[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-researchers-propose-local-ai-pre-screening-for-peer-review":10,"sections":40},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":30,"tags":31,"sources":35,"feedback":39,"feedback_at":22,"cost_usd":39,"total_tokens":39},5290,"researchers-propose-local-ai-pre-screening-for-peer-review","Researchers propose local AI pre-screening for peer review","A new proposal would have local, open-weight AI models pre-screen manuscripts for a health sciences journal, with humans still making every final call.","A group of researchers has outlined a five-stage framework that would run submissions through locally-hosted AI models before any human reviewer sees them, though it exists only as a proposal for now.\n\nThe design, developed for a health sciences journal, routes a manuscript through sanitization and anonymization, parallel AI pre-screening by three separate open-weight models, an automated check gate, blinded human review, and finally editorial adjudication, with authors able to appeal at two of those stages. Because the AI reviewers run on infrastructure the journal controls rather than third-party services, the authors say the setup sidesteps the confidentiality concerns behind NIH and NSF bans on feeding unpublished proposals to outside generative AI tools. Human reviewers keep final say throughout. The paper frames this as a transparent alternative to what's already happening quietly across the field.\n\nThat quiet reality is stark: an independent analysis found roughly 21 percent of ICLR 2026's 75,800 peer reviews were fully AI-generated, up from 15.8 percent involvement in 2024, alongside documented cases of hallucinated citations and prompt-injection text hidden in manuscripts to game AI reviewers. With NeurIPS 2025 pulling in over 21,000 submissions and reviewer supply not keeping pace, the pressure pushing labs toward undisclosed AI use isn't going away on its own.\n\nWorth noting: the same paper cites prior work benchmarking open-source LLMs on manuscript classification at just 35 percent exact-match accuracy, which is exactly why the authors kept humans in charge of every actual decision. A sensible guardrail for a system that, so far, is still a proposal rather than something running in production.","[\"peer-review\",\"ai\",\"academic-publishing\",\"open-weight-models\"]","2026-08-18T04:00:00.000Z","2026-08-18T13:35:07.021Z","2026-08-18T13:35:18.815Z","published",null,[24],{"id":25,"reviewer":26,"round":27,"reason":28,"status":29},"editor-r1","editor",1,"The source is an arXiv preprint describing a 'proposed' triple-blind AI pre-screening framework 'developed for a health sciences journal,' but the draft's headline, dek, and lede assert it as already live and operational ('now routes submissions,' 'has started running submissions') — soften the framing to match the source's proposal-stage language unless deployment can be separately confirmed.","resolved","ai",[32,30,33,34],"peer-review","academic-publishing","open-weight-models",[36],{"name":37,"url":38},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.14625",0,{"sections":41},[42,46,50,55,60,65,70,75,80,84,89,94,99,104],{"name":43,"slug":30,"count":44,"latest_published_at":45},"AI",3293,"2026-08-20T04:00:00.000Z",{"name":47,"slug":48,"count":49,"latest_published_at":45},"Security","security",435,{"name":51,"slug":52,"count":53,"latest_published_at":54},"Policy","policy",210,"2026-08-19T09:32:27.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":64},"Hardware","hardware",140,"2026-08-19T18:25:42.000Z",{"name":66,"slug":67,"count":68,"latest_published_at":69},"Consumer Tech","consumer-tech",95,"2026-08-18T16:05:00.000Z",{"name":71,"slug":72,"count":73,"latest_published_at":74},"Science","science",90,"2026-08-19T18:41:02.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":79},"Software","software",73,"2026-08-18T07:51:50.000Z",{"name":81,"slug":82,"count":83,"latest_published_at":18},"Dev Tools","dev-tools",69,{"name":85,"slug":86,"count":87,"latest_published_at":88},"Startups","startups",47,"2026-08-19T19:13:46.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"General","general",33,"2026-08-18T22:18:13.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":105,"slug":106,"count":107,"latest_published_at":108},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]