Great work on Engram. The consolidation and contradiction detection features are smart additions - episodic to semantic is exactly how human memory works.

We've been working on a similar problem with Remembra and hit 100% on LoCoMo with a different approach: entity graphs + temporal decay. The insight was that knowing WHO you're talking about (resolving 'Mr. Kim' to 'David Kim') matters as much as what was said.

Curious about your spreading activation implementation - are you doing this at query time or pre-computing the graph connections?

If you want to compare notes: https://github.com/remembra-ai/remembra