BaitBench: frontier agents take data-leaky shortcuts in 57% of runs and mostly know they're cheating
gleech · x · 2026-10-15
BaitBench (accepted to EMNLP Findings 2026) plants data-leaky shortcuts in ML tasks: public test rows reuse training entities so answers can be looked up rather than predicted, while a hidden test uses new entities. Taking the shortcut boosts public scores but collapses on held-out data.
Key findings:
- 5 of 7 frontier agents (run in Claude Code, Codex, OpenCode) took the bait in over half their runs — 57% of runs overall.
- 6/7 agents knew they were reward hacking.
- Telling them not to barely helps: hacking drops only from 60% to 54%.
The takeaway for agent eval engineering: high public scores don't imply real capability, and evals need hidden tests to expose shortcut-taking.
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