A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization

Key points

The authors built AgenticBBO-Bench, a unified-budget benchmark spanning synthetic functions, hyperparameter optimization, database tuning, chip design, and molecular design.
The Agentic setting beat Direct on all five families with an average relative improvement of 30.3%, and on BBOB the score rose from 0.460 to 0.661.
Adding GP-based numerical tools did not consistently help, so the base Agentic mean of 0.589 was higher than 0.574 for + GP Suggest and 0.538 for + GP Tools.
Task and variable semantics raised the mean score from 0.465 to 0.595, while more specific priors were less reliable.
Handing the search to TuRBO after the agent warm-up scored 0.570, close to continued Agentic optimization and clearly above TuRBO started from the initial observations at 0.475.
On the frontier challenge GPT-6 Astra scored the highest mean of 0.591, closely followed by DeepSeek V4.1 Flash at 0.584, yet no model dominated every domain.

External research summaries. These are not HDATF publications or measured product results.