Claim · 2024-07 · publication · Stated in source · Supported

NIST AI 800-1 (initial public draft): Managing Misuse Risk for Dual-Use Foundation Models cites Training Compute-Optimal Large Language Models.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

NIST AI 800-1 (initial public draft): Managing Misuse Risk for Dual-Use Foundation Models → cites → Training Compute-Optimal Large Language Models

Where it appears

Frontier AI frameworks and law, 2023–2026 →

Claim and evidence

NIST AI 800-1 (initial public draft): Managing Misuse Risk for Dual-Use Foundation Modelscites ↓Training Compute-Optimal Large Language Models

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Subject version: Initial public draft (July 2024) · Published 2024-07 · Effective date not established
Date: 2024-07 · publication Records dated 2024 →

Scope and limitsAn explicit reference only; neither agreement nor influence. The citing text does not identify which version of the work was read.

The evidence 01

primary sourceSupports the claim

NIST AI 800-1 Initial Public Draft: Managing Misuse Risk for Dual-Use Foundation Models (July 2024)

National Institute of Standards and Technology · 2024-07

Archived copy, captured 2024-07-27: NIST AI 800-1 Initial Public Draft: Managing Misuse Risk for Dual-Use Foundation Models (July 2024)

Cite the original source

Cite the original document, not Selekoa. Check these details against it.

Paraphrase

The text names the work by its title.

Location in source PDF p. 25, reference or mention by title

Machine-checked · 2026-10-31

Source details and check notes

Authors: U.S. AI Safety Institute (NIST)
Version: Retrieved on 2026-10-27 (SHA-256 30ced091493af421d454ca138a3496a39b8c6079f74223e868325318ffa767e4). The cross-family check of 2026-10-28 opened the Internet Archive capture linked here and matched the quoted wording in it, the release-period expression (decision 9); a capture shows only what the archive retrieved that day. Publisher as printed on the inspected expression: National Institute of Standards and Technology (U.S. AI Safety Institute).
Canonical address: Open the canonical source ↗
Accessed: 2026-10-27
Language: English (en)
Archive copy: Open archived copy, captured 2024-07-27: NIST AI 800-1 Initial Public Draft: Managing Misuse Risk for Dual-Use Foundation Models (July 2024). An archive copy shows only what the archive retrieved on 2024-07-27. It does not prove what the page said earlier, and it can differ from the copy checked on 2026-10-27.
Attribution: U.S. AI Safety Institute (NIST), “NIST AI 800-1 Initial Public Draft: Managing Misuse Risk for Dual-Use Foundation Models (July 2024)”, 2024-07.
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, key papers (research/key-papers). The citation was matched (title) in the text retrieved for the candidate pack on 2026-10-28 and re-found by its mechanical check. The paraphrase is Selekoa’s. 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. An explicit reference to a work. Citation alone is neither agreement nor causal influence.

Date. Date of the citing source as recorded in the dataset; unknown where the dataset has none.

Record ID kp-cites-nist-ai-800-1-ipd-2024-compute-optimal-language-models