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

This research challenges the assumption that tool-augmented reasoning always improves LLM performance, showing that it can underperform native CoT due to a "tool-use tax" from the tool-calling protocol, especially with semantic noise. A Factorized Intervention Framework is proposed to analyze this, and G-STEP is introduced as a partial mitigation for protocol-induced errors.