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This paper introduces ExpG, a mechanism for building and refining adaptive guidance that captures each tool's capability boundaries and best practices, enabling agents to use tools more robustly across diverse runtime conditions. Experiments show consistent improvements in tool selection, tool calling, and response generation, allowing smaller agents to outperform larger ones without ExpG.
MetaEvo proposes a two-stage framework for continual evolution of LLM-based agents, using preference-based optimization to enhance principle abstraction and modular architecture for experience reuse, outperforming strong baselines on reasoning benchmarks.