[2605.00136] Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents
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arXiv:2605.00136 (cs)
[Submitted on 30 Apr 2026]
Title:Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents
Authors:Kaituo Zhang, Zhen Xiong, Mingyu Zhong, Zhimeng Jiang, Zhouyuan Yuan, Zhecheng Li, Ying Lin
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Abstract:Tool-augmented reasoning has become a popular direction for LLM-based agents, and it is widely assumed to improve reasoning and reliability. However, we demonstrate that this consensus does not always hold: in the presence of semantic distractors, tool-augmented reasoning does not necessarily outperform native CoT. To explain this performance gap, we propose a Factorized Intervention Framework that isolates the cost of prompt formatting, the overhead of the tool-calling protocol, and the actual gain from executing tools. Our analysis reveals a critical tradeoff: under semantic noise, the gains from tools often fail to offset the "tool-use tax", which is the performance degradation introduced by the tool-calling protocol itself. To address this, we introduce G-STEP, a lightweight inference-time gate to mitigate protocol-induced errors. While this yields partial recovery, our findings suggest that more substantial improvements still require strengthening the model's intrinsic reasoning and tool-interaction capabilities.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.00136 [cs.AI]
(or arXiv:2605.00136v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.00136
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arXiv-issued DOI via DataCite (pending registration)
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From: Kaituo Zhang [view email] [v1] Thu, 30 Apr 2026 18:46:01 UTC (2,050 KB)
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