arXiv AI
Jul 14
VIA: Visual Interface Agent for Robot Control
arXiv:2607. 11119v1 Announce Type: cross Abstract: Robot manipulation is a complex task that requires visual understanding, physical reasoning, planning, and closed-loop control.
Multimodal models
Vision-language models, speech and cross-modal systems that read, look and listen in the same forward pass.
arXiv Machine Learning
Jul 14
Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts
arXiv:2607. 11656v1 Announce Type: cross Abstract: Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data.
arXiv AI
Jul 14
AutoVSR: Automatic Visual-to-Symbolic Reasoning for Symbolic Expression Generation from Circuit Schematic
arXiv:2607. 11338v1 Announce Type: new Abstract: Symbolic expressions can effectively characterize and predict circuit behavior, but deriving them directly from circuit schematics is challenging.
arXiv AI
Jul 14
From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
arXiv:2607. 11689v1 Announce Type: cross Abstract: Artificial general intelligence ultimately requires agents that can reason and act in the physical world.
arXiv AI
Jul 14
DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs
arXiv:2607. 11228v1 Announce Type: cross Abstract: While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases.
arXiv AI
Jul 14
MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms
arXiv:2607. 09749v1 Announce Type: cross Abstract: Foundation models have recently emerged as a powerful paradigm for learning transferable representations from large scale biomedical data, yet existing approaches for physiological waveforms primarily optimize reconstruction or forecasting objectives that do not explicitly preserve clinically meaningful waveform morphology.
arXiv AI
Jul 14
LOGOS: A Living Logic for AI Agent Teams That Evolve With Humans
arXiv:2607. 10878v1 Announce Type: new Abstract: AI agents are evolving from answer engines into persistent teams that use tools, delegate work, learn from experience, and modify the artifacts that shape their future behavior.
arXiv AI
Jul 14
ReflectWorld-MM: An Entity-Oriented Multi-Media Memory System for Open-Ended Video Streams
arXiv:2607. 09759v1 Announce Type: cross Abstract: Building assistants that can continually watch the world, remember what they see, and reason over their accumulated experience is a long-standing goal, and recently multimodal agents equipped with long-term memory over video streams have attracted increasing interest.
arXiv Machine Learning
Jul 14
Local Multimodal Music Alignment from Global Supervision
arXiv:2607. 10023v1 Announce Type: cross Abstract: Understanding music requires understanding localized relationships across data modalities, e.
arXiv AI
Jul 14
MAG: A Web-Agent Benchmark and Harness for Multimodal Action and Guide Generation
arXiv:2607. 10079v1 Announce Type: new Abstract: Digital Adoption Platforms (DAPs) are embedded overlays widely used on web systems to guide users through operations inside a page, helping them get started with unfamiliar interfaces quickly.
arXiv Machine Learning
Jul 14
On the Efficiency of LoRA Fine-Tuning for Vision-Language-Action Models in Industrial Robotic Manipulation
arXiv:2607. 10172v1 Announce Type: cross Abstract: Deploying billion-parameter Vision-Language-Action (VLA) models on industrial hardware requires fine-tuning to bridge the embodiment gap.
arXiv AI
Jul 14
AdvNav: Behavior-Guided Black-Box Adversarial Attacks on Vision-Language Navigation
arXiv:2607. 11063v1 Announce Type: new Abstract: Despite progress in Embodied AI, Vision-and-Language Navigation systems remain vulnerable to adversarial visual disturbances.
arXiv AI
Jul 14
Trivial Prompt Reframing Bypasses Safety Guardrails in Google\'s MedGemma-4B
arXiv:2607. 09804v1 Announce Type: cross Abstract: Open-weight medical language models are increasingly used as the base of patient-facing and clinician-support applications.
arXiv AI
Jul 14
Omni-Decision: A Progressive Evidence-State Agent System for Omni-Modal QA
arXiv:2607. 11433v1 Announce Type: new Abstract: Omni-modal evidence-seeking QA requires agents to answer questions whose evidence is sparsely distributed across videos, audio, images, web pages, and computation results.
arXiv Machine Learning
Jul 14
SpurLens: Automatic Detection of Spurious Cues in Multimodal LLMs
arXiv:2503. 08884v3 Announce Type: replace-cross Abstract: Unimodal vision models are known to rely on spurious correlations, but it remains unclear to what extent Multimodal Large Language Models (MLLMs) exhibit similar biases despite language supervision.
arXiv AI
Jul 14
UNIT: Unleash Large Language Models Potential for Graph Continual Learning
arXiv:2607. 10159v1 Announce Type: new Abstract: In real-world multimodal web scenarios, graph-structured data often arrives in a streaming manner, making graph continual learning a crucial paradigm for continuously modeling such evolving structures.
arXiv Machine Learning
Jul 14
NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra
arXiv:2512. 19733v3 Announce Type: replace-cross Abstract: Molecular structure elucidation from spectroscopic data is a long-standing challenge in Chemistry, traditionally requiring expert interpretation.
arXiv Machine Learning
Jul 14
LaGuadia: Language-Guided Adaptive Distillation from Pathology Foundation Models
arXiv:2607. 11257v1 Announce Type: cross Abstract: Pathology Foundation Models (PFMs) offer powerful Whole Slide Image (WSI) representations but suffer from massive computational costs.
arXiv Machine Learning
Jul 14
CHM-Net: Center Heatmap-driven Macro-Micro Modeling Network for MRI-based Microbial Density Stratification
arXiv:2607. 09812v1 Announce Type: cross Abstract: Microbial density is clinically important for tumor assessment and treatment decision-making, and recent advances in deep learning suggest that it can be non-invasively inferred from multimodal MRI.
arXiv Machine Learning
Jul 14
CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting
arXiv:2602. 03564v2 Announce Type: replace Abstract: Time series forecasting can be viewed as a generative problem that requires both semantic understanding over contextual conditions and stochastic modeling of continuous temporal dynamics.