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The RAND working paper of 12 June 2025 states that safety assessments rely on benchmarks that overlook the full range of potential threat actors and fail to account for how AI could enhance the capabilities of skilled individuals as well as enabling non-experts. 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 Earliest located Register (kind of text) Nonfiction Scope of the passage States the point as a criticism of benchmark coverage. Scope and limits The record documents what this working paper states about the scope of published assessments; it is not evidence that the proposition is true, and the paper is marked by its publisher as not formally reviewed or edited. This record holds the earliest located articulation from admission on 2026-10-30: the earlier passage the pack headlined does not meet the criterion (cross-family check, item 102), and this one states that safety assessments fail to cover skilled actors. The replaced headline stays on record as a correction. Only the summary's argument about assessment scope is cited; the paper's 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 “Second, safety assessments rely on imperfect benchmarks that overlook the full range of potential threat actors, failing to account for how AI could enhance the capabilities of skilled individuals in addition to its ability to enable or "uplift" nonexperts.” Location in source Summary, p. iv (PDF p. 4)
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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 (39 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 118) 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 118) 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, attested by the arXiv listing page; the paper's own cover gives only June 2025.
Record ID ab-arg-d7-brent-articulates