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WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling
Not provided in the abstract
optimizationreinforcement learningworld modelingmulti-objective
2609.01608
Builder Relevance
2h ago80%
Abstract
WMLLM proposes a framework for improving sample efficiency in black-box optimization problems using world modeling and large language models.
Reality Card
Core Claim
WMLLM achieves state-of-the-art results in multi-objective molecular optimization under a limited evaluation budget.
Method / Result
Improved sample efficiency and final optimization performance in black-box optimization tasks.
Limitations
The paper does not specify the reproducibility of the results or the generalizability of the method to other optimization problems.
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