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The RAND working paper of 12 June 2025 states that safety assessments mistakenly assume that biological weapons development requires tacit knowledge, that is, expertise gained through experience that cannot be conveyed in words. Copy claim link
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Supporting evidence: primary · 2 sources
Subject version RAND working paper WR-A3853-1, arXiv v1 (12 June 2025) · Published 2025-06-12 · Effective date not established Date 2025-06-12 · publication Role Contesting Register (kind of text) Nonfiction Scope of the passage Contests the premise as the paper states other assessments hold it. Engagement Engages a premise. The paper names the tacit-knowledge premise and argues against it from historical cases. Scope and limits The record documents what this working paper states about an assumption it attributes to other assessments; it is not evidence that the proposition is false, and the paper is marked by its publisher as not formally reviewed or edited. The paper's case studies and dialogues contain technical content. Only the summary's statement about the assumption is cited; that content was excluded and is not paraphrased.
The evidence 02 primary source Supports the claim
Contemporary AI foundation models increase biological weapons risk ↗ RAND Corporation (working paper WR-A3853-1), posted to arXiv · Publication date unknown
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Source excerpt “First, we argue they mistakenly assume biological weapons development requires tacit knowledge – expertise gained through experience that cannot be conveyed in words, including by large language models (LLMs).” Location in source Summary, p. iv (PDF p. 4)
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-30
Source details and check notes Authors RAND Corporation (working paper WR-A3853-1), posted to arXiv Version Retrieved for the candidate pack on 2026-10-28 (SHA-256 1cc64de0a880543bda08d64374ee47539c029c55b187ab3f4e66603e3f8ad7e5). arXiv v1 of 12 June 2025; the PDF is the RAND working paper WR-A3853-1 of June 2025, marked "not been formally reviewed or edited" Retrieved again at admission on 2026-10-30 (SHA-256 1cc64de0a880543bda08d64374ee47539c029c55b187ab3f4e66603e3f8ad7e5, the same bytes). Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Attribution RAND Corporation (working paper WR-A3853-1), posted to arXiv, “Contemporary AI foundation models increase biological weapons risk”. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, AI and biosecurity (research/ai-bio). The quotation (28 words) matched the text retrieved for the candidate pack on 2026-10-28 and again at admission on 2026-10-30, in a fresh retrieval whose SHA-256 equals the pack’s. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. The paper is the primary source for its own argument. A cross-family machine check (2026-10-30, item 116) re-read the record’s sources and found full support. No human editorial review. primary source Supports the claim
arXiv abstract page for 2506.13798 (submission history and report number) ↗ arXiv (Cornell University) · Publication date unknown
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Source excerpt “[v1] Thu, 12 Jun 2025” Location in source Submission history block, v1 entry
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-30
Source details and check notes Authors arXiv (Cornell University) Version Retrieved for the candidate pack on 2026-10-28 (SHA-256 64847d8461f1f0361daccfb569e8d6cc053bfd8a4125c5339eeda6ec8bb06c17). Abstract page as served on 2026-10-28; used only for the v1 submission date and the RAND report number Retrieved again at admission on 2026-10-30 (SHA-256 63834668f90e3a0e80b98abc1ee48e42834a3ecc9060acc685a1a048bafb24e2). Its wording can change; earlier wording is not authenticated. Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Attribution arXiv (Cornell University), “arXiv abstract page for 2506.13798 (submission history and report number)”. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, AI and biosecurity (research/ai-bio). The quotation (5 words) matched the text retrieved for the candidate pack on 2026-10-28 and again at admission on 2026-10-30, in a fresh retrieval (SHA-256 63834668f90e3a0e80b98abc1ee48e42834a3ecc9060acc685a1a048bafb24e2; the bytes differ from the pack’s retrieval). Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. Added after the audit of 2026-10-28: the day of the date rested on no cited passage. Cited for the submission date only; the retrieved PDF carries no arXiv stamp and its cover gives only the month. A cross-family machine check (2026-10-30, item 116) re-read the record’s sources and found full support. No human editorial review. Status and meaning Editorial status. Not reviewed by an editor . An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.
Relation. A passage of the document disputes the proposition or a premise it depends on. The record documents the text’s position, not who is right.
Date. Date of the arXiv v1 submission of the working paper, attested by the arXiv listing page; the paper's own cover gives only June 2025.
Record ID ab-arg-d2-brent-contests