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OMEM is a memory layer for AI agents that tracks beliefs over time, surfaces contradictions, and keeps provenance, aiming to replace static vector-store memory. The author is seeking early testers for this not-yet-production-ready tool.
This paper introduces Contextual Belief Management (CBM) for LLMs to handle long-term information, proposes the BeliefTrack benchmark for evaluation, and demonstrates that reinforcement learning and representation-level steering significantly reduce belief management failures.