engram_cosine_sim() was defined and embeddings were computed per-node
via nomic-embed-text on write, but the function was never called during
activation scoring. The goal_bias computation used only lexical substring
matching, ignoring all stored embedding vectors.
This change adds engram_embed_query() to embed the query string at search
time (5s timeout so Ollama latency never blocks activation), then blends
cosine similarity into the working-memory bias with α=0.3:
bias_final = goal_bias(lexical) * (1 + 0.3 * max(0, cosine_sim))
Nodes with high semantic similarity to the query but low lexical overlap
now receive up to 30% bias boost into working memory promotion. Gracefully
degrades to pure lexical when Ollama is unavailable or node has no embedding.
- Add ML-KEM-1024 + AES-256-GCM binary persistence to el_runtime.c with
two-key scheme (Neuron master + user key); SHAKE-256 key derivation
- Add nomic-embed-text 768-dim float32 embeddings on every node write
via Ollama; graceful fallback when Ollama is not running
- Wire all /api/neuron/* MCP routes directly into Engram (server.el),
eliminating the Kotlin server as the MCP backend
- Set ENGRAM_CHECKPOINT_INTERVAL = 1 (write binary on every node write,
not every 50)
- Add el_runtime.h declarations for engram_write_binary_el and
engram_load_binary_el builtins
will.anderson
Will Anderson