arXiv:2510.18632v4 Announce Type: replace-cross Abstract: Though recent advances in vision-language models (VLMs) have achieved remarkable progress across a wide range of multimodal tasks, understanding 3D spatial relationships from limited views remains a significant challenge. Previous reasoning methods typically rely on pure text (e.g., topological cognitive maps) or on 2D visual cues. However, their limited representational […]
Accelerating Residual Reinforcement Learning with Uncertainty Estimation
arXiv:2506.17564v2 Announce Type: replace-cross Abstract: Residual Reinforcement Learning (RL) is a popular approach for adapting pretrained policies by learning a lightweight residual policy that provides corrective actions. While Residual RL is more sample-efficient than finetuning the entire base policy, existing methods struggle with sparse rewards and are designed for deterministic base policies. We propose two […]
Predictive Analytics for Foot Ulcers Using Time-Series Temperature and Pressure Data
arXiv:2603.12278v1 Announce Type: new Abstract: Diabetic foot ulcers (DFUs) are a severe complication of diabetes, often resulting in significant morbidity. This paper presents a predictive analytics framework utilizing time-series data captured by wearable foot sensors — specifically NTC thin-film thermocouples for temperature measurement and FlexiForce pressure sensors for plantar load monitoring. Data was collected from […]
SHREC: A Spectral Embedding-Based Approach for Ab-Initio Reconstruction of Helical Molecules
arXiv:2603.12307v1 Announce Type: new Abstract: Cryo-electron microscopy (cryo-EM) has emerged as a powerful technique for determining the three-dimensional structures of biological molecules at near-atomic resolution. However, reconstructing helical assemblies presents unique challenges due to their inherent symmetry and the need to determine unknown helical symmetry parameters. Traditional approaches require an accurate initial estimation of these […]
Swap-guided Preference Learning for Personalized Reinforcement Learning from Human Feedback
arXiv:2603.12595v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) is a widely used approach to align large-scale AI systems with human values. However, RLHF typically assumes a single, universal reward, which overlooks diverse preferences and limits personalization. Variational Preference Learning (VPL) seeks to address this by introducing user-specific latent variables. Despite its promise, […]
Context-Enriched Natural Language Descriptions of Vessel Trajectories
arXiv:2603.12287v1 Announce Type: new Abstract: We address the problem of transforming raw vessel trajectory data collected from AIS into structured and semantically enriched representations interpretable by humans and directly usable by machine reasoning systems. We propose a context-aware trajectory abstraction framework that segments noisy AIS sequences into distinct trips each consisting of clean, mobility-annotated episodes. […]
VLM4Rec: Multimodal Semantic Representation for Recommendation with Large Vision-Language Models
arXiv:2603.12625v1 Announce Type: cross Abstract: Multimodal recommendation is commonly framed as a feature fusion problem, where textual and visual signals are combined to better model user preference. However, the effectiveness of multimodal recommendation may depend not only on how modalities are fused, but also on whether item content is represented in a semantic space aligned […]
Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference
arXiv:2602.19509v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) face a persistent trade-off between inference cost and reasoning capability. While “Oracle” models (e.g., Llama-3.3-70B) achieve state-of-the-art accuracy, they are prohibitively expensive for high-volume deployment. Smaller models (e.g., 7-9B parameters) are cost-effective but struggle with complex tasks. We observe that the emerging practice of LLM cascading […]
Artificial intelligence applications in Parkinson’s disease via retinal imaging
arXiv:2603.12281v1 Announce Type: new Abstract: Parkinson’s disease (PD) is projected to increase substantially due to population aging, making early diagnosis increasingly important, as timely detection may delay progression and reduce long-term complications. Retinal microvasculature has emerged as a promising anatomical biomarker of neurodegeneration, and when combined with artificial intelligence AI, retinal imaging may provide an […]
Altered Thoughts, Altered Actions: Probing Chain-of-Thought Vulnerabilities in VLA Robotic Manipulation
arXiv:2603.12717v1 Announce Type: cross Abstract: Recent Vision-Language-Action (VLA) models increasingly adopt chain-of-thought (CoT) reasoning, generating a natural-language plan before decoding motor commands. This internal text channel between the reasoning module and the action decoder has received no adversarial scrutiny. We ask: which properties of this intermediate plan does the action decoder actually rely on, and […]
Social Distancing Equilibria in Games under Conventional SI Dynamics
arXiv:2603.12107v2 Announce Type: replace-cross Abstract: The mathematical characterization of social-distancing games in classical epidemic theory remains an important question, for their applications to both infectious-disease theory and memetic theory. We consider a special case of the dynamic finite-duration SI social-distancing game where payoffs are accounted using Markov decision theory with zero-discounting, while distancing is constrained […]
FC-Track: Overlap-Aware Post-Association Correction for Online Multi-Object Tracking
arXiv:2603.12758v1 Announce Type: cross Abstract: Reliable multi-object tracking (MOT) is essential for robotic systems operating in complex and dynamic environments. Despite recent advances in detection and association, online MOT methods remain vulnerable to identity switches caused by frequent occlusions and object overlap, where incorrect associations can propagate over time and degrade tracking reliability. We present […]