Decoy-Calibrated Failure Audits for Language Models

Abstract

Useful audits reveal not only how often a model fails, but also where its failures concentrate. An auditor may test many candidate explanations: long inputs, indirect questions, distracting evidence, or combinations of these factors. The risk is selection. The largest observed effect may reflect a real failure mode, or it may simply be the best result among many tried. We introduce Janus, a procedure for deciding when a proposed error explanation is credible enough to report. The goal is not to generate new explanations, but to decide which ones hold up. The auditor starts with a fixed model, a labeled evaluation set, and a frozen list of candidate explanations, which we call descriptors. Janus scores each descriptor by its error-rate lift, then compares real descriptors with fake ones that have the same frequencies but are randomly assigned to examples. A descriptor is confirmed only if it beats this decoy floor on the data used for discovery and then repeats on separate held-out data. In a controlled audit of multi-table lookup tasks, Janus identifies the planted failure, confirming long-chain descriptors and their interactions. The LLM often stops partway through the lookup chain instead of reaching the final answer. On two public benchmarks, MuSiQue and LongBench v2, the SliceLine baseline flags plausible high-error pockets, but Janus confirms none of them. Ablations show why both safeguards matter. On LongBench v2, an uncalibrated fixed threshold reports 20 descriptors, the decoy floor leaves one, and the holdout check rejects the last one after its lift shrinks from 0.36 to 0.05. The resulting principle separates proposing explanations from reporting them. Candidates may come from any source, but only those that beat decoys and replicate on fresh data become audit findings.