DOCSCHISEL: Optimizing Tool Documentation for LLM Agents
A new research paper introduces DOCSCHISEL, an adaptive framework for optimizing tool documentation in large language model (LLM) agents. The study, available on arXiv (2608.10037), addresses the gap in understanding how tool documentation impacts agent performance. The authors conducted a large-scale empirical study revealing that existing tool documentation varies significantly in information fields, and the effectiveness of these fields depends on the task domain, LLM backbone, and agent paradigm. This suggests that no single fixed documentation works universally. DOCSCHISEL aims to adaptively optimize documentation to improve agent performance. The paper is categorized as a cross-type announcement and focuses on the intersection of AI, LLMs, and tool use.
Key facts
- Paper introduces DOCSCHISEL, an adaptive tool documentation optimization framework for LLM agents.
- Study conducted a large-scale empirical analysis of tool documentation.
- Found substantial heterogeneity in information fields across existing tool documentation.
- Effectiveness of information fields depends on task domain, LLM backbone, and agent paradigm.
- No fixed tool documentation can be universally optimal.
- Research addresses a gap in existing studies that treat documentation as fixed input.
- Paper available on arXiv with identifier 2608.10037.
- Announcement type is cross.
Entities
Institutions
- arXiv