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arXiv AI By Zhongzheng Li, Qingsong Ran, Shikun Feng, Nian Ran, Wenhao Li, Xiaoyuan Zhang, Yue Wang, Xiaoguang Zhao

WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling

Read the original on arXiv AI →

WMLLM is a self‑evolving optimization‑agent framework that uses a predict‑then‑act world modeling approach. The agent first predicts promising optimization directions and then generates candidates, refining both its world model and strategy through multi‑turn refinement, population‑based search, and reinforcement learning. Experiments on black‑box tasks, particularly multi‑objective molecular optimization, demonstrate that WMLLM improves sample efficiency and achieves state‑of‑the‑art results within a limited evaluation budget.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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