arXiv Machine Learning
Jul 9
Rethinking Multimodal Time-Series Forecasting Evaluation
arXiv:2607. 06973v1 Announce Type: new Abstract: We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX.
Multimodal models
Vision-language models, speech and cross-modal systems that read, look and listen in the same forward pass.
arXiv AI
Jul 9
Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report
arXiv:2607. 07370v1 Announce Type: cross Abstract: In embodied intelligence systems, the motion controller serves as the critical bridge between semantic reasoning and physical execution.
arXiv AI
Jul 9
ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
arXiv:2607. 06633v1 Announce Type: cross Abstract: In this paper, we address the problem of multimodal federated learning with missing modality.
arXiv AI
Jul 9
A Study of Commonsense Reasoning over Visual Object Properties
arXiv:2508. 10956v3 Announce Type: replace-cross Abstract: Inspired by human categorization, visual reasoning about object properties, such as physical attributes and functions, involves identifying and recognizing low-level details and higher-level abstractions.
arXiv AI
Jul 9
On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces
arXiv:2607. 07375v1 Announce Type: cross Abstract: Adversarial vulnerability in deep neural networks (DNNs) has been studied from the perspectives of decision-boundary geometry, feature robustness, input-output Jacobians, and the instability of inverse problems.
arXiv AI
Jul 9
When Prompts Ignore Structure: Graph-Based Attribute Reasoning for Calibrated VLMs
arXiv:2607. 07395v1 Announce Type: cross Abstract: Reliable confidence estimation remains a key limitation of test-time adaptation in vision-language models (VLMs), where prompt tuning improves zero-shot accuracy but often degrades calibration due to entropy-driven overconfidence.
arXiv AI
Jul 9
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks
arXiv:2607. 07189v1 Announce Type: new Abstract: Vision-language models (VLMs) and agentic AI have shown strong performance on semantic visual tasks, but it remains unclear whether they can handle the physics and inverse problems that underlie computational imaging.
arXiv AI
Jul 9
AT-Attn: Temporal-Aware Cross-Attention for Longitudinal Multimodal Alzheimer's Disease Diagnosis
arXiv:2607. 07091v1 Announce Type: cross Abstract: In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits.
arXiv AI
Jul 9
AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning
arXiv:2607. 07033v1 Announce Type: cross Abstract: Large vision-language models incur substantial inference costs because high-resolution inputs introduce thousands of visual tokens, many of which are redundant for a given query.
arXiv AI
Jul 9
Multimodal Voice Activity Projection for Turn-Taking in Social Robots with Voice-Activity-Related Pretrained Encoders
arXiv:2607. 07294v1 Announce Type: cross Abstract: Turn-taking prediction is a key requirement for social robots involved in human-human interaction, particularly in mediator settings, where the robot must anticipate conversational dynamics rather than merely react to pauses.
arXiv Machine Learning
Jul 9
Gradient-free Riemannian Langevin Sampler
arXiv:2607. 07519v1 Announce Type: new Abstract: We address the problem of efficiently sampling multimodal probability distributions, where standard Markov Chain Monte Carlo methods often suffer from poor mixing and mode trapping.
arXiv AI
Jul 9
FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning
arXiv:2512. 02076v2 Announce Type: replace-cross Abstract: We propose FDRMFL, a task-driven multimodal feature extraction framework for federated regression under non-IID data distributions.
arXiv AI
Jul 9
HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models
arXiv:2607. 07507v1 Announce Type: cross Abstract: Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidence.
arXiv AI
Jul 9
End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent
arXiv:2607. 06964v1 Announce Type: cross Abstract: Bridging the gap between human pilot intent and autonomous flight operation is critical for real-world electric vertical takeoff and landing (eVTOL) aircraft deployment.
arXiv Machine Learning
Jul 9
Unveiling the Visual Counting Bottleneck in Vision-Language Models
arXiv:2605. 30170v2 Announce Type: replace-cross Abstract: While Large Vision-Language Models (VLMs) excel at interpolation, they suffer catastrophic failures in systematic generalization, most notably in visual counting.
arXiv Machine Learning
Jul 9
Reward Valuation in Vision Language Models: Causal Mechanisms Underlying Anhedonia
arXiv:2607. 06626v1 Announce Type: new Abstract: Recent Vision-Language Models capture increasingly complex aspects of human cognition.
arXiv AI
Jul 9
MADB: A Large-Scale Music Aesthetics Dataset with Professional and Multi-Dimensional Annotations
arXiv:2607. 06929v1 Announce Type: cross Abstract: Music aesthetic assessment is a challenging yet underexplored problem, requiring models to capture fine-grained, multi-dimensional human perceptual judgments.
arXiv AI
Jul 9
Multimodal Spatiotemporal-Frequency Fusion with Peak Enhancement for Cellular Traffic Forecasting
arXiv:2607. 07016v1 Announce Type: cross Abstract: Accurate forecasting of cellular network traffic is essential for network planning, resource allocation, and quality-of-service assurance in modern mobile communication systems.
arXiv Machine Learning
Jul 9
Bifidelity Parameter Estimation Using Conditional Diffusion Models
arXiv:2504. 01894v2 Announce Type: replace Abstract: We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distribution.
arXiv Machine Learning
Jul 9
Entropy-Guided Tensor Compression for Multimodal Federated Learning on Edge Devices
arXiv:2607. 06651v1 Announce Type: new Abstract: Federated learning (FL) over mobile and edge devices increasingly involves multimodal models in which clients differ in both sensing capability and computational capacity.