Research Integrity

A 288-researcher randomized trial published in PNAS gave 103 teams three levels of AI autonomy in verifying published social science. AI-led teams reproduced only 37% of results, but the quieter finding sits in the middle arm: AI-assisted teams matched human-only teams on reproduction while finding fewer than half as many major errors, a verification loss with no visible signal in the finished work.

In April 2026 Nature published a multi-analyst study on an unusual scale: 457 independent analysts producing 504 reanalyses of 100 social and behavioural science studies, each given the original data and the original question and left to choose their own method. The figure that travelled was 34%, the share of claims where every reanalyst agreed with the original. The same paper reports 39% at a slightly looser bar and 80% at a simple majority, and plots the whole curve. That is the finding worth carrying. Robustness is not a number the data hands you, and a study about how a single analytic choice can carry a conclusion turns out to contain exactly that choice, disclosed openly by its authors. We read every figure off the paper itself, including what it does not establish and why preregistration will not fix it.

A Lancet-published audit of 2.5 million biomedical papers found fabricated references rising twelvefold in two years. Named methodologists immediately flagged real limits in how the finding was produced and what it can and cannot support.

In a single reviewing season, overlapping communities of AI researchers punished reviewers for using language models, permitted other reviewers to use them, and commissioned a machine to review every submission. Each venue was internally coherent, and the ICML chairs were admirably candid that their enforcement said nothing about whether the removed reviews were any good. That candour points at the real gap: peer review has become precise at identifying who produced a review and remains unable to say whether the review was worth having. A 2008 randomised trial at the BMJ, in which reviewers found an average of 2.58 of nine deliberately planted major errors, suggests the second question was going unanswered long before any machine was involved.

Two unrelated stories from 2026, a citation-fabrication crisis and a fabricated stroke-detection dataset, describe the same failure at different layers of the research stack.

404 Media reported that Research Gold, which sells systematic reviews and meta-analyses to medical researchers on an explicit promise of no AI involvement, staffed its About page with people who do not exist and lifted the identities of real methodologists from LinkedIn without permission. It quoted $1,900 for a review of a deliberately nonsensical question. The disclosure claim was the product being sold.

An updated preprint by Anna Abalkina and colleagues matched 2,062 papers in 366 IEEE conference proceedings to publication offers advertised on social media, and Science reports that 66 have been retracted so far. The count rose from 1,720 in the April version without any change in method, which tells you the figure is a floor rather than an estimate. The wider point for anyone screening literature is that conference proceedings sit outside every journal-level check a researcher would normally apply.

Anthropic has begun embedding an invisible watermark in text generated by Claude and attaching signed C2PA provenance metadata to the files it produces, in response to the EU AI Act's transparency code. The company's own documentation is candid that a detected mark only shows content was processed by Claude, not written by it, and that an absent mark proves nothing. The tools that would let anyone outside Anthropic check for the mark have not been published yet.

The American Journal of Political Science published a revised AI policy for authors and reviewers on 11 August 2026. Alongside the familiar requirements, no AI authorship and disclosure of any use that shaped the research, it asks for something most journal policies do not: the degree of human oversight applied to AI output, meaning whether it was reviewed, edited, or approved before it entered the manuscript. Non disclosure is stated grounds for decline. The revision lands while COPE and four partner organisations work toward a global AI disclosure standard due at the end of 2026.

Elsevier's International Journal of Biological Macromolecules has retracted 120 papers since June for systematic peer review manipulation in guest-edited special issues, nearly all published in 2024 and 2025 while the journal's annual output doubled. The count is the least useful figure in the story. One researcher reported 352 suspect papers to the journal and 12 have been retracted, and the papers already sit in reference lists a notice cannot reach.

JAMA Internal Medicine has declined for the third time to retract a 2000 study concluding that controlled-release oxycodone rarely caused withdrawal in osteoarthritis patients, after clinicians pointed to 2007 court records showing the sponsor, Purdue Frederick, withheld contradicting withdrawal data. The journal gave two reasons: no evidence of fabrication or falsification, and no agreement from all surviving coauthors. Both are narrower than what COPE and the ICMJE actually ask of editors, and the gap is the story.

A widely used Kaggle dataset marketed for stroke-detection research contained celebrity photos and images lifted from a dental journal. Kaggle acted only after a copyright complaint, not the original research-integrity report.

AJPS published a new AI policy on 11 August requiring authors to name every AI tool used in a manuscript, and barring reviewers from uploading manuscripts to any AI tool at all.

Clarivate confirms Heliyon will regain its Web of Science impact factor in October, two years after a quality hold and a mass self-correction of 1,197 items.