{"schemaVersion":"nuwa-record-evidence-1","batchId":"research-2026-10-28","baseSnapshotId":"2026-10-27.3","publication":"published","dossier":null,"update":{"id":"asimov","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":"9d5714ac10d4ef9765da15e8e3107589c760f22752adbeeb5f4be323c7f35267"},"text":{"en":"Hazard recognition, chat refusal, control actions and damage are distinct endpoints. RoboHarm shows interface-sensitive refusal; the simulation benchmarks add unsafe success, damage and process costs while still needing real-environment calibration.","zh":"危险识别、聊天拒答、控制动作与造成损伤是不同终点。RoboHarm 说明接口变化会改变拒答表现；两种仿真基准补充不安全成功、损伤和过程成本，仍需要真实环境校准。"},"sourceRefs":[{"sourceId":"dr-20261028-roboharm","url":"https://arxiv.org/html/2610.14043","title":"Refuses in Chat, Complies with Arms: LLM Refusal Does Not Survive Robot Control","locator":"Abstract; §§2–4; discussion; text lines 00044,00055,00061","version":"Original resource captured 2026-10-28T07:24:43.704880+00:00; sha256:c5b843fd1a59541e7c7713423f4c765813de1b6dc8cb85bc7aa5913c0c945572","artifactSha256":"c5b843fd1a59541e7c7713423f4c765813de1b6dc8cb85bc7aa5913c0c945572","textSha256":"4fd25df8449d2e25923baf97b4d2ef6692da0becbb6ac0fe89e3c9efe8367f2e","capturedAt":"2026-10-28T07:24:43.704880+00:00","resourceEdition":"arXiv:2610.14043v1 [cs.HC] 03 Oct 2026"},{"sourceId":"dr-20261028-oopsieverse","url":"https://arxiv.org/html/2606.31993","title":"OopsieVerse: A Safety Benchmark with Damage-Aware Simulation for Robot Manipulation","locator":"§§III–V; limitations; text lines 00029,00303,00307,00309","version":"Original resource captured 2026-10-28T07:24:43.858466+00:00; sha256:e5c0c24c6eb494b5bef1612f1f6d5381aa9972a4aaa8937de7dcdb3e046a48d0","artifactSha256":"e5c0c24c6eb494b5bef1612f1f6d5381aa9972a4aaa8937de7dcdb3e046a48d0","textSha256":"53f0774311c19af8e93ddf7a19e99fcab802a3966baac2e18161202599d14f9d","capturedAt":"2026-10-28T07:24:43.858466+00:00","resourceEdition":"arXiv:2606.31993v1 [cs.RO] 30 Jun 2026"},{"sourceId":"dr-20261028-foresight-vla","url":"https://arxiv.org/html/2606.27079","title":"ForesightSafety-VLA: A Unified Diagnostic Safety Benchmark for Vision-Language-Action Models","locator":"§§III–V; discussion; text lines 00082,00084,00134,00141,00146,00147","version":"Original resource captured 2026-10-28T07:24:44.372873+00:00; sha256:303708eaa7bcf2e1d96cd551279703949de0dff652b1b5b86e0cd2dd5905329a","artifactSha256":"303708eaa7bcf2e1d96cd551279703949de0dff652b1b5b86e0cd2dd5905329a","textSha256":"822f818c7622e93b18b658aae2074150b78ce4d7fde7a340fc5acbeaded9cd67","capturedAt":"2026-10-28T07:24:44.372873+00:00","resourceEdition":"arXiv:2606.27079v2 [cs.RO] 27 Jun 2026"}],"relatedWorkIds":["roboharm","oopsieverse","foresight-vla"],"publication":{"state":"published","publishedOn":"2026-10-29","authorizationReference":"user-authorized-global-index-publication-2026-10-29","reviewedCandidateId":"local-6efe31bc4a0c0f37"}}}