arXiv AI
Aug 12
DashArena: Benchmarking LLMs on Interactive Analytic Dashboard Generation
arXiv:2608. 10567v1 Announce Type: new Abstract: Analytic dashboards combine coordinated views and interactions for data exploration and decision-making.
Multimodal models
Vision-language models, speech and cross-modal systems that read, look and listen in the same forward pass.
arXiv AI
Aug 12
Post-Hoc Sparse Coding of Latent Communication Between Vision-Language Model Agents
arXiv:2608. 10198v1 Announce Type: new Abstract: Latent-space communication allows heterogeneous vision-language model agents to exchange continuous representations without serializing visual and reasoning states into text.
arXiv Machine Learning
Aug 12
UniProbe: A Learnable Token-Level Hallucination Detector for Large VLMs using Multi-Structural Internal Representations
arXiv:2608. 10835v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) achieve impressive visual reasoning and dialogue capabilities, yet frequently hallucinate content unsupported by the visual input.
arXiv AI
Aug 12
Never Stop Speaking: a Denial-of-Service Attack on End-to-End Speech Language Models
arXiv:2608. 10405v1 Announce Type: cross Abstract: Many studies have shown that specially crafted inputs can induce large language models (LLMs) to generate excessively long outputs, resulting in significant computational overhead and resource consumption.
arXiv Machine Learning
Aug 12
BooST: Bridging Semantics and Motions for Efficient Skill Transfer
arXiv:2608. 10600v1 Announce Type: cross Abstract: Skill abstraction---the process of learning reusable and temporally extended behaviors---has emerged as a key paradigm for improving sample efficiency and generalization in robot learning.
arXiv Machine Learning
Aug 12
MultiModal Code-Switching: Interleaving Visual Objects into Language for Explicit Object-Level Alignment
arXiv:2608. 11167v1 Announce Type: cross Abstract: Existing Multimodal Large Language Models (MLLMs) predominantly rely on image-text pairs for modality alignment pretraining, mapping global image representations to long textual descriptions.
arXiv AI
Aug 12
Hybrid Token Compression for Vision-Language Models
arXiv:2512. 08240v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) rely on hundreds of visual tokens, leading to high computational and memory costs.
arXiv AI
Aug 12
HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
arXiv:2506. 03922v4 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of domains.
arXiv AI
Aug 12
Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
arXiv:2605. 02937v2 Announce Type: replace-cross Abstract: Deep learning in de novo protein design has achieved atomic-level fidelity.
arXiv AI
Aug 12
Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models
arXiv:2604. 26508v2 Announce Type: replace-cross Abstract: Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms.
arXiv Machine Learning
Aug 12
FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data
arXiv:2608. 10857v1 Announce Type: new Abstract: Determining the complexity, or Intrinsic Dimension (ID), of data is fundamental to efficient and interpretable representation learning.
arXiv Machine Learning
Aug 12
Clarity: The Flexibility-Interpretability Trade-Off in Sparsity-aware Concept Bottleneck Models
arXiv:2601. 21944v3 Announce Type: replace Abstract: The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability.
arXiv AI
Aug 12
Situation Graph Prediction for User Perspective Modeling
arXiv:2602. 13319v2 Announce Type: replace Abstract: Perspective-aware AI requires modeling evolving internal states---goals, emotions, contexts---not merely preferences.
arXiv Machine Learning
Aug 12
Population-Aware Physics-Informed Neural Particle Flow for Robust Spacecraft Bayesian Navigation
arXiv:2606. 10959v2 Announce Type: replace Abstract: Spacecraft navigation often requires Bayesian inference from sparse nonlinear measurements that produce curved, multimodal, or geometrically constrained posterior distributions.
arXiv Machine Learning
Aug 12
TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling
arXiv:2608. 10402v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with growing contexts, and finish at highly variable times.
arXiv Machine Learning
Aug 12
HoloQ-VLA: Uniform W4A4 Quantization of Vision-Language-Action Models
arXiv:2605. 28803v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models unify perception, reasoning, and control in a single policy, but their multi-billion-parameter backbones and diffusion-based action heads make on-device deployment prohibitively expensive.
arXiv AI
Aug 12
LaViT: Aligning Latent Visual Thoughts for Multi-modal Reasoning
arXiv:2601. 10129v2 Announce Type: replace-cross Abstract: Current multimodal latent reasoning often relies on external supervision (e.
arXiv AI
Aug 12
SafeCap: Improving LVLM Safety with Image Captioning Reinforcement Learning
arXiv:2608. 10513v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) remain vulnerable to jailbreak attacks that exploit visual inputs to bypass safety alignment inherited from their language backbones.
arXiv AI
Aug 12
Lost in Reconstruction: Aligning Action Representations with Language in Vision-Language-Action Models
arXiv:2608. 10484v1 Announce Type: cross Abstract: Action verbs describe not only the physical outcomes of actions, but also how those actions are performed.
arXiv AI
Aug 12
Unlocking the Power of Medical Tabular Data via Semantic-Aware Multimodal Pre-training
arXiv:2608. 10522v1 Announce Type: cross Abstract: While vision-language models dominate medical representation learning, unstructured text lacks the dense, quantitative diagnostic phenotypes inherent in structured clinical tables.