arXiv AI By Zhongnian Li, Shi Xu, Peng Ying, Weikang Gong, Zhixiao Wang, Xinzheng Xu

Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems

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arXiv AI
Sep 12

MAPLE: Memory-Augmented Planning with Language and Evolution

MAPLE is a new agent that maintains and updates optimization problems through successive natural‑language requests, combining language‑based problem construction with mathematical programming and evolutionary search. It preserves the optimization program, accepted plans, earlier updates, and candidate solutions for future requests, enabling rapid adaptation to changing business constraints. In a benchmark of 15 trajectories and 180 updates across various operational domains, MAPLE completed all trajectories with high online scalar quality and Pareto hypervolume ratio, and maintained update validity and useful search information across substantial revisions.

By Kesheng Chen, Yamin Hu, Wenjian Luo
arXiv Computation and Language
Sep 28

ToolSearcher: Optimizing Tool Selection at Scale via Reinforcement Learning

ToolSearcher is a reinforcement learning framework designed to improve large‑scale tool selection for large language models. It introduces category‑constrained discrimination, event‑level search modeling, and trajectory‑aligned credit allocation to better distinguish similar tools, optimize multi‑turn search, and provide fine‑grained rewards. Experiments on large‑scale benchmarks show that ToolSearcher outperforms strong baselines in iterative search and complex tool composition scenarios.

By Zhenlong Dai, Xujie Song, Zitong Wang, Tong Niu, Jian liu, Weiqiang Wang, Xiu Tang, Sai Wu, Chang Yao, Jingyuan Chen
arXiv AI
Oct 1

Learning from Research: Toward Lifelong Agent Harness Evolution

The paper introduces ScholarEvolve, a framework that evolves the software harness of language agents by automatically incorporating insights from recent research papers. It organizes harness improvements into functional modules, uses topic modeling to identify distinct strategies, and evaluates combinations to boost task performance. Experiments show significant gains on AppWorld and Tau2-Bench, raising Qwen3.5-27B completion rates from 49.6% to 63.6% and GPT-5.4-mini pass@1 from 72.7% to 81.9%.

By Jingbo Yang, Kwei-Herng Lai, Xiaowen Wang, Yaar Harari, Evgeniy Gabrilovich, Shiyu Chang