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"Open-Sourcing Highly Capable Foundation Models" (arXiv v1, submitted 29 September 2023) states that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits, and names access to biological weapons knowledge among the dangerous capabilities at issue. Copy claim link
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Machine-checked, not reviewed by an editor Stated in source Supported
Supporting evidence: primary · 1 source
Subject version arXiv v1 (submitted 29 September 2023) · Published 2023-09-29 · Effective date not established Date 2023-09-29 · publication Role Earliest located Register (kind of text) Nonfiction Scope of the passage Scoped by the authors to some highly capable models expected in the near future. Scope and limits The record documents what this report states. Its claim is about models it expects in the near future, not about models available at its date, and the record does not extend it to any named model. It is not evidence that the proposition is true.
The evidence 02 primary source Supports the claim
Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives ↗ arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others) · Publication date unknown
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Source excerpt “we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits” Location in source Abstract, PDF p. 1
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-30
Source details and check notes Authors arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others) Version Retrieved for the candidate pack on 2026-10-28 (SHA-256 dc8784fd105170de8039888528167a17ba0df9da32aa8455bc423255f73cd9d8). arXiv v1, submitted 29 September 2023 (announced under identifier 2311.09227) Retrieved again at admission on 2026-10-30 (SHA-256 dc8784fd105170de8039888528167a17ba0df9da32aa8455bc423255f73cd9d8, the same bytes). Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Attribution arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others), “Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives”. 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 (27 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 report is the primary source for its own argument. A cross-family machine check (2026-10-30, item 103) re-read the record’s sources and found full support. No human editorial review. primary source Supports the claim
Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives ↗ arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others) · Publication date unknown
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Cite the original source Cite the original document, not Selekoa. Check these details against it.
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Source excerpt “Dangerous capabilities that highly capable foundation models could possess include making it easier for non-experts to access known biological weapons or aid in the creation of new ones” Location in source §2.1 "What are Highly Capable Foundation Models?", paragraph headed "Extreme risks and harms.", PDF p. 7
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-30
Source details and check notes Authors arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others) Version Retrieved for the candidate pack on 2026-10-28 (SHA-256 dc8784fd105170de8039888528167a17ba0df9da32aa8455bc423255f73cd9d8). arXiv v1, submitted 29 September 2023 (announced under identifier 2311.09227) Retrieved again at admission on 2026-10-30 (SHA-256 dc8784fd105170de8039888528167a17ba0df9da32aa8455bc423255f73cd9d8, the same bytes). Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Attribution arXiv (preprint, Elizabeth Seger and 21 other authors; Centre for the Governance of AI and others), “Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives”. 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. Shows that the biological case is among the risks the argument rests on. A cross-family machine check (2026-10-30, item 103) 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. Submission date of arXiv v1 as given by the abstract page and the PDF's own stamp, although the identifier belongs to the November 2023 series.
Record ID ab-arg-d6-seger-articulates