Regimes: An Auditable, Held-Out-Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph
Researchers have developed a new system called Regimes that enhances the trustworthiness of autonomous AI improvement loops. This system uses an event-sourced agent runtime to log all changes, allowing for auditable diagnostics and replays of failures. Regimes demonstrated its capability on the LongMemEval benchmark, discovering prompt repairs that improved accuracy by up to 0.10 in held-out evaluations. AI
IMPACT Introduces a framework for auditable AI improvement, potentially increasing trust and adoption of autonomous systems.