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LLM tool use reveals a "knowing-doing gap" in model-adaptive necessity

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new definition of tool necessity for large language models (LLMs) that accounts for model-specific capabilities. This definition revealed significant mismatches, ranging from 26.5% to 54.0%, between observed tool-use behavior and actual necessity across four models on arithmetic and factual question-answering tasks. The study further identified a "knowing-doing gap" in LLM tool use, where models often struggle to translate their recognition of tool necessity into actual tool-calling actions, with most mismatches occurring during this cognition-to-action transition. AI

IMPACT Highlights a critical area for improvement in LLM agent reliability, focusing on the translation of internal decision-making to external actions.

RANK_REASON Research paper published on arXiv detailing a new definition and analysis of LLM tool use. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM tool use reveals a "knowing-doing gap" in model-adaptive necessity

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17 / 100
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Research paper published on arXiv detailing a new definition and analysis of LLM tool use. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yize Cheng, Chenrui Fan, Mahdi JafariRaviz, Keivan Rezaei, Soheil Feizi ·

    Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Use

    arXiv:2605.14038v3 Announce Type: replace Abstract: Large language models (LLMs) increasingly act as autonomous agents that must decide when to answer directly vs. when to invoke external tools. Prior work studying adaptive tool use has largely treated tool necessity as a model-a…

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