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arXiv:2610.05230v1 Announce Type: new Abstract: Most vision-language-action models represent future motion as fixed-rate action chunks, tying temporal resolution and prediction horizon to a fixed outp

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Agents

VepAgent: Bridging Causal-Transition via Tool-Augmented Reinforcement Learning for Video Event Prediction

DGX agent

arXiv:2610.06293v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in video understanding, yet their reliance on retrospective summarizatio

agentsarxiv-cs-cv

6 Oct 2026

VERA: Verdict-Conditioned Reliability for Adaptive LLM Judges

arXiv:2610.05452v1 Announce Type: new Abstract: Accurately estimating judgment reliability is a central challenge in adapting LLM judges to newly verified feedback while preserving previously learned

Local Ai

Verifier-Guided Synthetic Augmentation for 3D Human Shape Generation

arXiv:2610.04006v1 Announce Type: new Abstract: Limited training data diversity constrains generative modeling of 3D human bodies: conservative models remain close to observed examples, whereas explor

local-aiarxiv-cs-cv

VGGT-Bridge: Beyond Sequential Pose Graphs via Coarse-Stride Skip Edges

arXiv:2610.06594v1 Announce Type: new Abstract: Feed-forward visual geometry transformers such as VGGT reconstruct dense 3D structure from images in a single forward pass, simplifying multi-view 3D re

VICON: Visual-Inertial-Contact based Hand-Object Tracking for Manipulation Datasets

arXiv:2610.05180v1 Announce Type: new Abstract: Learning dexterous manipulation benefits from human demonstration datasets that capture diverse and natural hand-object interactions. In particular, con

researcharxiv-cs-ro

Video Encoders Built on Image Representations

arXiv:2610.06616v1 Announce Type: new Abstract: The design of a video encoder determines when frames begin to interact and which frame-specific visual evidence remains accessible to the language model

Applications

Video Replanning via Latent Embedding Refinement and Rejection-Based Sampling

arXiv:2510.17315v3 Announce Type: replace Abstract: Video planning has emerged as a flexible framework for robot manipulation, in which a generative model predicts a video of task completion, and a do

applicationsarxiv-cs-ro

Video2World: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos

arXiv:2610.04432v1 Announce Type: new Abstract: Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manu

VideoResearchAgent: Grounded Task Synthesis and Sim-to-Real RL for Open-Web Video Research

arXiv:2610.04911v1 Announce Type: cross Abstract: Existing deep research agents are designed primarily for text- and image-based web sources, while video reasoning systems typically assume that releva

VideoTapestry: Query-Adaptive Memory Refinement for Multi-Agent Long-Video Understanding

arXiv:2610.06672v1 Announce Type: new Abstract: Long-video understanding places substantial demands on memory, as answering questions often requires retrieving information distributed across extended

Applications

Virtual model control for compliant reaching under uncertainties

arXiv:2610.05695v1 Announce Type: new Abstract: Virtual Model Control (VMC) is an approach to design a controller for force-controlled robots in complex uncertain environments. While this method was p

applicationsarxiv-cs-ro

Vision-enabled detection of safety helmet compliance in construction zones

arXiv:2610.05756v1 Announce Type: new Abstract: In the rapidly evolving field of construction management, worker safety remains a top priority. This paper introduces an innovative vision-based system

Hardware

Vision Transformer Ensembles for Panoramic Street Segmentation

arXiv:2610.06063v1 Announce Type: new Abstract: Semantic segmentation of street panoramas can support detailed descriptions of urban environments, yet small datasets and unequal training costs make mo

hardwarearxiv-cs-cv

Visual Grounding Safety in Vision-Language Models

arXiv:2610.05637v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly trained to generate structured outputs like points and bounding boxes that downstream interfaces, agent

Visual Swarm Navigation via Deep Reinforcement Learning and Evolutionary Hybrid Design

arXiv:2610.06400v1 Announce Type: new Abstract: Swarm robotics presents a robust and cost-effective paradigm for advanced automation in complex, dynamic environments, such as those encountered in sear

safetyarxiv-cs-ro

VisualErase: Dual-Branch Visual Trajectory Redirection for Robust Concept Erasure in Text-to-Image Diffusion Models

arXiv:2610.05000v1 Announce Type: new Abstract: Concept erasure is essential for the safe deployment of text-to-image diffusion models, as they may reproduce harmful, copyrighted, or privacy-sensitive

researcharxiv-cs-cv

VLA-ZO: Fast Zeroth-Order Adaptation for Vision-Language-Action Models

arXiv:2610.06271v1 Announce Type: new Abstract: Adapting vision-language-action (VLA) models to deployment-time distribution shifts is important for reliable robotic operation, but conventional first-

researcharxiv-cs-ro

vMF Sentence LDA: A Spherical Topic Model over Sentence Embeddings

arXiv:2610.05095v1 Announce Type: cross Abstract: Latent Dirichlet Allocation (LDA) and models derived from it remain widely used topic models. LDA observes each document as a bag-of-words and models

VolS-GS: Relightable Gaussian Splatting with Volumetric Subsurface Scattering

arXiv:2610.04007v1 Announce Type: new Abstract: We present VolS-GS, a relightable Gaussian splatting framework that reconstructs objects from one-light-at-a-time (OLAT) captures and renders them under

researcharxiv-cs-cv

Voltic: Distinguishing Volatility from Stochasticity in Recurrent Memory

arXiv:2610.05700v1 Announce Type: new Abstract: Recurrent sequence models must decide how strongly to overwrite their memory at each token. Read as Bayesian filtering, this write is the gain of a Kalm

Hardware

WAMJET: A Harness for World Action Model Acceleration

arXiv:2610.03797v1 Announce Type: new Abstract: World Action Models (WAMs) leverage pretrained video foundation models for robot manipulation, but their large backbones and video-action co-prediction

hardwarearxiv-cs-cv

WASP: Weakly Aligned Spatiotemporal Pairs for Fetal Brain MRI-Ultrasound Learning

arXiv:2610.04601v1 Announce Type: new Abstract: Magnetic Resonance Imaging (MRI) is widely regarded as the optimal sensor for fetal brain analysis due to its superior soft-tissue contrast and anatomic

WasserMan: Benchmark for Underwater Manipulation Policy Learning

model-releasesarxiv-cs-ro

arXiv:2610.04536v1 Announce Type: new Abstract: Underwater manipulation couples visual decisions, contact forces and a thruster-controlled floating base. We introduce WasserMan, to our knowledge the f

Watermarking: from Impossibility to Auditable Compliance

arXiv:2610.06317v1 Announce Type: cross Abstract: Article 50 (2) of the EU Artificial Intelligence Act requires providers of generative systems to make synthetic outputs machine-readable and detectabl

safetyarxiv-cs-lg

Watermarks and Fingerprints as Soft Bindings for Content Provenance: An Open-Licence Benchmark for Images, Audio and Video

arXiv:2610.04151v1 Announce Type: new Abstract: Content-provenance standards such as C2PA let a platform recover a stripped manifest through a soft binding: an invisible watermark read from the conten

WaveGSSM: Graph Wave State Space Models for Propagating Spatio-Temporal Patterns

arXiv:2610.06540v1 Announce Type: new Abstract: Spatio-temporal graph models typically encode each snapshot with a GNN and then connect the resulting representations through a temporal module. This sp

researcharxiv-cs-lg

Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection

arXiv:2610.05865v1 Announce Type: new Abstract: Feature pyramid methods, from FPN to BiFPN, have achieved strong performance in face detection by fusing multi-scale features. However, detecting faces

Weight Decay and Neuron Condensation: A Three-Stage Analysis of Two-Layer ReLU Networks

arXiv:2610.04533v1 Announce Type: new Abstract: Weight decay is widely used as a regularization technique in neural network training, yet its role in neuron condensation (parameter direction alignment

What Do Verifiable Rewards Teach Video-Language Models About Time? A Controlled Multi-Model Study

arXiv:2610.03792v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has produced large reasoning gains in language models, and verifiable video benchmarks make it app

What Does a Harness Buy? Tokens, Mostly

arXiv:2610.04433v1 Announce Type: cross Abstract: A coding agent is a language model wrapped in a harness: the system prompt, the tool set, and the context management that turn a chat model into somet

What Does a Harness Repair? A Preregistered Study of Visibility, Baseline Adequacy and Evaluation Defects

arXiv:2610.05533v1 Announce Type: new Abstract: Harness search keeps a change to the prompts, reasoning switches, token budgets or parsers around a frozen model if the change raises a score. Such a ga

What Does an Observability Foundation Model Know?

arXiv:2610.05577v1 Announce Type: new Abstract: A linear probe can show that a label is recoverable from a model's hidden states, but not whether that goes beyond what the input already reveals, or wh

What Does It Cost to Simulate a Quantum Sentence Classifier? An Energy and Compute Perspective on Near-Term QNLP

arXiv:2610.06176v1 Announce Type: cross Abstract: Near-term quantum natural language processing (QNLP) experiments often run on classical simulators, so simulator cost is part of the field's practical

What Gradients Add to Text Leakage in Split Language Models, Counted per Token and per Document

arXiv:2610.04128v1 Announce Type: cross Abstract: Split learning lets a client train a language model on a server without sending its text. The client runs the first layers itself and sends the server

Tutorials

What Matters for Latent Reasoning with Flow Matching

arXiv:2610.06666v1 Announce Type: new Abstract: Latent reasoning lets a large language model (LLM) think in a continuous space and verbalize only the answer. We argue that an effective latent thought

tutorialsarxiv-cs-lg

Agents

What May an Agent Change About Itself? A Containment Floor for Self-Configuring Agent Runtimes

arXiv:2610.06274v1 Announce Type: new Abstract: Many agent runtimes give the agent a tool for editing its own configuration. Some of that configuration grants abilities, such as enabling a tool. Other

agentsarxiv-cs-lg

What the Guard Misses, the Robot Executes: Implied Harm in VLA Instructions

arXiv:2610.05818v1 Announce Type: new Abstract: Vision-language-action models (VLAs) act on instructions without being able to refuse, so screening harmful requests falls to monitors. We test whether

researcharxiv-cs-ro

What to Preserve in Recursive Computation: A Local Predictive Sufficiency Principle

arXiv:2610.04303v1 Announce Type: new Abstract: Recursive computation repeatedly compresses or reuses intermediate states, creating a simple tension: information that must remain useful across longer

What Will Post-Training Fix? Per-Problem Gains Are Shared Across Independent RL Runs, and Existing Checkpoints Predict Them Better Than A Priori Signals

arXiv:2610.04978v1 Announce Type: new Abstract: Data selection, curricula and the evaluation of post-training recipes all assume that we can tell, before training, which problems a model will improve

Applications

When and What to Prune? Stage-Aware Visual Token Pruning for Efficient VLA

arXiv:2610.05273v1 Announce Type: new Abstract: Visual token pruning is an effective way to accelerate vision-language models and is especially useful for vision-language-action (VLA) inference, where

applicationsarxiv-cs-cv

When Are Concept Bottleneck Model Explanations Faithful and Compact?

arXiv:2610.06285v1 Announce Type: new Abstract: Concept bottleneck models (CBMs) are neural classifiers that allow to explain their decisions via high-level concepts, potentially enabling understandin

researcharxiv-cs-lg

Tutorials

When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation

arXiv:2605.05592v2 Announce Type: replace Abstract: Increasing a voting budget can improve or reduce population accuracy, and an initial gain or loss need not persist. We give a complete structural cl

tutorialsarxiv-cs-lg

Agents

When Debate Helps: Proposal Supply and Verification-Aware Readout in Multi-Agent Reasoning

arXiv:2610.04686v1 Announce Type: cross Abstract: Multi-agent debate can improve reasoning, yet often fails to beat simple majority voting. We argue that successful debate requires two distinct mechan

agentsarxiv-cs-lg

Tutorials

When Does a Diffusion Model Decide What to Draw ?

arXiv:2610.05645v1 Announce Type: new Abstract: A diffusion model starts from pure noise and removes it step by step. Somewhere along the way it stops being able to become 'anything' and becomes commi

tutorialsarxiv-cs-lg

When Does Longer Reasoning Help? Predicting Mathematical Reasoning Through Discovery and Execution

arXiv:2610.05322v1 Announce Type: new Abstract: Test-time compute can improve mathematical reasoning, but can short-budget runs predict how mathematical reasoning scales with additional compute? We in

When Does Retrieval Help? A Study of In-Context Adaptation in Vision-Language-Action Models

arXiv:2610.05492v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong potential as generalist robot policies, but adapting them to unseen tasks often requires costly pa

When Is Enough Enough in Self-Evolving LLM Systems?

arXiv:2610.04756v1 Announce Type: cross Abstract: Self-evolving large language model (LLM) systems repeatedly propose, evaluate, and incorporate updates to prompts, skills, or other persistent artifac