Story · Semantic Scholar
Retry, Switch, or Abstain? Learning Strategy-Aware Tool-Use Policies via Controlled Error Injection (Semantic Scholar)
paper · Story page
BENCH2ROBUST injects transient, persistent and silent tool failures into benchmarks that never fail, so retry, switch and stop become trainable choices. Across 7 models from 4 families the recovery gap is near universal; Bayesian Tool Memory adds up to 16.8 points without retraining.
In plain words
- Researchers created controlled tests where tools can fail briefly, permanently, or without reporting an error.
- Each task forces action-taking artificial intelligence to retry, switch tools, or stop when no workable option remains.
- A memory system records tool reliability, helping the artificial intelligence choose whether to retry, switch, or stop.
- Guided practice separately teaches recovery behavior that remains useful when the memory system is absent.
- This gives teams a way to prepare action-taking artificial intelligence for failures that ordinary tests leave out.
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Aug 18, 2026 · in the sections
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