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Can LLMs Catch a Rigged Backtest? A Clean-Control Calibration Benchmark

Paper recorded by Signals 4 on 2026-09-23 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-23 on arXiv · recorded by Signals 4 on 2026-09-24

Category: cs.CL · 自然语言处理 · first seen 2026-09-24

Abstract

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 p

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#11 most recent of 239 cs.CL papers we have recorded · ↑ newer: Scaling Attention Head Analysis via Gradient-Based Attribution in Cont · ↓ older: Reference-Based Analysis of Coherence and Diversity in Open-Ended Text
Cite this page: Can LLMs Catch a Rigged Backtest? A Clean-Control Calibration Benchmark: the #11 most recent of 239 cs.CL papers we have recorded (as of 2026-09-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/can-llms-catch-a-rigged-backtest-a-clean-control-calibration-benchmark.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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