3 papers
cs.LG2025
Heuristic Transformer: Belief Augmented In-Context Reinforcement Learning
Oliver Dippel, Alexei Lisitsa, Bei Peng
Transformers have demonstrated exceptional in-context learning (ICL) capabilities, enabling applications across natural language processing, computer vision, and sequential decisio…
cs.LG2025
MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures
Elena Zamaraeva, Christopher M. Collins, George R. Darling +8
Geometry optimization of atomic structures is a common and crucial task in computational chemistry and materials design. Following the learning to optimize paradigm, we propose a n…
cs.AI2025
A Knowledge-Informed Deep Learning Paradigm for Generalizable and Stability-Optimized Car-Following Models
Chengming Wang, Dongyao Jia, Wei Wang +3
Car-following models (CFMs) are fundamental to traffic flow analysis and autonomous driving. Although calibrated physics-based and trained data-driven CFMs can replicate human driv…