-Action Flow for Vision-Language-Action Models

arXiv:2610.02323v1 Announce Type: cross Abstract: Flow-based Vision-Language-Action (VLA) policies generate action chunks by transporting samples from a task-agnostic isotropic Gaussian source. As thi

local-aiarxiv-cs-cv

World Editing: Intervening on Executable Worlds at Increasing Depth

arXiv:2610.02331v1 Announce Type: new Abstract: Interactive world models are increasingly capable of generating environments and acting within them, yet deliberately editing an existing executable wor

World Embedding Benchmark

model-releasesarxiv-cs-cl

arXiv:2610.03632v1 Announce Type: cross Abstract: Physical fidelity has received increasing attention in world models and video generation, yet how video representations encode physical information re

Writerslogic at PAN 2026: Process over Content for Robust Detection under Domain Shift

arXiv:2610.03565v1 Announce Type: new Abstract: We describe the Writerslogic systems for three PAN at CLEF 2026 shared tasks (Reasoning Trajectory Detection, Voight-Kampff Generative AI Detection, and

local-aiarxiv-cs-cl

Writerslogic at the CLEF 2026 SimpleText Track: Multi-Candidate LLM Simplification and Stacked Complexity Spotting

model-releasesarxiv-cs-cl

arXiv:2610.03567v1 Announce Type: new Abstract: We describe the Writerslogic team's participation in the CLEF 2026 SimpleText shared task, addressing Task 1 (text simplification) and Task 2 (complexit

Wrong Organ, Right Physics: Transferring Echocardiography Pretraining to Lung Ultrasound for Tuberculosis Screening

arXiv:2610.03290v1 Announce Type: cross Abstract: Lung ultrasound (LUS) is attractive for tuberculosis (TB) screening at primary-care level, but labelled cohorts are small. Echocardiography carries no

researcharxiv-cs-cv

XGenAct: Geometry-Enhanced World Action Models through Cross-Task Generation

arXiv:2610.03516v1 Announce Type: cross Abstract: World action models (WAMs) have advanced robot control by predicting how observations and actions evolve over time. Despite this progress, RGB and act

tutorialsarxiv-cs-cv

ZAGNet: Zone-Aware Graph Aggregation Network for Patient-Level Lung Ultrasound Diagnosis

arXiv:2610.02263v1 Announce Type: cross Abstract: Patient-level lung ultrasound (LUS) diagnosis requires integrating findings acquired across multiple anatomical zones, yet clinical examinations frequ

researcharxiv-cs-cv

ZeBROD: Zero-Retraining Based Recognition and Object Detection Framework

arXiv:2512.04888v4 Announce Type: replace Abstract: Object detection constitutes the primary task within the domain of computer vision. It is utilized in numerous domains. Nonetheless, object detectio

local-aiarxiv-cs-cv

Zephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines

arXiv:2610.03087v1 Announce Type: cross Abstract: Deterministic data loading is important for foundation model development: model researchers need confidence that differences they observe across costl

ZeroMAG: Zero-Shot Multimodal Adapter Generation for Plug-and-Play EEG Foundation Models

arXiv:2610.03546v1 Announce Type: new Abstract: EEG foundation models (EFMs) capture reusable knowledge from large-scale EEG data, while many EEG recordings also include companion physiological signal

Applications

3DROID: A Renderable 3D Gaussian Dataset with Measured Per-Scene Reliability

arXiv:2610.01744v1 Announce Type: new Abstract: Robot manipulation models primarily reason from 2D observations while acting in the 3D physical world. To bridge this gap, recent work has augmented rob

applicationsarxiv-cs-cv

Applications

4Director: Controlling Video World Models with Rigid 3D Geometry

arXiv:2610.02160v1 Announce Type: new Abstract: Precise control over camera and object motion is essential for professional video production. Existing methods control objects only coarsely, through im

applicationsarxiv-cs-cv

A Citation-Grounded Benchmark for Trustworthy Earnings Call Transcript Analysis with Large Language Models

arXiv:2610.00969v1 Announce Type: cross Abstract: Large language models (LLMs) have been increasingly used for financial document analysis, including earnings call transcripts (ECTs). Beyond generatin

A Compact Explicit 4D Representation for Dynamic Scenes

arXiv:2610.01229v1 Announce Type: new Abstract: A compact dynamic-scene representation must retain both the surfaces seen over time and the appearance needed to render them from new viewpoints. We pre

researcharxiv-cs-cv

A Comparative Explainability Framework for DeBERTa-v3 in Zero-Shot Medical Abstract Classification

arXiv:2610.02116v1 Announce Type: new Abstract: A comparative explainability framework is presented to audit DeBERTa-v3 under zero-shot classification of medical abstracts. The work addresses the disa

researcharxiv-cs-ai

A Comprehensive Review of One-Pixel Attack: Research Status, Taxonomy, Applications, Regulation Policy and Future Directions

arXiv:2610.00125v1 Announce Type: cross Abstract: One-Pixel Attacks (OPAs) represent one of the most extreme demonstrations of adversarial fragility in deep learning, where modifying a single pixel ca

safetyarxiv-cs-cv

A Deterministic and Auditable AI Security Risk Assessment Framework with ATLAS Aligned Executable Rules and Formal Verification

arXiv:2610.01436v1 Announce Type: new Abstract: Artificial intelligence systems are increasingly deployed in high impact and safety critical settings, yet security assessment remains difficult to repr

A First Glance at Jev for Network Traffic Classification: Accuracy, Processing Time, and Cost

model-releasesarxiv-cs-lg

arXiv:2610.00376v1 Announce Type: new Abstract: We evaluate Jev on ten dataset-defined application labels in CESNET-QUICEXT-25 using only the first ten packets' sizes, directions, and inter-packet tim

A foundation for systematic analysis of transformers and RNNs for tractography

arXiv:2610.01894v1 Announce Type: new Abstract: Machine learning (ML) has emerged as a promising approach for improving diffusion MRI (dMRI) tractography, a task that remains limited by the intrinsic

Agents

A framework for auditing grounding claims

arXiv:2512.06205v3 Announce Type: replace Abstract: The symbol grounding problem asks how a token such as cat can be about cats. We propose a framework for auditing grounding claims against a declared

agentsarxiv-cs-ai

A Framework for Egocentric and Exocentric Procedural Understanding via Temporal Segmentation and Semantic Abstraction

arXiv:2610.00069v1 Announce Type: cross Abstract: Long-horizon ego/exo data contains rich procedural evidence, but are redundant, noisy, and costly to process or retain. We propose a compact framework

local-aiarxiv-cs-ai

Applications

A Hybrid Approach to Malware Detection: Integrating Few-Shot Model-Agnostic Meta-Learning with Autoencoders

arXiv:2610.01949v1 Announce Type: cross Abstract: Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are t

applicationsarxiv-cs-ai