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black-box optimization

topic4 events
papersTODAY 04:00 UTC

arXiv Paper Decouples Convergence and Diversity in Multi-Objective Bayesian Optimization

A new arXiv preprint introduces a method for multi-objective Bayesian optimization that treats the search for convergence toward the Pareto front and the search for diverse coverage separately. The approach aims to improve how well expensive black-box problems with multiple objectives are approximated under tight sample budgets. The work is theoretical and benchmark-oriented, with no released product or model attached.

research/bayesian-optimization/arxiv/benchmarks
papersSEP 10 04:00 UTC

Practical DIRECT-type algorithm proposed for medium-scale black-box global optimization

A new arXiv preprint presents a DIRECT-type algorithm aimed at extending deterministic global optimization to medium-scale black-box problems. Classic DIRECT methods are mostly effective in low dimensions and tend to converge slowly, so the authors focus on making the framework practical at larger scales. The method retains the balanced exploration-exploitation behavior that made DIRECT widely used.

optimization/algorithms/black-box
papersSEP 10 04:00 UTC

Self-play in code distills a text harness for black-box optimization, arXiv study finds

Researchers explore whether a language-model agent can acquire a numerical search strategy through executable practice and then transfer it as plain text. Targeting low-budget black-box optimization, where unaided LLMs fall short of strong classical optimizers, the approach uses self-play in code to automatically build the harness. The work suggests learned optimization behavior can be distilled into reusable text prompts instead of manually engineered ones.

agents/self-play/optimization/prompting