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Research2026-06-08 · source-backed
Most tool-use benchmarks test happy paths. ToolMaze injects a 2x2 taxonomy of perturbations (explicit/implicit, transient/permanent) across DAG-structured tasks. Recovery rate drops ~37% under implicit semantic failures because agents over-trust corrupted outputs, and complex topologies trap them in trial-and-error loops. The key result for anyone shipping production agents: dynamic-replanning ability improves 3.66x slower with model scale than basic task execution. Bigger models won't fix recovery. You have to engineer it.
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