No LLM in the loop. The consolidation pass is deterministic:

Pull the N most recent active memories (default 30) with embeddings Pairwise cosine similarity, threshold 0.85 For each similar pair, check if they share extracted entities Shared entities + similarity 0.85-0.98 → flag as potential contradiction (same topic, maybe different facts) No shared entities + similarity > 0.85 → redundancy (mark for consolidation) Second pass at 0.65 threshold specifically for substitution-category pairs (e.g., "MySQL" vs "PostgreSQL" in otherwise-similar sentences) — these are usually real contradictions even at lower similarity Consolidation then collapses the redundancy set into canonical memories with combined importance/certainty. No LLM call, no randomness. Reproducible, cheap, runs in a background tick every ~5 minutes.

The LLM could improve this (better merge decisions, better entity alignment) but the tradeoff is cost and non-determinism. v1 is deterministic on purpose.

Source: crates/yantrikdb-core/src/cognition/triggers.rs and consolidate.rs next to it.

> with embeddings Pairwise cosine similarity, threshold 0.85

So, your system is unable to differential between AWS and Azure (~95 similarity). Probably unable to consistently differentiate between someone saying they love and hate something.