Find a story
Search Spins
Search titles, summaries, and missing voices across published articles — press releases, announcements, and media coverage.
0 results for “black-box optimization”
WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling
WMLLM is a new self-evolving optimization agent framework that uses large language models for world modeling to improve sample efficiency in black-box optimization, especially for multi-objective molecular design.
Sep 3, 2026
Out-Of-The-Loop Multi-Fidelity Bayesian Optimization
A new multi-fidelity Bayesian optimization method is proposed that incorporates historical high-fidelity data and task descriptors to improve performance when the highest-fidelity function cannot be queried during optimization.
Aug 6, 2026
BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems
Researchers introduced BBOWP-Bench, a new benchmark suite to evaluate large language models on black-box optimization word problems—where LLMs must infer both search space design and algorithm selection from natural-language problem descriptions.
Aug 5, 2026