AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
[Submitted on 14 Oct 2026] Title:When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus View a PDF of the paper titled When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus, by Adarsh Agrawal (Independent Researcher) View PDF HTML (experimental) Abstract:Correctness feedback suppresses the very evidence that specification-completeness auditing is designed to uncover: programs that satisfy a written contract but violate intended behavior. We demonstrate this objective reversal on 12 Verus/Rust tasks across four model families under matched call budgets. Independent no-feedback discovery more than doubles certified-witness yield relative to IntentShield's corrective-feedback loop (59.0% versus 27.1%). A format-matched iterative control places the larger share of this gap in feedback content, not independent restarts. The advantage holds in every model family and survives parser repair. A separate phase-separated execution matches the aggregate yield without reusing responses; most certified witnesses also fail held-out cases. Preserving these witnesses delivers stronger specification repair: Phase-Revised contracts achieve 81.3% implementation success, versus 62.5% for IS-Revised, with Direct-Revised close behind. Both no-feedback witness sources substantially outperform weakened contracts. The effect does not reproduce in the available Dafny and Lean evaluations, establishing an empirical boundary around the studied Verus/Rust workflow. Completeness auditing and correctness repair have opposing immediate objectives. Preserve and certify the admitted failure before repairing it. Comments: Paper accepted at the NeurIPS 2026 Workshop on AI for Verifiable Coding Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2610.20676 [cs.AI] (or arXiv:2610.20676v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2610.20676 arXiv-issued DOI via DataCite Submission history From: Adarsh Agrawal [view email] [v1] Wed, 14 Oct 2026 14:43:24 UTC (1,388 KB) Full-text links: Access Paper: View a PDF of the paper titled When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus, by Adarsh Agrawal (Independent Researcher) View PDF HTML (experimental) TeX Source view license Ancillary-file links: Ancillary files (details): README.md camera_ready_results.json make_camera_ready_figures.py source_manifest.json validate_arxiv.py Additional Features Audio Summary Current browse context: cs.AI new | recent | 2026-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)