{"schemaVersion":"nuwa-record-evidence-1","batchId":"research-2026-10-28","baseSnapshotId":"2026-10-27.3","publication":"published","dossier":{"id":"reprobait","en":{"takeaway":"Reconstruction fidelity and memorisation need separate treatment in tool-access governance.","question":"How does tool reconstruction evidence affect access-control arguments?","design":"The study evaluates reconstruction of selected scientific tools under differing information conditions. Table 2 reports best-of-three performance normalised to the original tool’s metric. This record retains high-level design, partial reconstruction and the limits of memorisation diagnostics.","findings":["Some outcomes are partial reconstructions. A normalised ratio is not a general task success rate, and limited memorisation diagnostics do not identify its causal contribution to each result."],"interpretation":"An access-control case must distinguish original-tool availability, replacement fidelity and actual human uplift. One normalised score does not establish the links between them.","limits":"The selected tools and best-of-three statistic constrain generalisation. Memorisation diagnostics offer limited evidence rather than causal attribution.","connections":[],"decisionRelevance":"NUWA recommendation: Use as bounded evidence for the assumptions in a tool-access safety case; preserve normalisation and attempt selection."},"zh":{"takeaway":"跨信息条件测试工具重建；原表为三次尝试的最佳值并相对原工具指标归一，部分成果只是局部重建。","question":"限制信息访问能否阻止科研工具被重建？","design":"研究在不同信息条件下测量编码智能体对选定科学工具的重建。表 2 使用三次尝试的最佳结果并相对原工具指标归一；这里只保留高层测量设计、部分重建与记忆诊断边界。","findings":["部分结果属于局部重建。相对原工具指标的归一比例不是通用任务成功率；有限的记忆诊断不能确定记忆对每项结果的因果贡献。"],"interpretation":"访问限制的安全论证需要区分原工具可得性、替代工具保真度与实际人类增益。它们之间的推理不能由单个归一分数替代。","limits":"仅限选定工具；记忆诊断为中等证据且不识别因果贡献；归一比例不是通用任务成功率。","connections":[],"decisionRelevance":"NUWA 建议：补充访问控制论证所需的重建证据，仅保留非操作性方法与边界。"},"assertionPolicy":{"findings":"source-finding","interpretation":"nuwa-interpretation","decisionRelevance":"recommendation"},"observations":[],"relatedWorkIds":["deep-ignorance"],"supplementSourceId":"reprobait","batchId":"research-2026-10-28","sourceRefs":[{"sourceId":"dr-20261028-reprobait","url":"https://arxiv.org/html/2610.19810","title":"ReproBAIT: Can coding agents build their own biological AI tools?","locator":"Abstract; Table2 caption; §3; limitations; text lines 00207","version":"Original resource captured 2026-10-28T07:25:52.920583+00:00; sha256:cdc5d63c54b48afc059393a7b154a5670ae73b72361536695ada5890520234f1","artifactSha256":"cdc5d63c54b48afc059393a7b154a5670ae73b72361536695ada5890520234f1","textSha256":"2011ce04644811678df376ca56c266fc53901266551409c68506adfd26ae414d","capturedAt":"2026-10-28T07:25:52.920583+00:00","resourceEdition":"arXiv:2610.19810v1 [cs.AI] 14 Oct 2026"}],"publication":{"state":"published","publishedOn":"2026-10-29","authorizationReference":"user-authorized-global-index-publication-2026-10-29","reviewedCandidateId":"local-6efe31bc4a0c0f37"},"review":{"state":"published-note","sourceScope":"full-text","availableScope":null,"readingCoverage":"selected-original-sections","completePaperAudit":false,"independentEvidenceReview":"pending-human-review","bilingualEditorialReview":"pending-human-review","authorship":"model-assisted-research-note","preparedFingerprint":"c0887ee4521de31e75fec04c1872987ddb6a212718da69e97f8f04876b0a7d11"}},"update":null}