COLM Best Paper: Int-Bench Shows LLM Assistants Intervene Too Early and Too Often
maxhkw · x · 2026-10-10
The paper AI Assistants Overassist by Verona Teo, Raghav Jain, Tobias Gerstenberg and Max Kleiman-Weiner won Best Paper at the COLM Workshop on Agent Behavior.
- The authors introduce Int-Bench, a simulation-based benchmark where a "teacher" LLM watches a "student" solve problems and decides whether, when, and how to intervene — across code debugging, math, and brain teasers.
- Findings: LLM teachers intervene more frequently and earlier than humans, and tend to hand over complete solutions instead of targeted hints.
- This suggests current LLM assistants optimize for short-term task success rather than supporting the reasoning process needed for deeper learning and generalization.
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