DOCSCHISEL: Adaptive Tool Documentation Optimization Framework for LLM Agents
How an adaptive optimization framework is solving the hidden documentation bottleneck in AI agent performance.
Large language models (LLMs) increasingly rely on external tools to accomplish complex real-world tasks, making tool documentation a critical grounding resource for LLM agents. Existing studies mainly focus on improving the tool-use capabilities of LLM agents, while largely treat...
ArXiv CS.LG
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This research reveals that tool documentation for LLMs is not a one-size-fits-all component, proving that dynamic optimization is essential for reliability. By iteratively refining documentation based on execution failures, DocsChisel significantly boosts agent success rates without the need for model retraining.
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