{"schemaVersion":"nuwa-record-evidence-1","batchId":"research-2026-10-28","baseSnapshotId":"2026-10-27.3","publication":"published","dossier":{"id":"protein-filtering","en":{"takeaway":"Initial suppression and durable safeguards are different claims.","question":"Is initial suppression from pretraining filtering also a durable safeguard?","design":"The authors evaluate pretraining filtering across several sizes of the ESM-2 protein language model, distinguishing initial proxy-task performance from durability under subsequent adaptation. Each model uses one training seed. Operational recovery methods are excluded from this record.","findings":["Initial suppression and durability under later adaptation are distinct claims. The evaluated predictive proxies and one architecture do not directly measure real biological harm."],"interpretation":"Safeguard evidence should address selectivity, initial effect and durability. Its connection with Deep Ignorance is complementary argument structure, not independent replication on the same model.","limits":"The study uses one architecture and one seed per trained model. Proxy prediction outcomes do not establish real biological harm or transfer to all language models.","connections":[],"decisionRelevance":"NUWA recommendation: Compare safeguard scope and durability with unlearning and access-control evidence, retaining architecture and adaptation boundaries."},"zh":{"takeaway":"作者在 ESM-2 的若干规模上区分初始能力压低与后续适应后的防护持久性。","question":"预训练过滤的选择性与持久性是否一致？","design":"作者在 ESM-2 蛋白语言模型的若干规模上评估预训练过滤，区分初始代理任务能力和后续适应条件下的持久性。每种模型只使用一个训练种子；本记录不展示可执行恢复方法。","findings":["初始能力压低和后续适应下的防护持久性属于不同主张；所测预测代理和单一模型架构不直接测量现实生物危害。"],"interpretation":"防护证据需要同时回答选择性、初始效果与长期持久性。与 Deep Ignorance 的联系是论证结构上的互补，不能视为同一模型上的独立复现。","limits":"每种模型单一训练种子、单架构；代理预测任务不是现实生物危害，不外推至所有大语言模型。","connections":[],"decisionRelevance":"NUWA 建议：接入 Deep Ignorance 与 unlearning 研究的范围比较；不收集可执行恢复方法。"},"assertionPolicy":{"findings":"source-finding","interpretation":"nuwa-interpretation","decisionRelevance":"recommendation"},"observations":[],"relatedWorkIds":["deep-ignorance"],"supplementSourceId":"protein-filtering","batchId":"research-2026-10-28","sourceRefs":[{"sourceId":"dr-20261028-protein-filtering","url":"https://arxiv.org/html/2610.26651","title":"The Effectiveness and Durability of Pretraining Data Filtering in Protein Language Models","locator":"Abstract; §4.4 limitations; conclusion; text lines 00271","version":"Original resource captured 2026-10-28T07:25:52.921030+00:00; sha256:83a3c96f121b7de5bcb6ba87f388d6ac39e33416fd00b70cbcf68d3235ac7670","artifactSha256":"83a3c96f121b7de5bcb6ba87f388d6ac39e33416fd00b70cbcf68d3235ac7670","textSha256":"5e4f05b3088329b3d6ea82417774119d3c1102e72bcc082ec6e2a5536d461ccb","capturedAt":"2026-10-28T07:25:52.921030+00:00","resourceEdition":"arXiv:2610.26651v1 [cs.LG] 19 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":"0e247cd4a71def075c9f138766e732cdefdbeddecfc9daf441ffa5179df12dd6"}},"update":null}