Infant Agent: A Tool-Integrated, Logic-Driven Agent with Cost-Effective API Usage
Despite the impressive capabilities of large language models (LLMs), they currently exhibit two primary limitations, \textbf{\uppercase\expandafter{\romannumeral 1}}: They struggle to \textbf{autonomously solve the real world engineering problem}. \textbf{\uppercase\expandafter{\romannumeral 2}}: Th...
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creator | Lei, Bin Li, Yuchen Zeng, Yiming Ren, Tao Luo, Yi Shi, Tianyu Gao, Zitian Hu, Zeyu Kang, Weitai Chen, Qiuwu |
description | Despite the impressive capabilities of large language models (LLMs), they currently exhibit two primary limitations, \textbf{\uppercase\expandafter{\romannumeral 1}}: They struggle to \textbf{autonomously solve the real world engineering problem}. \textbf{\uppercase\expandafter{\romannumeral 2}}: They remain \textbf{challenged in reasoning through complex logic problems}. To address these challenges, we developed the \textsc{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 \textsc{Infant Agent}, GPT-4o's accuracy on the SWE-bench-lite dataset rises from \(\mathbf{0.33\%}\) to \(\mathbf{30\%}\), and in the AIME-2024 mathematics competition, it increases GPT-4o's accuracy from \(\mathbf{13.3\%}\) to \(\mathbf{37\%}\). |
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subjects | Accuracy Infants Large language models Operators (mathematics) Reasoning Task complexity |
title | Infant Agent: A Tool-Integrated, Logic-Driven Agent with Cost-Effective API Usage |
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