AI Daily — May 4, 2026
2026-05-04
arxiv.org
Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents
Researchers challenge the assumption that tool-augmented LLM agents always outperform chain-of-thought reasoning, finding that under semantic noise, tool-calling protocols can introduce enough overhead (the 'tool-use tax') to negate the gains from actual tool execution. A Factorized Intervention Framework isolates three cost components: prompt formatting overhead, the tool-calling protocol cost, and the net gain from tool execution. This has practical implications for agent system design, suggesting that naive tool integration can hurt performance in noisy real-world settings.