From Proof to Program: Characterizing Tool-Induced Reasoning Hallucinations in Large Language Models
This addresses the reliability of reasoning in AI systems that use external tools, which is crucial for applications requiring trustworthy problem-solving, though it is incremental as it builds on existing tool-augmentation research.
The paper tackles the problem of tool-augmented language models (TaLMs) producing untrustworthy reasoning by treating tool outputs as substitutes, a failure mode termed Tool-Induced Myopia (TIM), and finds that while TaLMs achieve up to a 19.3 percentage point gain in final-answer accuracy, their reasoning deteriorates, with TIM present in ~55% of high-risk cases.
Tool-augmented Language Models (TaLMs) can invoke external tools to solve problems beyond their parametric capacity. However, it remains unclear whether these tool-enabled gains reflect trustworthy reasoning. Focusing on the Code Interpreter tool, we show that even when tools are selected and executed correctly, TaLMs treat tool outputs as substitutes for reasoning, producing solutions that appear correct but lack coherent justification. We term this failure mode Tool-Induced Myopia (TIM), and study it using PYMATH, a benchmark of 1,679 competition-level mathematical problems for which Python code is helpful but not sufficient. We further develop a multi-dimensional evaluation suite to quantify reasoning degradation in TaLMs relative to their non-tool counterparts. Our findings reveal that while TaLMs achieve up to a 19.3 percentage point gain in final-answer accuracy, their reasoning behavior consistently deteriorates (e.g., non-tool LLMs win up to 41.5% more often in pairwise comparisons of the reasoning process). This degradation intensifies with tool use; the more frequently a model invokes tools, the less coherent its reasoning becomes. Moreover, tool use shifts errors from arithmetic mistakes toward global reasoning failures (logic, assumption, creativity); with TIM present in ~55% of high-risk cases. Finally, we propose a preference-optimization-based framework that realigns TaLMs to use tools as assistive evidence, improving both final-answer accuracy and reasoning depth under tool use. Codes and data are available at: https://github.com/megagonlabs/TIM.