Pith. sign in

REVIEW 3 cited by

STREAM (ChemBio): A Standard for Transparently Reporting Evaluations in AI Model Reports

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2508.09853 v2 pith:ZNF3S7HM submitted 2025-08-13 cs.CY cs.AI

STREAM (ChemBio): A Standard for Transparently Reporting Evaluations in AI Model Reports

classification cs.CY cs.AI
keywords evaluationsstandardmodelreportschembioreportingresultsdevelopers
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Evaluations of dangerous AI capabilities are important for managing catastrophic risks. Public transparency into these evaluations - including what they test, how they are conducted, and how their results inform decisions - is crucial for building trust in AI development. We propose STREAM (A Standard for Transparently Reporting Evaluations in AI Model Reports), a standard to improve how model reports disclose evaluation results, initially focusing on chemical and biological (ChemBio) benchmarks. Developed in consultation with 23 experts across government, civil society, academia, and frontier AI companies, this standard is designed to (1) be a practical resource to help AI developers present evaluation results more clearly, and (2) help third parties identify whether model reports provide sufficient detail to assess the rigor of the ChemBio evaluations. We concretely demonstrate our proposed best practices with "gold standard" examples, and also provide a three-page reporting template to enable AI developers to implement our recommendations more easily.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting

    cs.AI 2026-06 unverdicted novelty 6.0

    EvalCards is a composable reporting schema and monitoring tool for AI evaluations, derived from 52 papers and 10 interviews, and applied to 5,816 models and 101,843 results to surface reporting gaps.

  2. NeurIPS Should Require Reproducibility Standards for Frontier AI Safety Claims

    cs.CY 2026-05 conditional novelty 5.0

    NeurIPS should enforce a three-tier disclosure framework plus mandatory claim inventories for papers asserting that frontier AI models are safe or ready for release.

  3. Risk Reporting for Developers' Internal AI Model Use

    cs.CY 2026-04 unverdicted novelty 4.0

    A harmonized risk reporting standard for internal frontier AI model use, structured around autonomous misbehavior and insider threats using means, motive, and opportunity factors.