arXiv：cs.AI· Zhongnian Li, Shi Xu, Peng Ying, Weikang Gong, Zhixiao Wang, Xinzheng Xu·· 7 小时前
TASA：面向组合优化问题的可追踪智能体优化框架
Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems
AI 导读
针对 OPRO 优化过程难以追踪、无法利用历史迭代信息的问题，研究者提出工具增强策略智能体 TASA，通过优化轨迹为每步规划策略，将优化过程分解为可追踪的策略选择。TASA 还利用 LLM 构建问题专用工具，并引入策略引导的自适应工具规划器来高效更新解，在持续探索下建立了局部收敛并验证了求解质量与策略引导搜索的可追踪性。
正文
Abstract:Optimization by PROmpting (OPRO) directly leverages Large Language Models (LLMs) to generate candidate solutions through natural-language prompting. However, OPRO's optimization process is difficult to track, which prevents LLMs from capturing useful information accumulated over previous iterations, thereby limiting solution quality. To address this limitation, we propose a Tool-Augmented Strategic Agent (TASA) which summarizes and exploits trajectory to improve solution quality. Specifically, TASA plans the strategy for each step based on the optimization trajectory, which decomposes the optimization process into traceable strategy selections. Moreover, we leverage LLM to construct problem-specific tools and introduce a strategy-guided adaptive tool planner, which efficiently utilizes the information of trajectory to update solutions. We establish local convergence under persistent exploration and evaluate the complete framework under a shared candidate-evaluation budget. The results support its solution quality and the traceability of strategy-guided search.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.14736 [cs.AI] |
| (or arXiv:2610.14736v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14736 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shi Xu [view email]
[v1]
Sat, 10 Oct 2026 08:57:19 UTC (1,891 KB)
来源：arXiv：cs.AI · arxiv.org