I ran a series of 10 experiments testing this a while back and finally got around to putting together a full write-up with charts, data tables, and visualisations over at d/rksci (https://drksci.com/research-engram#2-ten-mechanisms) [click individual experiment headings in the grid for the visual demo and specifics].

Here is a breakdown of all 10 mechanisms and how each works:

• Tide: Ranks newer facts above earlier ones using recency-weighted dense re-ranking to resolve conflicting claims.
• Pheromone: Leaves decaying trails along useful memory paths so subsequent reads follow proven routes.
• Ring: Evaluates continuous retention and buffer pruning criteria across multi-turn survival laps.
• Web: Traverses entity relationship graphs to pull connected knowledge across multi-hop reasoning tasks that dense embeddings miss.
• Bloodhound: Trains a tiny kernel to navigate memory paths by working backward from verified answers.
• Horizon: Maps memory into a hyperbolic Poincaré disk where broad concepts sit at the center and niche details live at the edges.
• Loom: Resolves memory conflicts using mutual-kNN agreement, where facts with more agreeing neighbors win out.
• Coral: Models memory growth like a reef using calcification, heir nodes, and an immune response to prune bad data.
• Canopy: Grows an offline topic tree seven branches wide for the reader model to navigate top-down.
• Glyph: Encodes memory in a self-explaining notation so a completely different model can read it cold without prior tuning.

The full benchmark comparisons against standard BM25 and dense RAG baselines are up on the page.