Can LLMs Catch a Rigged Backtest? A Clean-Control Calibration Benchmark
Backtest auditing is a calibration problem: high flaw recall is not useful when the model falsely flags matched clean strategies. We build a 96-item paired benchmark in which every flawed backtest has a clean control that holds strategy, dates, code style, labels, and reporting scaffold fixed while changing one methodology detail. A deterministic scorer separates flaw recall, clean-control false positives, evidence localization, and fix relevance. Over 1440 cached audits from four text endpoints, the primary DeepSeek auditor reaches 100.0\% closed and clean-aware code recall, but open prompts over-flag 93.8\% of clean code controls, and clean-aware all-three specificity is 87.5\% even whe...
| Authors | Makar Ulesov, Vladislav Smirnov, Omar Ibrahim et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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