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

This arXiv update presents WMLLM, a self-evolving optimization-agent framework that uses predict-then-act world modeling. The approach leverages large language models to forecast promising optimization directions before candidate generation, followed by agentic refinement, population-based search, and reinforcement learning to improve both the world model and optimization strategy. Experiments on black-box optimization, especially multi-objective molecular optimization, demonstrate improved sample efficiency and competitive final performance, achieving state-of-the-art results under limited evaluation budgets.

WMLLM integrates world modeling with a predict-then-act paradigm to guide candidate generation in black-box optimization tasks. The agent predicts promising directions and then generates candidates, with multi-turn refinements to continually improve its model and strategy.

The method emphasizes sample efficiency and leverages population-based search and reinforcement learning to evolve its optimization approach. Empirical results are highlighted in multi-objective molecular optimization benchmarks under constrained budgets.

Source: cs.LG updates on arXiv.org — https://arxiv.org/abs/2609.01608