Episode Details

We previously announced a forthcoming research journal for AI alignment. This cross-post from our blog describes our tentative plans for the features and policies of the journal, including experiments like reviewer compensation and reviewer abstracts. It is the first in a series of posts that will go on to discuss our theory of change, comparison to related projects, possible partnerships and extensions, scope, personnel, and organizational structure.

The journal is being built to serve the alignment research community. This post's purpose is to solicit feedback and encourage you to contact us here if you want to participate, especially if you are interested in becoming a founding editor or part-time operations lead. The current plans are merely a starting point for the founding editorial team, so we encourage you to suggest changes and brainstorm the ideal journal.

Summary

The Alignment journal will be a fast and rigorous venue for AI alignment. We intend to:

• Improve and disseminate research on alignment that emphasizes scientific and conceptual understanding and de-emphasizes capabilities obtained through empirical hill climbing (e.g., benchmaxxing)
• Combine the best features from traditional journals, ML conferences, internet forums, and social media while avoiding their pathologies: existing academic venues can [...]

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Outline:

(01:03) Summary

(04:29) Motivation: Why a journal? Why these features?

(07:07) Journal not conference

(08:41) Journal features: details

(08:46) Process transparency

(09:08) Reviewer abstracts

(13:04) Reviewer Abstracts for the ODYSSEY Conference

(13:41) Reviewer compensation

(18:53) Reviewer Compensation for the ODYSSEY Conference

(19:56) Reviewer matching

(21:45) Semi-confidential review

(23:46) Review discussion streamlining

(25:08) AI usage

(26:23) Quality recognition

(27:41) Archival venue

(28:48) Web-first open formatting

(31:38) Open choices

(32:19) Credits and thanks

(32:44) Appendix 1: ODYSSEY Reviewer Abstract Examples and Instructions

(33:26) Wide Neural Networks as a Baseline for the Computational No-Coincidence Conjecture

(33:38) Author abstract

(34:22) Reviewer abstract, by an anonymous reviewer

(35:54) Communication & Trust

(36:03) Author abstract

(37:03) Reviewer abstract, by Daniel Alexander Herrmann

(40:31) A Model for Scaling Laws of General Intelligence

(40:41) Author abstract

(41:39) Reviewer abstract, by Rif A. Saurous

(43:55) Reviewer Abstract Instructions

(46:00) Appendix 2: Diamond Open Access Criteria

(47:57) Appendix 3: Editor-written abstracts instead?

The original text contained 11 footnotes which were omitted from this narration.

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First published:
April 7th, 2026

Source:
https://www.lesswrong.com/posts/hvq7amw8FKZxEeKqA/an-alignment-journal-features-and-policies

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Narrated by TYPE III AUDIO.