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arXiv:2610.16793v1 Announce Type: new Abstract: Interacting with unfamiliar objects requires understanding how their parts connect and move. We introduce ViewStruct, a framework for jointly predicting

applicationsarxiv-cs-cv

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VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric

DGX agent

arXiv:2610.16441v1 Announce Type: new Abstract: Current image-text evaluation often relies on a global score, which gives limited insight into which semantic details are supported by the image. Explai

model-releasesarxiv-cs-cv

14 Oct 2026

Vision Models Capture Complementary Representations in Children That Converge in Adults

arXiv:2610.16860v1 Announce Type: new Abstract: How the relationship between image-text aligned and vision-only models changes across development remains unknown. We applied time-resolved representati

Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors

arXiv:2610.15556v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models commonly generate robot actions token by token through Large Language Model (LLM) decoders, causing inference late

Visual Dependence Does Not Imply Answerability: A Systematic Analysis of Answerability Signals in Frozen InstructBLIP

arXiv:2610.17902v1 Announce Type: new Abstract: Visual question answering models often produce plausible responses even when the available visual evidence is insufficient to support a valid answer. Al

Visual Sevoing from Lines

arXiv:2610.18363v1 Announce Type: cross Abstract: In this work, we present a fundamentally new approach to visual servoing using lines. It is based on a theoretical and geometrical study of the main l

Local Ai

Visual Tokens on Demand: Visual Budgeting for Scalable and Efficient Multi-Image Reasoning in Multimodal LLMs

arXiv:2610.18858v1 Announce Type: new Abstract: Multimodal large language models have advanced rapidly, but most remain optimized for single-image inputs. Multi-image reasoning is costly because each

local-aiarxiv-cs-cv

VisualCoding: Learning Executable Skills for Reliable Visual Reasoning

arXiv:2610.14403v1 Announce Type: new Abstract: Learning reusable skills can improve visual reasoning agents, yet applying a skill still requires the agent to implement or invoke a procedure and produ

Visualizing the World Model in an Agent's Value Function

arXiv:2610.14186v1 Announce Type: cross Abstract: Could model-free reinforcement learning algorithms actually be learning a model of the environment? In this work, we apply counterfactual intervention

Visually Legible but Adversarial: Watermark-Constrained Attacks on End-to-End VLMs for Document Understanding

arXiv:2610.15728v1 Announce Type: new Abstract: Visible watermarks convey document ownership, confidentiality, and status, but can also carry adversarial signals. We introduce Auto-Regional Adversaria

VitaBench: Memory Lifecycles Beyond Retrieval

arXiv:2610.14731v1 Announce Type: new Abstract: Long-term memory benchmarks for language agents primarily evaluate whether information from earlier interactions can be retrieved and used when relevant

VL-DocIR: A Benchmark for Vision-Based Long Document Information Retrieval

arXiv:2610.17057v1 Announce Type: cross Abstract: Vision-based document retrieval has improved rapidly, but current evaluation settings still emphasize short documents and single relevant pages. This

VLA-GAUGE: Systematically Evaluating Generalization and Robustness in Vision-Language-Action Models

arXiv:2610.16567v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) have achieved high success rates on familiar manipulation tasks, yet it remains unclear whether such scores reflect

VLCQ: Post-training quantization for deep neural networks using variable length coding

arXiv:2610.15092v1 Announce Type: new Abstract: Quantization plays a crucial role in efficiently deploying deep learning models on resource-constrained devices. Post-training quantization does not req

VRIQ: Benchmarking and Diagnosing the Visual-Reasoning IQ of Vision-Language Models

arXiv:2602.05382v2 Announce Type: replace Abstract: Vision-language model (VLM) errors on visual reasoning may come from missing visual structure, from mistakes after that structure is extracted, or f

Walk Like This: Stylized Locomotion for Physics-Based Characters through Example-Driven Diffusion

arXiv:2610.18147v1 Announce Type: new Abstract: We present a real-time framework for expressive legged locomotion that enables independent control over velocity and style. Unlike existing data-driven

researcharxiv-cs-ro

Applications

Watermarking Continuous Tabular Data Generation

arXiv:2405.14018v2 Announce Type: replace-cross Abstract: In this paper, we introduce a simple yet effective tabular data watermarking mechanism with statistical guarantees. We show theoretically that

applicationsarxiv-cs-lg

WAVE-0: Bridging VLA Policies and WAMs with Lightweight Latent Future Prediction

arXiv:2610.18568v1 Announce Type: new Abstract: Vision-language-action (VLA) policies excel at generalist robotic manipulation but generate actions without explicitly modeling their environmental cons

tutorialsarxiv-cs-ro

We Have Been Interpreting Diffusion Transformer Backbone Behaviors Wrong

arXiv:2610.14517v1 Announce Type: new Abstract: During a network forward call, diffusion backbones naturally produce many intermediate activations. If we evaluate each activation against an identical

tutorialsarxiv-cs-cv

Weakly Convex Ridge Regularization for 3D Non-Cartesian MRI Reconstruction

arXiv:2603.27158v3 Announce Type: replace Abstract: While highly accelerated non-Cartesian acquisition protocols significantly reduce scan time, they often entail long reconstruction delays. Deep lear

Web Scale on a Budget: Tail-Aware Retrieval Evaluation

arXiv:2610.17986v1 Announce Type: new Abstract: A common practice in instance retrieval is to grow the gallery size to improve a benchmark's discrimination capability. One prominent example is ILIAS,

Weighting-Based Strategies for Classification: Analysis and Local Peak Dynamics with CART Trees

arXiv:2610.18037v1 Announce Type: cross Abstract: Tabular learning tasks often suffer from class imbalance, which is often addressed via weighting-based strategies. In this work, we compare the naive

tutorialsarxiv-cs-lg

What a Data Release Must Preserve for Off-Policy Evaluation

arXiv:2610.15283v1 Announce Type: cross Abstract: In off-policy evaluation, merging decision-time histories that matter differently for a specified policy value or comparison can make that evaluation

What Can Language Models Inherit from Solomonoff Induction? Limits, Attainable Properties, and What Served Conditionals Reveal

arXiv:2610.14007v1 Announce Type: new Abstract: A familiar line of reasoning runs as follows: language models are flexible approximators trained under a strictly proper loss, so scale brings them clos

What DNA Foundation Models Learn Beyond Sequence Composition

arXiv:2610.15711v1 Announce Type: new Abstract: Do genomic foundation models (FMs) provide predictive information beyond simple sequence composition? We evaluate frozen representations from five DNA F

tutorialsarxiv-cs-lg

What Do Sparse Autoencoder Feature Discovery Scores Identify? From Activations to Interventions

arXiv:2610.15850v1 Announce Type: new Abstract: Sparse autoencoder (SAE) features are typically discovered from how their activations co-vary with behaviour, yet they are often used through interventi

What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO)

arXiv:2610.15033v1 Announce Type: cross Abstract: Generative search engines such as ChatGPT, Gemini and Perplexity now decide which Web pages users see as sources, and a growing industry of generative

What is the Value of Censored Data? An Exact Analysis for the Data-driven Newsvendor

arXiv:2602.16842v2 Announce Type: replace Abstract: We study the offline data-driven newsvendor problem when historical demand is censored by past inventory levels. We introduce a class of policies fo

What Limits Cross-Task Generalization in World Action Models?

arXiv:2610.16641v1 Announce Type: new Abstract: Cross-task generalization remains difficult for world action models (WAMs), even when their predicted futures depict plausible manipulation. This gap ca

researcharxiv-cs-ro

What Makes a Good Visual Representation for Spatial Understanding in MLLMs?

arXiv:2610.17844v1 Announce Type: new Abstract: Spatial understanding is a necessary step in moving multimodal large language models (MLLMs) toward reasoning about the 3D physical world. Their visual

What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs

arXiv:2610.17376v1 Announce Type: cross Abstract: A shared latent space is often assumed to make representations from different schemas directly interchangeable, yet sharing parameters, dimensionality

What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study

arXiv:2610.18869v1 Announce Type: cross Abstract: How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We comp

What Really Works for Universal Multimodal Embeddings? A Controlled Study of Contrastive Training Strategies

arXiv:2610.15035v1 Announce Type: new Abstract: Contrastive learning has become the standard paradigm for multimodal representation learning, aligning semantically matched pairs while separating misma

Agents

What Survives When Activation Steering Is Used as Memory

arXiv:2610.14958v1 Announce Type: cross Abstract: Activation steering has recently been used as a memory for skills: past experiences of an agent are stored as steering vectors and retrieved later. Fa

agentsarxiv-cs-lg

When do explicit penalties improve flow matching?

arXiv:2610.15689v1 Announce Type: cross Abstract: Flow matching can learn structure directly from data, raising the question of when additional constraints improve generation. We study penalties that

When Do Learned Blocks Preserve Causal Meaning? Block Identifiability and Constructive Causal Abstractions

arXiv:2610.18155v1 Announce Type: new Abstract: Standard identifiability theory assumes one-dimensional latent factors, but learned representations can encode latent information across multidimensiona

When Does Compensating Inherited Quantization Error Help? Mean-Square Guarantees for Layer-Wise Quantization and Their Limits

arXiv:2610.16020v1 Announce Type: new Abstract: Layer-wise post-training quantization methods let each layer compensate for the quantization error inherited from earlier layers. We ask under what assu

Agents

When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability

arXiv:2610.18229v1 Announce Type: cross Abstract: Deep reinforcement learning agents in partially observable competitive games must act on beliefs about an unseen opponent's intent. We study a spatial

agentsarxiv-cs-lg

When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination

arXiv:2605.06204v2 Announce Type: replace-cross Abstract: Trimming suspicious calibration points is a common response to contamination in conformal prediction. Its effect on clean-target coverage, how

When Dynamic Scaling Fails: A Scale-State Feedback Governs Recurrent-State Quantization in Linear-Attention and SSM LLMs

arXiv:2610.15139v1 Announce Type: new Abstract: Linear-attention and state-space LLMs carry long context in a fixed-size recurrent state, and quantizing that state is the natural way to cut its cost d

When Left Is Right? Camera-Motion Understanding beyond Screen

arXiv:2610.17242v1 Announce Type: new Abstract: Can a vision-language model tell left from right? Modern VLMs solve visual puzzles and follow hour-long videos, yet when asked whether the camera moved

Agents

When LLM Agents See a Conflict but Still Proceed: Cross-Domain Dependencies in Network Slicing

arXiv:2610.17118v1 Announce Type: cross Abstract: Future AI-native networks are being designed to apply autonomous agents built on large language models (LLMs) in network management through intents. W

agentsarxiv-cs-lg

Hardware

When Log Parsing Removes What Matters: Detecting Failures in Distributed GPU Workloads

arXiv:2610.14009v1 Announce Type: new Abstract: Large AI models increasingly rely on distributed GPU execution, yet it remains unclear whether established failure analysis methods transfer effectively

hardwarearxiv-cs-lg

Local Ai

When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features

arXiv:2610.18113v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) commonly assume that node features are fully observed, despite missing features being pervasive in real-world applications.

local-aiarxiv-cs-lg

When More Memory Fails: Hidden Feasibility Holes in Dynamic Tensor Rematerialization

arXiv:2610.14369v1 Announce Type: new Abstract: Dynamic Tensor Rematerialization (DTR) trains neural networks under a memory budget by evicting tensors and recomputing them on demand. A natural expect

Applications

When More Objectives Make Identification Easier: The Role of Preferences and Feedback

arXiv:2610.18577v1 Announce Type: new Abstract: Best-arm identification (BAI) aims to identify the optimal arm using as few samples as possible. While most existing work focuses on a single objective,

applicationsarxiv-cs-lg

When Participant Disjoint Validation Is Not Enough to Establish Generalization in Parkinson's Gait Classification

arXiv:2610.16220v1 Announce Type: new Abstract: Participant-disjoint cross-validation prevents direct participant overlap, but it may still produce optimistic estimates when acquisition site and diagn

When pose graphs are not enough: a failure analysis of pose-graph-based camera estimation for Gaussian splatting

arXiv:2610.14090v1 Announce Type: new Abstract: Exterior Gaussian-splat twins for real-estate listings are captured to a fixed recipe: a consumer drone orbits a single property in several overlapping

researcharxiv-cs-ro