This paper proposes AURA, an AIoT framework for continuous, real-world assessment of driving safety among older adults. It integrates rich in-vehicle sensing, multi-scale behavioral modeling, and context-aware analysis to provide explainable insights into driving performance, addressing the limitations of current infrequent and uncontextualized assessment methods.

This paper introduces the IRIS Benchmark, a novel framework designed to synchronously evaluate fairness in both understanding and generation tasks within Unified Multimodal Large Language Models (UMLLMs). It aims to resolve the "Tower of Babel" dilemma of fragmented fairness metrics by offering a multi-dimensional, trade-off analysis approach and uncovering systemic biases in leading models.

This paper provides a comprehensive review of deep learning-based multimodal data fusion in astronomy. It discusses the motivation for integrating diverse astronomical data, outlines various data sources and modalities, and introduces representative deep learning models and fusion strategies to enhance understanding of the universe.

This research explores whether emotional support delivered by a group of AI agents is more effective than support from a single AI agent. It finds that group AI support enhances perceived efficacy by strengthening users' connectedness with the AI system, offering new insights for designing AI-based emotional support.

The Auton Agentic AI Framework introduces a principled architecture to bridge the gap between stochastic Large Language Model outputs and the deterministic requirements of backend systems, standardizing the creation, execution, and governance of autonomous agent systems. It achieves this through a declarative agent specification, hierarchical memory, built-in safety mechanisms, and runtime optimizations for improved reliability and performance.