BOxCrete is an open-source AI model using Gaussian Process regression and Bayesian Optimization to predict concrete compressive strength and optimize mix designs for performance and sustainability. It addresses the complexity of modern concrete formulations by providing a reproducible, data-driven framework built on a new open-access dataset.

This paper introduces LSAI, a novel large and small AI model codesign framework, to enable agentic robots to perform accurate and real-time environment sensing and estimation with efficient path planning in complex scenarios like search and rescue. It aims to overcome limitations of traditional and singular large AI solutions in multi-robot cooperation by deeply integrating edge and terminal intelligence.

This paper argues that the widespread adoption of frictionless Generative AI risks "cognitive agency surrender" by exploiting human cognitive miserliness and inducing automation bias. It proposes "Scaffolded Cognitive Friction," using Multi-Agent Systems as computational Devil's Advocates to introduce beneficial epistemic tension, alongside multimodal computational phenotyping to measure its effect.

This paper introduces a novel deep reinforcement learning framework for safe multi-agent control in highway merging scenarios, integrating partial attention mechanisms into a QMIX architecture. It proposes both spatial and temporal attention to focus on relevant neighboring vehicles and their historical states, combined with a comprehensive reward signal to balance global traffic objectives and individual agent interests. The approach demonstrates significant improvements in safety, driving speed, and overall reward compared to baseline models in SUMO simulations.

This study empirically investigates how school-level Artificial Intelligence (AI) readiness influences student AI literacy in vocational education through the mediating role of aggregated teacher capabilities. Using a large national dataset, it establishes that institutional preparedness, particularly when translated into teacher competence, significantly impacts student learning outcomes across diverse regional contexts.