Infant Agent: A Tool-Integrated, Logic-Driven Agent with Cost-Effective API Usage
Bin Lei, Yuchen Li, Yiming Zeng, Tao Ren, Yi Luo, Tianyu Shi, Zitian Gao, Zeyu Hu, Weitai Kang, Qiuwu Chen
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Despite the impressive capabilities of large language models (LLMs), they currently exhibit two primary limitations, 1: They struggle to autonomously solve the real world engineering problem. 2: They remain challenged in reasoning through complex logic problems. To address these challenges, we developed the Infant Agent, integrating task-aware functions, operators, a hierarchical management system, and a memory retrieval mechanism. Together, these components enable large language models to sustain extended reasoning processes and handle complex, multi-step tasks efficiently, all while significantly reducing API costs. Using the Infant Agent, GPT-4o's accuracy on the SWE-bench-lite dataset rises from 0.33\% to 30\%, and in the AIME-2024 mathematics competition, it increases GPT-4o's accuracy from 13.3\% to 37\%.