Agents' Overreliance on Unreliable Tools
LLM agents use tools to access information and perform computations beyond their parametric knowledge. Existing tool-use benchmarks evaluate whether agents select and call the right tools, assuming that tool returns are reliable. However, tools can return plausible but incorrect outputs. We evaluate 14 models with three tools (web search, an LLM sub-agent, and a code executor), corrupting their returns to examine whether agents overrely on unreliable tools. Agents adopt corrupted returns at high rates, with mean adoption exceeding one third for every tool and reaching 68.0% for web search. Corrupted returns also frequently override correct answers that agents give without tools, and this ...
| Authors | Hoyeol Yang, Woojung Song, Taewon Kim et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 September 2026 |
| Last updated at source | 26 September 2026 |
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