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Results are framed as the paper's findings rather than as established fact.","excerpt":"Authors:Jae Hun Hwang, Hong-In Won"},{"dimension":"summary","status":"uncertain","explanation":"Central jargon goes unexplained for non-specialists: executable model, finite guard repairs, two-stage devices, information-gain selection and workload-valued testing. The reading also blurs a key finding into 'depended on how repairs were weighted': uniform normal learning beat AI-weighted (language-model-informed) learning by 540.75, and testing did not close that gap. It omits the small magnitudes (2.14 percent; 3.33 percent versus random). Smallest repair: state the uniform-beats-AI-weighted result plainly, give the percentages, and add plain-language glosses or tooltip suggestions for the core terms.","excerpt":"uniform normal learning costs 540.75 less than AI-weighted normal learning on this cohort, with a device-paired interval excluding zero"},{"dimension":"date","status":"pass","explanation":"originalPublished is 2026-10-29, which matches the v1 submission date. The feed date of 2026-10-31 is later and is correctly not used.","excerpt":"[Submitted on 29 Oct 2026]"},{"dimension":"support","status":"uncertain","explanation":"The listed claims match the abstract verbatim. However, the summary calls 'the paper's central point' that 'retaining alternatives is not enough by itself.' The abstract supports both retaining alternatives and evaluating selection together with weighting and learning; the 'not enough by itself' wording is an interpretive reframing presented as the authors' thesis. Smallest repair: use the abstract's own conclusion wording.","excerpt":"These results support retaining alternatives to a generated repair and evaluating diagnostic selection together with repair weighting and learning from execution."},{"dimension":"media","status":"pass","explanation":"The selected media is the verified direct arXiv abstract page for this paper. It is the most direct way to access the source.","excerpt":null}],"createdAt":"2026-11-11T11:40:25.340Z","model":"anthropic/claude-opus-5.5"}],"correctionUrl":"https://escapemeridian.com/corrections/"}