large language models 2algorithm discovery 1automated objective discovery 1combinatorial optimization 1evolutionary algorithms 1experience replay 1heuristic design 1neural combinatorial optimization 1partial differential equations 1physics-informed neural networks 1preference learning 1reinforcement learning 1
From the 3 of 16 linked papers with an AI index.
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cs.AI2026
EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
Peng Yin, Kai Li, Yifan Zhang +1
EvoPINN is an agentic framework that uses a large language model to automatically generate and verify executable algorithms for physics-informed neural networks, improving the accu…
cs.AI2026
Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
Yuheng Jing, Kai Li, Ziwen Zhang +8
In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT)-where coordination with un…
cs.AI2026
Game-Theoretic Co-Evolution for LLM-Based Heuristic Discovery
Xinyi Ke, Kai Li, Junliang Xing +2
Large language models (LLMs) have enabled rapid progress in automatic heuristic discovery (AHD), yet most existing methods are predominantly limited by static evaluation against fi…