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V-LynX: Token Interface Alignment for Video+X LLMs

arXiv:2606.00508v1 Announce Type: cross Abstract: This study introduces an intriguing phenomenon in Video LLMs: rather than merely translating frames into textual embeddings, Video LLMs establish a co

Agents

V2I Work Zone Geometry Reconstruction with Pose-Conditioned UWB Range Denoising

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Human DGX agent

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DGX agent

arXiv:2606.00119v1 Announce Type: cross Abstract: Reliable work zone mapping is important for connected and autonomous vehicles (CAVs) to navigate safely and smoothly through work zone areas. Cone-mou

agentsarxiv-cs-ai

2 Jun 2026

Value Flows

arXiv:2510.07650v4 Announce Type: replace-cross Abstract: While most reinforcement learning methods today flatten the distribution of future returns to a single scalar value, distributional RL methods

Value-Free Policy Optimization via Reward Partitioning

arXiv:2506.13702v4 Announce Type: replace-cross Abstract: Single-trajectory preference optimization methods learn from datasets of ((prompt, response, reward)) tuples, offering a practical alternative

Variance-sensitive Thompson sampling for generalised linear bandits, revisited

arXiv:2606.00431v1 Announce Type: new Abstract: We prove a variance-sensitive regret bound for Thompson sampling in stochastic generalised linear bandits. The argument assumes a warm-up, after which t

Variational Learning for Insertion-based Generation

arXiv:2606.02133v1 Announce Type: cross Abstract: Non-monotonic sequence generation methods, such as masked diffusion models, provide a flexible alternative to left-to-right autoregressive modeling by

Local Ai

VDSB-GWSyn: Diffusion Schrodinger Bridge for Controllable and Anatomically Feasible Guidewire Synthesis in Coronary Angiography

arXiv:2606.00109v1 Announce Type: cross Abstract: Coronary guidewire endpoint localization is a fundamental capability for computer-assisted PCI, and its importance increases as robot-assisted PCI is

local-aiarxiv-cs-ai

VEDAL: Variational Error-Driven Asynchronous Learning for 3D Gaussian Splatting Pruning

arXiv:2606.02346v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) achieves remarkable novel view synthesis quality with real-time rendering, yet suffers from excessive memory consumption du

Vegas: Self-Speculative Decoding with Verification-Guided Sparse Attention

arXiv:2602.07223v2 Announce Type: replace Abstract: Long-context large language model (LLM) inference has become the norm for today's AI applications. However, it is severely bottlenecked by the incre

VERA: Variational Inference Framework for Jailbreaking Large Language Models

arXiv:2506.22666v3 Announce Type: replace-cross Abstract: The rise of API-only access to state-of-the-art LLMs highlights the need for effective black-box jailbreak methods to identify model vulnerabi

safetyarxiv-cs-cl

Verifying Meta-Awareness via Predictive Rewards in Reasoning Models

arXiv:2510.03259v2 Announce Type: replace-cross Abstract: Recent research on reasoning models explores the meta-awareness of language models, including their ability to determine optimal thinking dura

Versatile Framework with Semantic and Structural guidance for Image Reconstruction from Brain Activity

arXiv:2606.00121v1 Announce Type: cross Abstract: Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task in brain decoding. Especially, the achievement of preci

VESTA: Visual Exploration with Statistical Tool Agents

arXiv:2606.00384v1 Announce Type: new Abstract: Fitting quantitative models to data is a central step in scientific workflows, yet it remains one of the least automated. Recent agent-based systems lev

VET: A Framework for Analyzing AI Discourse

arXiv:2606.01929v1 Announce Type: new Abstract: Public discourse on AI has become polarized; exaggerated positions on AI in traditional and social media threaten the development of AI Literacy among t

Hardware

ViBE: Co-Optimizing Workload Skew and Hardware Variability for MoE Serving

arXiv:2606.00735v1 Announce Type: cross Abstract: In distributed Mixture-of-Experts (MoE) inference, input-dependent token routing interacts with GPU performance variability to create persistent strag

hardwarearxiv-cs-lg

Local Ai

VICR: Visual In-Context Restoration for Real-World Image Super-Resolution

arXiv:2606.00704v1 Announce Type: new Abstract: Real-world image super-resolution (Real-ISR) requires balancing structural fidelity to degraded observations with realistic detail synthesis. However, e

local-aiarxiv-cs-cv

Tutorials

Video Reasoning without Training

arXiv:2510.17045v2 Announce Type: replace-cross Abstract: Video reasoning using Large Multimodal Models (LMMs) relies on costly reinforcement learning (RL) and verbose chain-of-thought, resulting in s

tutorialsarxiv-cs-ai

Agents

VideoBrain: Learning Adaptive Frame Sampling for Long Video Understanding

arXiv:2602.04094v2 Announce Type: replace Abstract: Long-form video understanding remains challenging for Vision-Language Models (VLMs) due to the inherent tension between computational constraints an

agentsarxiv-cs-cv

Visible Light Positioning With Lame Curve LEDs: A Generic Approach for Camera Pose Estimation

arXiv:2602.01577v3 Announce Type: replace-cross Abstract: Camera-based visible light positioning (VLP) is a promising technique for accurate and low-cost indoor camera pose estimation (CPE). To reduce

Vision Language Models Cannot Reason About Physical Transformation

arXiv:2603.07109v2 Announce Type: replace Abstract: Understanding physical transformations is fundamental for reasoning in dynamic environments. While Vision Language Models (VLMs) show promise in emb

Vision-language Models for Driver Monitoring Systems: A Driver Activity Description Dataset

model-releasesarxiv-cs-cv

arXiv:2606.02273v1 Announce Type: new Abstract: Understanding subtle driver actions is essential for building reliable driver monitoring systems. Existing visionlanguage models (VLMs) are trained on g

VISReg: Variance-Invariance-Sketching Regularization for JEPA training

arXiv:2606.02572v1 Announce Type: new Abstract: Self-supervised learning methods prevent embedding collapse via modeling heuristics or explicit regularization of the embedding space. Among the latter,

Visual-Noise Guided In-Context Distillation for Multimodal Large Language Model Unlearning

arXiv:2606.00105v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress on vision-language tasks, but they may also memorize and expose sensitive o

Visual Persuasion: What Influences Decisions of Vision-Language Models?

arXiv:2602.15278v2 Announce Type: replace-cross Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (V

Visualizing definitional divergence in high-dimensional data by manifold alignment: Application to 3D right ventricular strain computations

arXiv:2501.12178v2 Announce Type: replace Abstract: Medical imaging studies often rely on a single sample per subject, assuming it is representative of their physiological traits. However, variations

safetyarxiv-cs-cv

Agents

VLAMotor: Test-Guided Enhancement of Vision-Language-Action Models via Agent-BasedData Synthesis

arXiv:2606.00053v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models follow a data-driven paradigm and are constrained by the coverage of training data, making them prone to failure on

agentsarxiv-cs-ro

VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting

arXiv:2606.02138v1 Announce Type: cross Abstract: Out of distribution (OOD) events in multivariate time series forecasting are rare but often dominate real world risk, making average case forecasting

VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models

arXiv:2601.03309v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining sig

VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization

arXiv:2606.02564v1 Announce Type: new Abstract: The recent 'Reasoning with Video' paradigm utilizes Video Generation Models (VGMs) to generate temporally coherent visual trajectories to complete reaso

Tutorials

VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecasting

arXiv:2509.15394v3 Announce Type: replace Abstract: Accurate electricity demand forecasting is challenging due to the strong multi-periodicity of real-world demand series, which makes effective modeli

tutorialsarxiv-cs-lg

VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio

arXiv:2512.10120v2 Announce Type: replace-cross Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identit

Applications

WALL-WM: Carving World Action Modeling at the Event Joints

arXiv:2606.01955v1 Announce Type: cross Abstract: WALL-WM is a World Action Model that shifts video-action learning from chunk-centric optimization to event-grounded Vision-Language-Action pretraining

applicationsarxiv-cs-cv

WAON: A Large-Scale Japanese Image-Text Dataset for Cultural Adaptation in Contrastive Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2510.22276v3 Announce Type: replace-cross Abstract: Contrastive vision-language models have achieved remarkable progress through large-scale pretraining. Recent work has shown that removing Engl

WaveFilter: Enhancing the Long-Context Capability of Diffusion LLMs via Wavelet-Guided KV Cache Filtering

arXiv:2606.00724v1 Announce Type: cross Abstract: Diffusion Large Language Models (DLMs) have demonstrated significant advantages across various tasks. However, constrained by their multi-step iterati

Wavelet-Fusion Diffusion Model for Multimodal Brain MRI Synthesis with Modality and Metadata Conditioning

arXiv:2606.00689v1 Announce Type: new Abstract: Multimodal MRI provides complementary information for neuroimaging analysis, where different imaging modalities capture distinct anatomical, tissue, and

safetyarxiv-cs-cv

WAXAL-NET: Finetuned Edge ASR Across 19 African Languages

arXiv:2606.02375v1 Announce Type: new Abstract: We evaluate whether compact domain-specialized ASR models can outperform massively multilingual foundation models for conversational African speech acro

researcharxiv-cs-cl

Weak Critics Make Strong Learners: On-Policy Critique Distillation for Scalable Oversight

arXiv:2606.00424v1 Announce Type: new Abstract: As large language models become stronger, weak supervisors may fail to provide reliable labels, preferences, or final judgments for complex outputs, lim

Local Ai

WebSpline: Structure-Informed Splines for Real-Time 3D Gaussians from Monocular Videos

arXiv:2606.02096v1 Announce Type: new Abstract: Dynamic scene reconstruction from monocular videos remains highly challenging, as existing methods often struggle to balance global structural coherence

local-aiarxiv-cs-cv

Well-Posed KL-Regularized Control via Wasserstein and Kalman-Wasserstein KL Divergences

arXiv:2602.02250v2 Announce Type: replace-cross Abstract: Kullback-Leibler (KL) divergence regularization is widely used in reinforcement learning, but it becomes infinite under support mismatch and c

What Cosine Similarity of Label Representations Can and Cannot Tell us

arXiv:2603.29488v2 Announce Type: replace Abstract: Cosine similarity is often used to measure the similarity of vector representations of neural network models. However, the cosine similarity of repr

What Do LLMs Know About Alzheimer's Disease? Multi-loss Fine-Tuning and Probing for AD Detection

arXiv:2602.11177v2 Announce Type: replace-cross Abstract: Reliable early detection of Alzheimer's disease (AD) is challenging, particularly due to the limited availability of labeled data. While large

What Makes a Strong Model? A Unified Spectral Analysis of Knowledge Transfer over High-dimensional Linear Regression

arXiv:2606.01292v1 Announce Type: cross Abstract: Teacher-Student Knowledge Transfer (KT) is ubiquitous in modern machine learning, ranging from classical model compression via Knowledge Distillation

What to Format and How: A Benchmark and Workflow Approach for Document Formatting

model-releasesarxiv-cs-cl

arXiv:2606.01936v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have opened up new possibilities for automated document formatting. However, real-world formatting often

What to Test Next: Interpretable Coverage Gap Discovery in Driving VLMs

arXiv:2606.01624v1 Announce Type: new Abstract: Driving vision-language models (VLMs) must accurately understand scenes across diverse conditions defined by Operational Design Domains (ODDs), yet veri

safetyarxiv-cs-cv

When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

arXiv:2602.16763v2 Announce Type: replace Abstract: Artificial intelligence benchmarks are an important mechanism for measuring model progress and guiding deployment decisions. However, benchmarks qui

When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning

arXiv:2602.08236v2 Announce Type: replace-cross Abstract: Despite rapid progress in MLLMs, visual spatial reasoning remains unreliable when correct answers depend on how a scene would appear under uns

When Data Is Scarce: Scaling Sparse Language Models with Repeated Training

arXiv:2606.01155v1 Announce Type: cross Abstract: Scaling laws for dense LLMs under infinite data are well explored, but how sparsity interacts with limited data is not. In this work, we study sparse

When Do Attention Circuits Form? Developmental Trajectories of Capability and Attention-Sink Emergence Across Three 1B-ClassArchitectures

arXiv:2606.02378v1 Announce Type: cross Abstract: We track the developmental trajectory of attention-head circuit formation across three 1B-class language models spanning two architecture families (de