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Knowledge-Augmented Tool Execution
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knowledge-augmented-tool-execution-423f65f0·1 events·first seen 7d agoAliases: Knowledge-Augmented Tool Execution
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KATE framework improves LLM tool calling via experiential knowledge integration and parallel reasoning
Researchers present KATE (Knowledge-Augmented Tool Execution), a framework addressing LLM failures in multi-step tool use by systematically studying knowledge acquisition, activation, and internalization. Key findings include that instance-level experiential knowledge outperforms abstract intent-level knowledge, that expanding reasoning width via parallel sampling with aggregation beats deeper chain-of-thought, and that reinforcement learning outperforms supervised fine-tuning for knowledge internalization. KATE is evaluated on BFCL-V3 and AppWorld benchmarks, showing consistent improvements over strong baselines across model scales.