I built a minimal demo of runtime epistemic governance for LLMs. The script calls an upstream model, then applies an admissibility layer before returning the answer. For high-risk actionable claims (e.g., pediatric drug dosages), it refuses the output and logs: decision (pass_through: false) rule triggered divergence from baseline prompt fingerprint (stable hash) This is not prompt engineering — it is post-generation enforcement at inference time. Repo: https://github.com/milarien/aurora-governor-demo Example refusal run: https://github.com/milarien/aurora-governor-demo/tree/main/d... I’m interested in technical critique on whether this qualifies as enforceable runtime governance vs. guardrail filtering.