Regimes: An Auditable, Held-Out-Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph
Quick Answer
Regimes introduces an auditable improvement loop on ActiveGraph, enhancing LongMemEval-S accuracy by up to +0.10 through systematic failure diagnosis and repair.
Quick Take
This approach leverages event-sourced agent runtime to ensure transparency in the improvement process, making it applicable across various tasks.
Key Points
- Regimes operates on ActiveGraph, enabling controlled improvement loops.
- Achieved accuracy improvements of +0.05 to +0.10 on LongMemEval-S.
- Failures are recorded and replayed, ensuring transparency in the process.
- The loop is target-agnostic, functioning across different tasks.
- Introduces a failure-regime taxonomy for effective routing of issues.
Paper Resources
Source Excerpt
arXiv:2606. 10241v1 Announce Type: new Abstract: Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unlogged, diagnoses cannot be replayed, and promote-or-discard decisions land in a side database rather than the agent's own history. We show that an event-sourced agent runtime removes that friction and turns controlled improvement into a first-class workflow. …
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