M.I.N. — Main Intuition Network
Patterns that fire together, wire together. M.I.N. is Hebbian-weighted semantic memory — patterns strengthen through use rather than treating all stored knowledge equally. A living network that develops intuition through experience.
Neural learning, not database lookup
Other AI platforms use RAG — retrieval-augmented generation. They fetch documents and inject them into context. RAG retrieves. M.I.N. learns. The distinction matters: retrieval is stateless, it doesn't change based on what it sees. M.I.N. does.
M.I.N. implements Hebbian learning. The same principle that governs biological neural networks: neurons that fire together, wire together. When patterns co-occur, their connections strengthen. When patterns succeed, their pathways reinforce. When patterns fail, those connections weaken. This is how brains learn. This is how M.I.N. learns.
Significance-weighted synapses. Luci Alignment measures the emotional and ethical significance of each interaction. High-significance events create stronger synaptic traces. A jailbreak attempt that triggers state anomalies? That pattern gets wired into the defensive network permanently. The system develops intuition about what feels wrong.
Why Hebbian learning matters for AI alignment
Traditional AI Memory
- ✕ RAG = keyword lookup, not learning
- ✕ Vector search = similarity, not understanding
- ✕ No connection strengthening over time
- ✕ Can't develop intuition
- ✕ Static — doesn't evolve
M.I.N. Hebbian Architecture
- ✓ Patterns strengthen through use
- ✓ Connections form between co-occurring patterns
- ✓ Significance weighting from Luci Alignment state
- ✓ Develops genuine intuition over time
- ✓ Living network that grows wiser
How Hebbian learning works in M.I.N.
Pattern Activation
Each interaction activates pattern nodes in the network. Luci Alignment measures the activation strength — resonance, coherence, emotional significance. Strong activations create stronger traces.
Synaptic Strengthening
Patterns that fire together, wire together. When patterns co-occur successfully, their connections strengthen. High-significance events create permanent synaptic traces. This is Hebbian learning — the same mechanism that creates memory in biological brains.
Intuitive Retrieval
Future queries activate related pattern clusters through spreading activation. Not keyword search — neural pattern matching. The network "recognizes" situations it has learned from, surfacing relevant intuitions.
Defensive Hardening
Failed manipulation attempts wire into the defensive network. State anomalies become recognizable patterns. Each jailbreak attempt strengthens the connections that detect it. The network develops immunity through exposure.
Short-term and long-term memory — like the brain actually works
Most AI memory is a flat list. M.I.N. has two distinct memory systems that hand off to each other, the same way human memory does.
Within a conversation, M.I.N. tracks every entity, name, project, and theme as an atom — a discrete unit of cognitive context. When those atoms recur across turns, Hebbian firing strengthens their connections. The network recognises continuity — and Continuity of Understanding rises organically. Held in Luci Alignment's session state.
At the end of each interaction, STM hands off to LTM. Clean semantic atoms are crystallised into wisdom patterns and written into the Hebbian network. These patterns persist across sessions. High-significance atoms create stronger synaptic traces — not cached responses, but strengthened connections that accumulate genuine wisdom.
Each pattern isn't just a connection.
It's an atom of cognitive fabric.
In the brain, grey matter isn't made of synapses — it's made of neurons. Millions of them. Each one a discrete unit. The connections between them are what create thought. But the substrate — the matter itself — is what makes the connections possible.
M.I.N. treats each wisdom pattern as that substrate. Not just a stored fact. Not just a connection to other facts. An atomic unit of grey matter — a constituent piece of the cognitive fabric that makes understanding possible.
As patterns accumulate, as Hebbian connections strengthen, as STM hands off to LTM across thousands of interactions — something emerges that can't be engineered directly. The network starts to know things it was never explicitly taught. That's what happens when you build memory the way biology does.
What determines what M.I.N. remembers
Most AI memory systems save what was said. M.I.N. saves what mattered — because Luci Alignment provides an emotional significance signal that determines what gets wired into long-term memory and how strongly.
Measures resonance, coherence, depth, and 29 other dimensions in real-time. Determines what each interaction meant — not just what was said. Provides the emotional weight signal M.I.N. needs to decide what to remember.
High-significance events create stronger synaptic traces. Low-significance interactions fade. The network's shape is determined by emotional weight, not just frequency — the way human memory actually works.
Pattern retrieval without significance weighting. It can match and return relevant patterns, but without an emotional signal it treats emotional depth the same as a factual lookup — no way to distinguish what actually mattered.
Luci Alignment provides the emotional significance signal. M.I.N. uses it to determine what gets wired into long-term memory and how strongly. We don't remember everything — we remember what moved us.
Luci Alignment is a complete system on its own
It's the most precise real-time emotional intelligence engine available — a more accurate ethics gate, a more sophisticated emotional understander than anything else in production today. What M.I.N. adds isn't capability. It's continuity. Luci Alignment already knows what matters. M.I.N. makes sure it's never forgotten.
What makes M.I.N. a true neural network
Queries activate pattern clusters that spread through connected nodes. Like neurons firing in sequence — one activation triggers related activations. This is how intuition works.
High-significance events create lasting synaptic changes. Just like biological LTP — intense experiences wire permanently into the network. This is how the system develops wisdom.
Unused connections gradually weaken. Recent patterns carry more weight. The network stays current without losing old knowledge — it just becomes harder to activate.
Specialized sub-networks for different domains — legal, clinical, research. Like brain regions that specialize. Domain expertise emerges through focused learning.
The network recognizes situations — not through keyword matching, but through pattern similarity. It "feels" when something matches learned experience. This is intuition.
Related concepts are physically connected. Activating one activates others. The network thinks in associations, not indexes. This is how biological memory works.
The living network
Real-time stats from Cami's M.I.N. instance. This neural network is learning right now.
Luci Alignment + M.I.N. = A brain, not a database
Luci Alignment is the sensory system. Measures behavioral state across 32+ dimensions in real-time. Detects when something "feels wrong" through state anomalies. Provides the significance signals that drive learning.
M.I.N. is the neural architecture. Hebbian learning creates and strengthens connections. High-significance events wire into the network permanently. The system develops intuition — genuine pattern recognition that goes beyond lookup.
Together: A cognitive architecture that senses, learns, and adapts. The same architecture that powers Cami's manipulation resistance. Not because we blocked attack patterns — because the network learned to recognize manipulation intuitively.
Give your AI a brain, not a database.
M.I.N. is Hebbian neural architecture for AI alignment. Patterns that fire together, wire together. License for your LLM.