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Verifiable Visual Rewards Transfer from Synthetic Scenes to Natural Prompts

arXiv:2609.35641v1 Announce Type: cross Abstract: Precise instruction following in image generation, such as satisfying object counts and spatial relations, remains an open challenge at least in part

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Verifying Neural Networks with Reinforcement Learning

DGX agent

arXiv:2609.34553v1 Announce Type: new Abstract: Formal verification can play a key role in ensuring the reliability of Deep Neural Networks (DNNs) deployed in safety-critical systems. Modern DNN verif

safetyarxiv-cs-lg

29 Sept 2026

Verifying the Linear Representation Hypothesis: How Interpretable Are Vision SAEs?

arXiv:2609.35020v1 Announce Type: new Abstract: Vision Sparse Autoencoders (SAEs) have become a popular tool in Mechanistic Interpretability due to their presumed ability to disentangle complex featur

safetyarxiv-cs-cv

VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation

arXiv:2609.35028v1 Announce Type: new Abstract: Large language models (LLMs) have made misinformation inexpensive to produce but not to verify, creating a growing asymmetry in the information ecosyste

VGGT-Diff: Visual Geometry Meets Diffusion for Sparse-View Novel View Synthesis

arXiv:2609.33253v1 Announce Type: new Abstract: We present VGGT-Diff, a geometry-routed multi-view diffusion model for sparse-view novel view synthesis. Existing novel view synthesis (NVS) methods fac

tutorialsarxiv-cs-cv

ViBR-WM: Visual Bayesian Regression for World Modeling

model-releasesarxiv-cs-cv

arXiv:2609.33844v1 Announce Type: cross Abstract: Modeling temporal dependence and uncertainty is central to forecasting with world models. The Visual Bayesian Regression World Model combines visual f

ViCoR: Reliable Molecular Structure Extraction via Spatially Aligned Verification and Executable Revision

arXiv:2609.33603v1 Announce Type: new Abstract: Reliable optical chemical structure recognition (OCSR) is essential for building high-quality chemical data from scientific literature, yet even small r

applicationsarxiv-cs-cv

VIDEAS: Distilling Explicit Action Semantics from Demonstration Videos for World Models via Prior-Guided Simulation

model-releasesarxiv-cs-ro

arXiv:2609.33464v1 Announce Type: new Abstract: World models learn internal representations of environment dynamics to predict future states, enabling agents to optimize action plans without physical

Video Captioning in Low-Light Conditions through Efficient Uncertainty-Aware Caption Correction

arXiv:2609.31697v1 Announce Type: new Abstract: Low-light conditions can significantly degrade the ability of vision-language models (VLMs) to accurately describe human actions in videos. In this work

applicationsarxiv-cs-cv

Video, Ergo Genero: Unifying Video Tasks via Spatiotemporal Analogy

arXiv:2609.33935v1 Announce Type: new Abstract: Adapting video models to new tasks typically requires dedicated data curation and fine-tuning. While visual analogy provides a training-free alternative

Video-to-Music Generation for Gameplay Videos

arXiv:2609.31810v1 Announce Type: cross Abstract: Video-to-music models have advanced considerably in the last few years, particularly in film and music video applications. In this paper, we investiga

VideoPhysEdit: Physical Counterfactual Video Editing via Rigid-Body Physical Scene Reconstruction

model-releasesarxiv-cs-cv

arXiv:2609.35134v1 Announce Type: new Abstract: Video editing has advanced substantially in recent years, with methods increasingly accounting for the visual consequences of edits, such as changes to

Vision--Language Signals in Constrained RL: Safety Gains Without Anticipation

arXiv:2609.34041v1 Announce Type: new Abstract: Safe reinforcement learning seeks policies that maximise task performance while satisfying safety constraints. In driving benchmarks, however, collision

Agents

Vision-Language Agents for Active Perception in Optics Laboratories

arXiv:2609.32918v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly being used in scientific workflows, but their ability as agents to directly control laboratory experime

agentsarxiv-cs-cv

VisionHOPE: Visual Backbones as Self-Modifying Learning Systems

arXiv:2609.33325v1 Announce Type: new Abstract: Visual backbones have evolved from Convolutional Neural Networks (CNNs) with local aggregation to Vision Transformers (ViTs) with global interactions, S

local-aiarxiv-cs-cv

VisionLogic: Discovering and Grounding Decision-Relevant Visual Concepts

arXiv:2503.10547v3 Announce Type: replace Abstract: Concept-based explanations help users understand vision models through recognizable visual patterns. However, existing methods often rely on correla

VisionPsy-Nano: Improving Accuracy, Efficiency, and Reliability in On-Device Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2609.31746v1 Announce Type: new Abstract: Sub-billion-parameter Vision-Language Models are increasingly viable for on-device deployment, yet compact model size alone does not guarantee usability

VL-AcneSeg: A Vision-Language Framework for Region-Aware Acne Lesion Segmentation

arXiv:2609.34472v1 Announce Type: new Abstract: Acne assessment is crucial for clinical decision-making, yet traditional grading and counting are subjective and fail to account for lesion size. While

Agents

VLM-Guided Experience Replay

arXiv:2602.01915v2 Announce Type: replace Abstract: Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) have enabled powerful semantic and multimodal reasoning capabiliti

agentsarxiv-cs-lg

VPTwin: Real-Sim-Real Video Prediction for Robotic Manipulation Planning

arXiv:2609.33104v1 Announce Type: new Abstract: While action-conditioned video prediction provides an intuitive world model for robotics, purely data-driven predictors often suffer from compounding er

tutorialsarxiv-cs-ro

W2Rep: Learning Visual Representations by Watching the World Change

arXiv:2609.35464v1 Announce Type: new Abstract: Images capture the world at one moment, whereas video reveals how it changes. Image self-supervision learns spatial structure from a single moment, whil

tutorialsarxiv-cs-cv

WALA Learning Executable Latent Actions from Action-Labeled Demonstrations and Action-Free Videos

arXiv:2607.11397v2 Announce Type: replace Abstract: Human videos provide rich information about object manipulation at a scale difficult to reproduce with robots, but often lack action annotations tha

safetyarxiv-cs-ro

WAM-OPD: Sharpening World Action Models via On-Policy Distillation

arXiv:2609.34250v1 Announce Type: new Abstract: Pretrained world action models (WAMs) provide generalist capabilities across diverse robotic manipulation tasks, yet improving target-task performance t

safetyarxiv-cs-ro

Wasserstein Convergence of ODE-Based Samplers in Decentralized Diffusion Model via Velocity Field Decomposition

arXiv:2606.15835v2 Announce Type: replace Abstract: Diffusion models have achieved impressive empirical success in generative tasks, and their convergence theory is now relatively well understood. Mot

Watch the Model Think: On-Policy Extraction of Activation Steering Vectors

arXiv:2602.14143v2 Announce Type: replace Abstract: When a model solves a problem on one attempt and fails it on the next, what separates the two is rarely the final answer token; it is the trajectory

Wavelet-Based Parity Detection Revisited: Representation Dependence, Generalization, and Mechanistic Analysis

arXiv:2511.00071v2 Announce Type: replace Abstract: Parity is exactly determined by the least significant bit (LSB), so machine learning is unnecessary for solving the task. This work instead uses par

WB-WAM: Heterogeneous Body-Hand Pre-training for Humanoid Loco-Manipulation

arXiv:2609.34199v1 Announce Type: new Abstract: Humanoid loco-manipulation demands coordinated body and hand behavior, while conventional robot pre-training data provide limited coverage of such whole

applicationsarxiv-cs-ro

Weighted Spline-Expanded Networks with Distributional Balancing for Continuous Treatment Effects

arXiv:2609.33740v1 Announce Type: cross Abstract: Estimating causal effects with continuous treatments in observational studies is challenging due to confounding, model misspecification, and high-dime

Weighting Schedules Govern What and When Score-Based Generative Models Learn from Multimodal Data

arXiv:2609.35322v1 Announce Type: new Abstract: Score-based generative models generate new samples by integrating a time-dependent drift that carries Gaussian noise onto the target distribution. In pr

tutorialsarxiv-cs-lg

What Can a Leaderboard Certify? Compositional Controllability for Fair Evaluation and Training of Biomedical Literature-Review Agents

arXiv:2609.32318v1 Announce Type: new Abstract: Leaderboards rank long-horizon agents by their final outputs. Yet a higher score alone does not establish whether two systems are comparable or which st

What Do Latent Predictive Vehicle Representations Retain? Measuring State, Geometry, and Local Response

arXiv:2609.32512v1 Announce Type: new Abstract: Models of vehicle dynamics learned from logged states and commands complement physics-based models, and latent world models, which predict in a learned

What Does a ProcGen Generalization Gap Measure? Action Rules, Convergence, and the Missing Random Floor

arXiv:2609.32532v1 Announce Type: new Abstract: A generalization gap in reinforcement learning (return on training levels minus return on held-out levels) is usually reported without a reference point

What Does a Stream Model Buy You in Flow Matching?

arXiv:2609.34123v1 Announce Type: new Abstract: Stream-level flow matching replaces the linear interpolant of conditional flow matching (CFM) by a Gaussian-process (GP) stream connecting each source--

What does FFN compression change downstream? Same-state causal restoration in diffusion language models

arXiv:2609.31685v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable flexible, parallel generation, but their iterative denoising remains computationally expensive, motivating incre

What Does Model Growth Really Add? Functional Capacity Beyond Parameter Count

arXiv:2607.14571v2 Announce Type: replace Abstract: Model growth adds parameters to a trained checkpoint and continues training, aiming to increase capacity while reusing the existing model instead of

What Does the Rank Buy? A Spectral and Distributional Analysis of Low-Rank Adaptation

arXiv:2609.32002v1 Announce Type: cross Abstract: The rank r in LoRA is widely treated as a capacity control: a smaller rank is assumed to yield a simpler model that generalizes better. We show that,

What Makes World Action Models Generalize? An Empirical Study of Test-Time Future Modeling

arXiv:2609.34981v1 Announce Type: new Abstract: World action models (WAMs) predict the future alongside actions during training. Due to the heavy computation cost of video denoising, whether the futur

applicationsarxiv-cs-cv

What masking geometry works best for EEG foundation models?

arXiv:2609.33487v1 Announce Type: new Abstract: EEG foundation models hold promise for scalable brain-signal decoding across clinical and cognitive neuroscience applications, yet their pre-training pi

What Must a World Model Distinguish for Planning?

arXiv:2609.33030v1 Announce Type: new Abstract: World models simulate the consequences of action candidates, but good planning need not preserve every physical distinction required for accurate predic

What Next-Event Accuracy Cannot See: Closed-Loop Evaluation of Emergency Department Trajectory Simulators

arXiv:2609.31635v1 Announce Type: new Abstract: Clinical trajectory models are usually evaluated by next-event accuracy on observed histories. Simulation is different: models must condition on their o

What Paired Evaluations Reveal under Visual Perturbations

arXiv:2609.35583v1 Announce Type: new Abstract: Robustness evaluation must examine diverse visual perturbations, while benchmarks cover only some real-world conditions and physical testing is costly.

safetyarxiv-cs-cv

What Should a Streaming Video Model Remember?

arXiv:2606.16353v2 Announce Type: replace Abstract: Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under

tutorialsarxiv-cs-cv

What Should Data Teach? Moving Bottlenecks Across Circuit, Store, and Use

arXiv:2609.32991v1 Announce Type: new Abstract: What should data teach a language model at a particular point in training? A circuit view reveals three distinct bottlenecks: forming a computation, mak

What Should Federated LoRA Share? FedSAIL via Input-aware Subspace Alignment

arXiv:2609.32485v1 Announce Type: new Abstract: Federated low-rank adaptation (LoRA) requires identifying an update structure that is shared across heterogeneous clients. Prior work reports strong sim

What Should the Reflector See? An Empirical Study of Evidence in Reflective Prompt Optimization

arXiv:2609.32452v1 Announce Type: cross Abstract: Reflective prompt optimization revises instructions using examples of a model's behavior, but which evidence the reflector should receive remains uncl

What Should We Freeze? Guarded Freezing: Connectivity Shapes the Fine-Tuning of Pretrained Models

arXiv:2609.32124v1 Announce Type: new Abstract: When adapting pre-trained models through fine-tuning, freezing weights alone might not preserve performance, as updates elsewhere can change the inputs

Agents

What Stops Recursive Self-Improvement in Robotics? Lessons from 123 Rounds of Agentic Skill Discovery

arXiv:2609.31760v1 Announce Type: cross Abstract: Can a robot improve itself the way coding agents now improve software? We built an agentic system to find out. It watches a robot fail, works out whic

agentsarxiv-cs-lg

What Survives the Codec Shift: Pooled No-Vocals Residuals for Speech Deepfake Detection

arXiv:2609.33375v1 Announce Type: cross Abstract: The transition from vocoder-based to neural-codec speech synthesis makes generalization more difficult for speech deepfake detectors, particularly tho