ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step
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TL;DR - ScrambleToolBench is an interactive terminal benchmark that strips semantic tool schemas away, forcing agents to discover hidden tool behavior purely through trial-and-error interaction. It matters because it shows frontier LLM agents can make initial discoveries but fail to adapt deductively when the environment shifts.
- Removes semantic cues and uses a continuous task curriculum so agents cannot lean on prior knowledge; behavior must be inferred from interaction alone.
- Adds dynamic stressors — mapping drift, stochastic action failures, and temporal execution windows — to test hypothesis revision as the environment changes.
- Under structural change, agents show belief inertia or revert to exhaustive search instead of deductive strategies like cycle tracing.
- Scaling test-time reasoning amplifies brute-force search rather than enabling deduction; persistent memory cuts compounding errors but still doesn't yield efficient structural inference.
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ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step
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Hugging Face upvotes 11
TL;DR - ScrambleToolBench is an interactive terminal benchmark that strips semantic tool schemas away, forcing agents to discover hidden tool behavior purely through trial-and-error interaction. It matters because it shows frontier LLM agents can make initial discoveries but fail to adapt deductively when the environment shifts.
- Removes semantic cues and uses a continuous task curriculum so agents cannot lean on prior knowledge; behavior must be inferred from interaction alone.
- Adds dynamic stressors — mapping drift, stochastic action failures, and temporal execution windows — to test hypothesis revision as the environment changes.
- Under structural change, agents show belief inertia or revert to exhaustive search instead of deductive strategies like cycle tracing.
- Scaling test-time reasoning amplifies brute-force search rather than enabling deduction; persistent memory cuts compounding errors but still doesn't yield efficient structural inference.