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VesselBench-800K: A Large-scale Perception Benchmark for Multimodal Vessel Detection, Counting, and Density Estimation

arXiv:2609.37003v1 Announce Type: new Abstract: Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most ex

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Human DGX agent

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Safety

VidAct: Learning Manipulation from In-the-Wild Videos with Object-Centric 3D Awareness

DGX agent

arXiv:2609.36870v1 Announce Type: new Abstract: Video demonstrations offer a scalable alternative to costly robot data for learning manipulation, yet existing reconstruction-based approaches often rel

safetyarxiv-cs-ro

30 Sept 2026

Agents

Video-RSI: Recursive Self-Improvement of Video Understanding Agents via Harness Evolution

arXiv:2609.37950v1 Announce Type: new Abstract: Video understanding agents acquire evidence through an executable harness that controls what they observe and how they use those observations. However,

agentsarxiv-cs-ai

Video2Skill: From Streaming Experience to Reusable Embodied Skills

model-releasesarxiv-cs-cl

arXiv:2609.36691v1 Announce Type: new Abstract: Manipulation behaviors vary widely across objects and scenes, but they share a small set of reusable skills, and planning with these skills helps embodi

Video2STL: Grounding VLM-Generated Temporal Specifications for Robot Learning

arXiv:2609.37519v1 Announce Type: cross Abstract: Video-based policy learning is particularly promising, as it illustrates target behaviors without requiring action annotations or embodiment-matched d

VideoLoop: Looped Working Memory Against Semantic Thrashing in Long-Form Video Agents

arXiv:2609.38119v1 Announce Type: new Abstract: Long-form video understanding requires multimodal agents to iteratively gather evidence over many reasoning steps. However, most existing agentic method

VIF-Bench: Evaluating Visual Instruction Following in Multi-Reference Image Generation

arXiv:2609.37709v1 Announce Type: cross Abstract: Recent multimodal image generation models can take multiple images and textual instructions as input, enabling reference-based generation guided not o

VISTA-Bench: Benchmarking Multilingual Image Translation with Image-Specific Rubrics

arXiv:2609.37287v1 Announce Type: cross Abstract: Image translation is a fundamental capability of multimodal models for multilingual applications, requiring visual understanding and meaning preservat

VISTA: Internalizing Collective Visual Experience via On-Policy Distillation for Active Multimodal Agents

arXiv:2609.38086v1 Announce Type: new Abstract: Active multimodal agents use visual tools to acquire task-relevant evidence while reasoning. Although reinforcement learning samples multiple interactio

VISTA: Value-Informed Event Appraisal for Multimodal Emotion Conflict

arXiv:2609.37324v1 Announce Type: new Abstract: Conflicting emotional cues can be individually valid: a subdued voice may reflect a blocked goal while a smile satisfies a social obligation. Their inte

researcharxiv-cs-ai

Applications

Visual Anomaly Synthesis for Model Selection in Data Scarcity

arXiv:2609.37360v1 Announce Type: new Abstract: Defect detection systems for industrial condition monitoring can only be relied upon if they are validated, yet defective samples are rare and, for a sp

applicationsarxiv-cs-cv

Visual Branch is What You Need for CLIP-based Class-Incremental Learning

arXiv:2609.37888v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) requires models to recognize new classes over time without forgetting previously learned ones. With the rise of vision-

researcharxiv-cs-cv

Visual Parallel Search: Learning to Search High-Resolution Images with Parallel Tile Inspection and Adaptive Zoom

arXiv:2609.37002v1 Announce Type: cross Abstract: High-resolution visual question answering often fails because a multimodal model does not acquire the small, spatially localized evidence needed to an

Visual sensitivity is not claim retractability: persistence-aware credit assignment for multimodal reinforcement learning

arXiv:2609.36572v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has been extended to Large Vision-Language Models (LVLMs), and perception-aware methods further en

Local Ai

VLALight: A Vision-Language-Action Model for Traffic Signal Control

arXiv:2609.36934v1 Announce Type: new Abstract: Traffic signal control (TSC) is essential for improving urban mobility and reducing congestion. Although roadside cameras are widely deployed at signali

local-aiarxiv-cs-ai

VLM Fine-Tuning for End-to-End Combinatorial Optimization

arXiv:2609.37175v1 Announce Type: new Abstract: Large language models (LLMs) have provided a unified interface for end-to-end combinatorial optimization (CO), but textual serialization alone may obscu

researcharxiv-cs-cl

VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training

arXiv:2609.36648v1 Announce Type: new Abstract: Vision-language pre-training has reshaped image clustering, giving rise to language-assisted image clustering (LaIC), which leverages textual semantics

Volatility-Clustering Adaptation for Financial Time Series

arXiv:2609.37715v1 Announce Type: new Abstract: Time-series foundation models are increasingly adapted to new domains through fine-tuning on target data, under the implicit assumption that more target

researcharxiv-cs-lg

VoxelSage: Tool-Augmented 3D CT Analysis and Simulator-Shielded Sequential Resection Planning for Liver Tumors

arXiv:2609.37648v1 Announce Type: new Abstract: Preoperative liver-tumor assessment requires segmentation, physical-space measurement, visual evidence, and resection planning from the same three-dimen

vSkipper: Translating Dynamic Layer Skipping into LLM Serving Gains

arXiv:2609.37062v1 Announce Type: cross Abstract: Dynamic layer skipping reduces LLM computation by allowing each token to execute only a subset of the model's layers. However, existing skippers rely

VStress: Correlation-Aware Auditing and Adaptive Budget Allocation for Repeated Verifiers

arXiv:2609.36958v1 Announce Type: cross Abstract: Repeated verifier calls are useful only when they contribute conditional information. We introduce VStress, an auditable replay contract, and VStress-

safetyarxiv-cs-ai

Agents

Waggle-Dance-Inspired Multi-UAV Recruitment with RGB and Synthetic Event Vision

arXiv:2609.37464v1 Announce Type: new Abstract: The honeybee waggle dance motivates a communication mechanism in which one agent's motion guides the subsequent work of other agents. For aerial robots,

agentsarxiv-cs-ro

Wasserstein Causal Forests for Distribution-Valued Outcomes

arXiv:2609.35898v1 Announce Type: cross Abstract: This paper proposes Wasserstein Causal Forests (WCF) for settings in which each unit's outcome is itself a probability distribution. This study also d

Watch-Think-Interact: Bootstrapping Long-Horizon Multi-Turn Streaming Video Reasoning with Reinforcement Learning

arXiv:2609.37035v1 Announce Type: new Abstract: Streaming video assistance requires models to answer asynchronous questions from an observed prefix under a fixed context budget. Existing approaches mo

researcharxiv-cs-ai

WayFinder: Hierarchical Visual-Language-Action for Zero-Shot Waypoint Generation and Low-Level Kinematic Control

arXiv:2609.37922v1 Announce Type: new Abstract: Visual Language Action (VLA) models offer unprecedented generalization for autonomous robots; however, their real-world deployment is frequently bottlen

safetyarxiv-cs-ro

Waypoint-1.5: A Real-Time Video World Model for Consumer Hardware

arXiv:2609.37107v1 Announce Type: new Abstract: We present Waypoint 1.5, a real-time diffusion world model for interactive video generation on consumer-grade hardware. Unlike general video diffusion m

Weeding Out Bad Seeds: Initial-Noise-Robust Unlearning for Text-to-Image Diffusion Models

arXiv:2609.37537v1 Announce Type: new Abstract: Machine unlearning has emerged as a critical post-hoc safety measure to erase sensitive concepts from Text-to-Image (T2I) models without prohibitive ret

Agents

WEFT: Scaling Tool-Use Post-Training for General-Purpose Agents

arXiv:2609.36887v1 Announce Type: new Abstract: Recent efforts to scale tool-use post-training have largely centered on the synthesis of executable environments, which constitute only one component of

agentsarxiv-cs-ai

Weight-Adjusted Gradients Reveal Parameter Importance and Failure Modes in LLMs

arXiv:2607.10803v2 Announce Type: replace-cross Abstract: Understanding which parameters are influential in Large Language Models (LLMs) is central to improving their efficiency, reliability, and inte

Weights Read and Write Features: Scalable Parameter Decomposition Grounded in Activation Space

arXiv:2609.37731v1 Announce Type: new Abstract: Activation space and parameter space provide complementary views of model computation. Activations represent information, while weights read, transform,

WeLike2Party! In-Context Motion Transfer for Multi-Human Image Animation

arXiv:2609.36937v1 Announce Type: cross Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation,

What Comes Next? Omni-StoryBench for Evaluating Story-Grounded Omnimodal Generation

arXiv:2609.37317v1 Announce Type: new Abstract: Omnimodal evaluation should go beyond independent text, image, and speech production: individually plausible outputs may not express a coherent shared e

What Does Post-Training Change in Multilingual Reasoning?

arXiv:2609.37104v1 Announce Type: cross Abstract: Open-source reasoning models provide unequal access to reasoning capability across languages. When a model can solve a problem but cannot deliver a co

What if automating AI R&D triggers an intelligence explosion?

arXiv:2609.36054v1 Announce Type: cross Abstract: In contrast to even a year ago, AI systems now write most of the code inside the companies that build them. As more of the AI research and development

safetyarxiv-cs-ai

What Makes High-Magnification Knowledge Transferable? A Study of Cross-Resolution Distillation in Whole-Slide Imaging

arXiv:2609.36407v1 Announce Type: new Abstract: Cross-resolution knowledge distillation aims to improve low-magnification whole- slide analysis by transferring high-magnification representations, yet

researcharxiv-cs-cv

What Makes Recurrence Effective in Looped Language Models?

model-releasesarxiv-cs-cl

arXiv:2609.36636v1 Announce Type: cross Abstract: Looped language models (LoopLMs) increase computational depth through parameter sharing, offering a path to scale inference computation without adding

Local Ai

What Shared Prefixes Hide: Trajectory Dropout for On-Policy Distillation

arXiv:2609.33455v2 Announce Type: replace Abstract: On-policy distillation (OPD) trains a student model on its own trajectories using dense token-level feedback from a stronger teacher model. Since ea

local-aiarxiv-cs-ai

What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views

arXiv:2609.36881v1 Announce Type: new Abstract: Causal foundation models (CFMs) pre-trained on data generated from various structural causal models (SCMs) have been proposed for estimating causal effe

When Can Prefixes Compile LoRA? Exact Resource-Capped Tests for Frozen Attention

arXiv:2609.36766v1 Announce Type: new Abstract: Can a fixed continuous prefix replace a given low-rank adapter while the attention head stays frozen? In this research, we show that the answer depends

researcharxiv-cs-lg

When do data mixtures improve scaling laws? Insights from high-dimensional regression

arXiv:2609.38011v1 Announce Type: new Abstract: Modern machine learning systems are trained on mixtures of data from different domains, and choosing the right mixture can substantially improve downstr

When Does Correction Become Repair? Mechanistic Auditing of Internal Interventions in Tool-Using LLMs

model-releasesarxiv-cs-cl

arXiv:2609.36138v1 Announce Type: new Abstract: Before invoking external tools, an agentic LLM must select among a K-way action space: executing a call, seeking clarification, answering directly, or d

When Is Coarse Supervision Worth It? Cost-Aware Learning under Unknown Aggregation

arXiv:2609.36704v1 Announce Type: new Abstract: Modern learning systems often acquire supervision at multiple resolutions, trading annotation cost against information content. We study cost-aware two-

When Models Don't Manipulate Manifolds: The Geometry of a Comparison Task

arXiv:2609.37680v1 Announce Type: cross Abstract: One of the current premises of mechanistic interpretability research is that detailed accounts of the geometry of neural network representations can t

When Semantics Matter: Reliability-Aware Semantic-Rhythm Control for Co-Speech Gesture Generation

arXiv:2609.36685v1 Announce Type: new Abstract: Co-speech gesture generation aims to synthesize natural gestures that are both temporally synchronized with speech and semantically consistent with the

When Should Agents Check External State? Budgeting Observations for Stored Intentions

arXiv:2609.37125v1 Announce Type: new Abstract: Prospective memory allows an agent to retain an intention tied to a future condition, but the stored intention does not reveal whether that condition cu

safetyarxiv-cs-ai

When Should LLMs Trust Their Own Revisions? A Risk-Aware Study of Intrinsic Self-Correction

arXiv:2609.35832v1 Announce Type: cross Abstract: Intrinsic self-correction asks a language model to revise its own answer without receiving new external evidence. A second pass can recover mistakes,

When Successful Memories Mislead Embodied Agents:Memory Adaption For Task-Conditioned Execution

arXiv:2609.35808v1 Announce Type: cross Abstract: Experience reuse can reduce repeated exploration in embodied agents, but a trajectory that succeeded previously may be unsuitable for the current exec

researcharxiv-cs-ai

When to Adapt: Multi-Signal Domain Shift Detection for Efficient Training-Free Adaptation in Open-Vocabulary Segmentation

arXiv:2609.37602v1 Announce Type: cross Abstract: Robust and reliable perception is essential for autonomous robots operating in real-world environments, particularly in long-term missions where envir