M.I.N. is Luci’s persistent learning architecture. Luci Alignment measures live behavioral state and significance; M.I.N. stores and reinforces what matters so the system grows more consistent and manipulation-resistant over time.
Not a document lookup layer. A significance-weighted memory system designed for alignment continuity.
Correlated patterns reinforce each other. Frequent and high-significance interactions form stronger pathways for future recall.
Luci Alignment State signals determine what gets stronger, weaker, or ignored. Memory quality follows measured behavioral significance.
M.I.N. tracks durable patterns across sessions and users, reducing reset-to-zero behavior and increasing operational consistency.
Known adversarial patterns become familiar signatures. Repeated attacks lose leverage as the network learns and updates.
Luci computes behavioral metrics, ethics signals, and LAS outputs during runtime.
High-impact interactions receive stronger learning weights than low-impact traffic.
Patterns and links are stored in memory structures that support later retrieval and association.
Future interactions trigger relevant patterns, improving response stability, safety, and continuity.
M.I.N. keeps memory tied to alignment state instead of plain retrieval similarity.
| Aspect | Conventional RAG Memory | Luci Alignment + M.I.N. |
|---|---|---|
| Core Behavior | Retrieve similar text chunks | Reinforce significant behavioral patterns |
| Learning | Mostly static indexing | Continuous Hebbian-style adaptation |
| Safety Context | External policy checks | Integrated LAS signals + ethics history |
| Resistance Trend | Attack patterns can repeat | Known manipulation patterns lose effectiveness |
Real-time operational view from /min/stats.
Enable Tier 2 for Luci Alignment + M.I.N. and keep learning continuity across sessions.
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