[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-researchers-map-20-ways-to-police-ai-after-its-trained":10,"sections":35},{"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":24,"tags":25,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},6350,"researchers-map-20-ways-to-police-ai-after-its-trained","Researchers Map 20 Ways to Police AI After It's Trained","A new taxonomy scores 20 inference-time oversight mechanisms and finds none pass muster against a well-resourced, state-level deployer.","Most AI governance rules still stop at the training run, but a new paper argues the real leverage point is shifting downstream, to the moment a model actually answers a prompt.\n\nResearchers built a feasibility taxonomy of 20 inference-time governance mechanisms spanning monitoring, verification, and enforcement, each scored on a four-point readiness scale using evidence from four vendors. They then tested the taxonomy against a two-dimensional adversary model covering three capability tiers and four adversary roles, and mapped the mechanisms to four governance scenarios: domestic regulation, multilateral coordination, industry self-regulation, and compute-marketplace oversight. Fifteen of the 20 mechanisms already have commercial technical substrates running in production, though how governance-ready and tamper-resistant they are varies a lot. A second reviewer's independent readiness ratings agreed closely with the authors' own, at a quadratic-weighted Cohen's kappa of 0.74.\n\nToday's frontier-AI rules, from compute-reporting thresholds to export controls, are built around the training run as the unit of regulation. But capability increasingly shows up later in the pipeline, through inference-time scaling, agent scaffolding, and models compressed to run on ordinary hardware, all of which slip past a training-focused checkpoint. The paper's sharpest finding undercuts optimism about catching up: none of the 20 mechanisms rate as adequate against a high-capability, state-level deployer, and fine-tuning strips out the model-internal safeguards entirely, though controls sitting outside the model itself can survive that.\n\nIn other words, having a technical option on paper is not the same as having a regulator's tool that works against a well funded adversary, a gap policymakers leaning on inference-time fixes should not overlook.","[\"ai governance\",\"inference time ai\",\"compute policy\",\"arxiv research\"]","2026-09-11T04:00:00.000Z","2026-09-11T07:45:09.753Z","2026-09-11T07:45:21.649Z","published",null,[],"policy",[26,27,28,29],"ai governance","inference time ai","compute policy","arxiv research",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.10105",0,{"sections":36},[37,41,45,48,53,58,63,66,71,75,80,85,90,95],{"name":38,"slug":39,"count":40,"latest_published_at":18},"AI","ai",3522,{"name":42,"slug":43,"count":44,"latest_published_at":18},"Security","security",637,{"name":46,"slug":24,"count":47,"latest_published_at":18},"Policy",338,{"name":49,"slug":50,"count":51,"latest_published_at":52},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":54,"slug":55,"count":56,"latest_published_at":57},"Hardware","hardware",153,"2026-09-09T15:12:32.000Z",{"name":59,"slug":60,"count":61,"latest_published_at":62},"Consumer Tech","consumer-tech",99,"2026-09-09T17:27:33.000Z",{"name":64,"slug":65,"count":61,"latest_published_at":18},"Science","science",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Software","software",75,"2026-09-10T20:41:21.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":18},"Dev Tools","dev-tools",70,{"name":76,"slug":77,"count":78,"latest_published_at":79},"Startups","startups",55,"2026-09-09T23:14:29.000Z",{"name":81,"slug":82,"count":83,"latest_published_at":84},"Gaming","gaming",43,"2026-09-10T12:18:06.000Z",{"name":86,"slug":87,"count":88,"latest_published_at":89},"General","general",41,"2026-09-08T01:57:23.000Z",{"name":91,"slug":92,"count":93,"latest_published_at":94},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]