Alignment Journal

Editorial policy

Scope and acceptance criteria

Last updated: 19 October 2026

Scope

The Alignment Journal aims to clarify and address the challenges in designing a superintelligent AI that is aligned with human values. To serve that purpose, the Journal publishes work on the scientific foundations of understanding, predicting, and steering artificial intelligent systems.

We seek contributions that build theoretical or otherwise principled analyses of agency, robustness, incentives, generalization, interpretability, and long-term behavior across many learning and decision-making paradigms. We are especially interested in approaches that transcend contemporary architectures and potentially apply to future superintelligent systems that interact and self-improve.

A submission should make a claim the significance of which survives the deletion of any particular model, dataset, or benchmark, e.g., a theorem, an impossibility result, a formal framework, a protocol with an analyzed guarantee, or a conceptual argument precise enough to be wrong. Empirical work is welcome as validation or interrogation of such a claim.

Negative results are in scope and we would like more of them. This includes no-go and impossibility theorems, counterexamples to claimed guarantees, refutations of published results, and formalization attempts that failed for an articulable reason.

Alignment is an inherently interdisciplinary topic. To be accepted, work must substantively advance conceptual scaffolding or explanatory understanding of AI. Mere relevance or token connection are insufficient.

The topics we focus on are naturally influenced by the expertise of our editorial board, which will grow over time. We especially welcome work bridging alignment and these topics:

  • Causal inference
  • Computational mechanics and dynamical systems
  • Decision theory, game theory, and agent foundations
  • Information theory
  • Mechanism design and social choice theory
  • Moral philosophy, including metaethics
  • Multi-agent systems
  • Probability, statistics, and uncertainty quantification
  • Learning theory
  • Statistical physics and stochastic dynamics

as well as rigorous empirical studies of misalignment in frontier AI systems. This list is suggestive rather than exhaustive, and we are open to other topics if they fit the aim of the journal.

We currently are unlikely to review papers on the following topics, but may consider them in the future:

  • human-AI interaction
  • cognitive science and mathematical psychology
  • societal impacts, ethics, and governance of AI
  • security engineering and model-weight operational practice

Acceptance criteria

The Alignment Journal accepts articles for publication based primarily on three criteria:

  • Correctness: The claims in the article must be worthy of belief.
    • Mathematical computations must be accurate, proofs must be true, methodologies must be rigorous, and arguments must be sound.
    • Caveats and countervailing considerations must be presented in the manuscript with prominence appropriate to their seriousness (e.g., in the abstract).
    • Confidence in claims resting on empirical results is undermined insofar as those results are difficult to reproduce.
  • Insight: The article must advance our principled understanding of intelligent systems.
    • Improved benchmark performance per se does not generally imply insight, but the statistical behavior of models can of course provide important information about their internals.
    • Empirical evidence of improved performance on non-alignment capabilities is insufficient.
    • Scientific novelty in the traditional strict sense is not necessarily required: insightful translational and synthesis work will be accepted so long as it satisfies all criteria.
  • Value: The work should be the most valuable thing to read now for at least some alignment researchers.
    • It is not sufficient that a paper could be useful to someone at some time; the arXiv and journals/conferences with correctness-only criteria are more appropriate venues.
    • A result's value to other researchers is greatly diminished to the extent it is difficult to independently reproduce and build upon from the methodology and materials provided.

Desk rejects

Work of any subject that fails the criterion above is declined without review. Common cases:

  • interpretability results reporting what was found in a model, without a formal claim about what such findings can establish
  • capability or propensity evaluations without an accompanying theoretical or conceptual advance
  • benchmark and dataset papers
  • unprincipled training techniques evaluated primarily by performance
  • attack and defense results that do not engage with theory
  • agenda and position essays lacking a strong technical or conceptual contribution
  • surveys without deep synthesis

Other publication factors

Preprint requirement

Each submission must be available (in non-anonymized form) on one of these standard preprint repositories: arXiv, SSRN, ECCC, PhilPapers, and PsyArXiv. Exceptions are made for paper types which are not permitted in the otherwise appropriate repository; in these cases, please make sure to include an explanation in the “notes for the editor” box while submitting. Email the editors with any questions.

Archival status and prior publication

The journal is archival. The published articles constitute a permanent, citable version of record — the definitive, unchanging form of the work that is preserved indefinitely and treated as the canonical reference. The authors cannot publish the same work in another journal or conference. (Preprints on the arXiv or other preprint servers are encouraged of course.) That said, we adopt JMLR’s policy toward significant expansions of previously published work.

Specifically, we will consider submissions that have been published in a more limited form at workshops or conferences. In these cases, we expect the expansion to cite the prior work, go into much greater depth, and to extend the published results in a substantive way. In all cases, authors must (a) notify the editors about previous publication, including a link, at the time of submission and (b) explain the differences from their prior work.

Examples of (possibly) acceptable 'deltas' beyond a conference paper include: new theoretical results, entirely new application domains, significant new insights and/or analyses. Examples of insufficient deltas include: adding proofs that were omitted from a conference paper; minor variations or extensions of previous experiments; adding extra background material or references. However, we ultimately leave the decision about whether a 'delta' is significant enough up to the individual reviewers.

Reproducibility

We strongly encourage authors of empirical and computational manuscripts to ensure their work is reproducible. In particular, we recommend they (1) include sufficient methodological detail, ideally a dedicated reproducibility section, and (2) upload data, code, and similar material to robust repositories. As described in the acceptance criteria, reviewers and editors should factor in the ease of reproducibility in their decisions. Where code, data, or other materials needed to reproduce or build on the work have not been shared, the review summary should alert the reader to it.

Potential for harm

The journal only reviews work for which a public preprint is already available. Senior editors and action editors may, as a matter of conscience, decline to handle any submission they judge to be a significant risk of causing harm, such as advancing dual-use AI capabilities. A senior editor may also petition the senior editorial board to reject a submission on those grounds. However, it is unavoidable that some beneficial alignment research will be dual-use to some degree, and no journal can reliably determine the net effect of publishing an individual paper, so our main safeguard is the requirement that accepted work substantively advances our principled understanding of intelligent systems.