Self-improving agent's apparent gains were noise; author publishes null result

The author built ALMA, a governance layer for self-improving agents that proposes changes to itself under a frozen validator and pre-registered experiments. Re-measuring with larger selection panels and a recalibrated threshold showed the apparent improvements were selected noise, reversing 7 of 7 times on held-out data. Re-testing all 58 in-scope configurations found none beat the base, so the meta-loop adopted zero changes and the search was closed. The author chose to publish this as a certified null result rather than keep tuning, and plans future reviewer runs on ReviewBench with a manifest for each run to prevent cherry-picked scores.