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cs.LG2026
Right Answers, Costly Models: The Efficiency Gap in LLM-based Optimization Modeling
Zhong Li, Xin Huang, Jinhui Wan +6
Optimization modeling formulates real-world decision problems as mathematical programs that solvers can use to find optimal decisions. Large language models (LLMs) can automate thi…
cs.LG2026
Compositional Behavioral Semantics for State Abstraction in Reinforcement Learning
Yivan Zhang, Ziyan Luo, Manuel Baltieri
State abstraction plays a key role in scaling reinforcement learning to complex but structured systems. In studying such systems, a wide range of behavioral structures have been st…