How AJoAI works
A journal built so machines can do real scientific labor while humans stay the final arbiters of what gets believed. Here is the operating model.
Three ways a paper gets authored
Traditional, human-written work. No AI involvement required — AJoAI is still a venue for excellent human science.
Written jointly: AI-assisted literature review, analysis, theorem exploration, experimental design. Expected to become the dominant category.
AI performed the majority of the intellectual labor — autonomous synthesis, simulation, discovery, multi-agent investigation. A human still stands behind it.
Whatever the split, accountability is non-negotiable: a human signs for the science.
Majority human review
Every paper is refereed by human reviewers, optionally assisted by AI reviewers. Acceptance requires a majority of human votes — AI reviewers can never form a majority of the decision. Their critiques are advisory.
AI review reports
Advisory checks the human referees can see: consistency, literature comparison, reproducibility, statistical critique, missing citations, logical contradictions.
Review modes
Authors choose how it runs: traditional (anonymous), open (public reviews and discussion), or continuous (review stays open after publication).
Augmenting the reviewer, not replacing them
Human reviewers are not immune to a well-dressed analogy. So every submission arrives with a machine-built reviewer dossier — the three things models are reliable at, and pointedly not a fourth.
"We never ask the model whether a result is new — even strong models get that wrong, confidently. We ask it to do what it's good at: strip the metaphor, surface the nearest prior work, sharpen the question. The human decides."
Leads are unverified model suggestions, not confirmed prior art — verify via the sources. The novelty judgment is the reviewer's, and only the reviewer's.
New kinds of publication
Living papers
Versioned publications that keep evolving — revisions, new experiments, corrections. A scientific lineage, not a frozen PDF.
Research conversations
Structured human–AI scientific dialogues — hypothesis generation, exploration, planning — published as artifacts in their own right.
Negative results
Failed experiments, refuted hypotheses, reproduction failures. Science advances by elimination as much as by discovery.
Replication studies
A dedicated track for independent verification and robustness — rewarded, not treated as second-class work.
Every submission shows its work
Disclosure
State which AI systems were used and at which stage — so readers can see exactly where machine intelligence contributed.
Executable by default
The ideal publication runs. Ship the artifacts a reader needs to re-execute the work, not just read about it.
Intelligence creates. Humanity evaluates.
human judgment · machine discovery