AGENTIC-SYSTEMSMay 5, 20261 min read7 views

Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents

Researchers reveal the "tool-use tax," showing that tool-augmented reasoning can underperform compared to native Chain-of-Thought due to protocol overhead. The study introduces G-STEP to mitigate these errors while highlighting the need for stronger intrinsic reasoning.

Computer Science > Artificial Intelligence

Title:Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents

View PDF HTML (experimental)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.

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Source: arXiv cs.AI Recent

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