arXiv cs.AI· Ming Chen, Rong-Xi Tan, Ke Xue, Yu-Jie Zhou, Taiye Lu, Zhi-Xuan Gao, Peng Xie, Zijun Shen, Chen Lu, Haopu Shang, Chao Qian·· 9 天前AI 评分27
AgenticBBO-Bench：面向 LLM 智能体黑盒优化的跨域基准
A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization
AI 导读
研究者提出 AgenticBBO-Bench，一个覆盖合成函数、超参数优化、数据库调优、芯片设计与分子设计的跨域智能体黑盒优化基准，采用统一有限预算评估协议。实验中智能体 BBO 在全部五个领域家族平均得分高于直接 LLM 方法，并在四个领域超过最优数值优化器。
正文 · 原文
Authors:Ming Chen, Rong-Xi Tan, Ke Xue, Yu-Jie Zhou, Taiye Lu, Zhi-Xuan Gao, Peng Xie, Zijun Shen, Chen Lu, Haopu Shang, Chao Qian
Abstract:Black-box optimization (BBO) arises in many scientific and engineering problems where objective evaluations are expensive and limited. Recent large language model (LLM) agents offer a new way to approach BBO by combining task semantics, computation, optimization tools, and feedback-driven decision making, showing great potential due to the integration with mathematically rigorous tools. However, existing agentic BBO studies use different task domains and system configurations, making their results difficult to compare and the effects of individual design choices hard to isolate. We therefore introduce AgenticBBO-Bench, a cross-domain benchmark for agentic BBO spanning synthetic functions, hyperparameter optimization, database tuning, chip design, and molecular design under a unified finite-budget evaluation protocol. In our experiments, agentic BBO achieves higher family-averaged scores than direct LLM-based methods in all five domains and outperforms the best numerical optimizers in four. We further study three factors shaping agent performance: optimization tools, task information and prior knowledge, and the role of the LLM during search. Our results show that additional numerical tools do not consistently improve performance, task semantics are broadly useful while more specific priors are less reliable, and numerical optimizers can effectively absorb gains from search trajectories established by the agent. Finally, we introduce a five-task frontier challenge within AgenticBBO-Bench and evaluate seven LLMs under the Codex agent harness, where GPT-6 Astra and DeepSeek-V4.1-Flash lie on the Pareto frontier of performance and cost among the evaluated models. Our code is available at this https URL.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2610.12183 [cs.LG] |
| (or arXiv:2610.12183v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12183 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ke Xue [view email]
[v1]
Thu, 8 Oct 2026 15:48:07 UTC (350 KB)
来源：arXiv cs.AI · arxiv.org