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The RAND working paper "Contemporary AI foundation models increase biological weapons risk" (WR-A3853-1, arXiv v1 of 12 June 2025) states that contemporary AI models can meaningfully contribute to biological weapons development, against published safety assessments that assert minimal risk. Copy claim link
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Machine-checked, not reviewed by an editor Stated in source Supported
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 Articulation Register (kind of text) Nonfiction Scope of the passage Stated as a finding of the paper's own model dialogues, which are not described here. Scope and limits The record documents what this working paper states. The paper's own cover marks it as not formally reviewed or edited. The record is not evidence that the proposition is true and implies no influence between texts. The paper's summary and body contain technical content, including named model dialogues about a specific laboratory task. Only the summary's argument-level sentences are cited; the technical 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 “In finding that contemporary AI models can meaningfully contribute to biological weapons development, our analysis challenges the results of published safety assessments that assert that these models pose minimal risk.” 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 (30 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 text is the primary source for its own statement. A cross-family machine check (2026-10-30, item 115) 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 115) 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 meets the proposition’s criterion. The record documents what the text states, in its register (fiction and satire are labelled); it is not evidence that the proposition is true and implies no influence on or from another text.
Date. Date of the arXiv v1 submission of the working paper, whose cover reads June 2025.
Record ID ab-arg-d1-brent-articulates