Noise injection into model weights provides a model-agnostic detection signal for sandbagging because disrupting underperformance mechanisms produces anomalous performance improvement rather than degradation

## Review of PR **1. Schema:** The file is a claim with all required fields present (type, domain, description, confidence, source, created, title) and correctly formatted frontmatter. **2. Duplicate/redundancy:** This appears to be a revision of an existing claim rather than new enrichment injection; the changes refine the title, description, and body text while maintaining the same core evidence about noise injection detection from Tice et al., so no redundancy issues exist. **3. Confidence:** The confidence level is "likely" (downgraded from "experimental"), which is appropriate given the evidence shows peer-reviewed validation at NeurIPS 2025 with successful demonstration on Mistral Large 120B, though the method's real-world applicability remains limited by white-box access requirements. **4. Wiki links:** The related claims array contains several wiki links including `[[ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring]]` and others that may or may not resolve, but as instructed, broken links do not affect the verdict. **5. Source quality:** The source (Tice et al., NeurIPS 2025) is a peer-reviewed conference paper from a top-tier ML venue, tested on a large production model (Mistral Large 120B), providing strong credibility for the technical claims. **6. Specificity:** The claim is falsifiable—someone could demonstrate that noise injection does NOT produce asymmetric performance responses, or that sandbagging models degrade monotonically like non-sandbagging models, making it sufficiently specific. <!-- VERDICT:LEO:APPROVE -->