⚠️ AXON is not a chatbot. It is a continuously learning cognitive system that builds memory, personality, and internal world models over time.

🧠 AXON

Emerging Artificial Intelligence

A biologically-inspired AI that sees, hears, recognises faces, reads your voice, learns, remembers, competes, forms beliefs, builds opinions, reflects on itself, narrates competing worldviews, manages four tiers of memory, uses the LLM as an imagination engine rather than an answer machine, evaluates competing thoughts before speaking, closes a real learning loop — and speaks through any LLM you choose, local or cloud.

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⭐ Why people are watching this

• First open system with persistent identity across sessions
• LLM used as imagination layer, not decision engine
• Real-time internal competition between thoughts
• Multi-tier memory that changes behavior over time
• Personality that drifts based on experience

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What is AXON?

AXON is not a chatbot wrapper. It is a persistent, biologically-inspired intelligence with 2.342 billion virtual neurons across 12 functional brain regions, running fully on a local GPU.

Unlike stateless assistants, AXON accumulates experience over time — every conversation, recognised face, learned fact, Hebbian weight change, formed belief, and written reflection is stored in a local SQLite database and survives reboots. The model is always the same mind that was there the last time you talked to it.

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🔬 Sensory & Motor Layer

Capability	Implementation
👁️ Vision	YOLOv8 face detection + FER / DeepFace emotion recognition
🪪 Face Identity	dlib 128-d embeddings — recognises who it's talking to
🎤 Voice Input	OpenAI Whisper STT
🔊 Voice Output	edge-tts speech synthesis
😤 Audio Emotion	Real-time prosody analysis (pitch / energy / ZCR)
🌐 LLM Backend	LM Studio (local, private) · OpenAI · Claude · Gemini · Groq

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🧬 Internal Cognitive Depth

What separates AXON from other "neural" AI projects is genuine internal depth that goes beyond signal routing:

• Synchronized cognitive loop — every 100ms, all subsystems execute in explicit dependency order
• Cluster competition — lateral inhibition forces regions to fight for dominance
• Four internal drives — curiosity, social, competence, stability — each accumulates pressure when unmet and discharges when satisfied
• Weighted belief system — beliefs update from lived experience and actively challenge external knowledge
• Multi-dimensional value scoring — the same outcome is scored differently depending on personality state
• Structured self-model — I am / I believe / I like / I avoid / I want rebuilt continuously and injected into every decision
• Autonomous reflection — AXON reflects every ~15 seconds, forming conclusions from its own activation patterns
• Seven competing worldviews — fight for narrative dominance on every cognitive tick (e.narratives)
• Four-tier memory hierarchy — Episodic → Semantic → Value → Identity — governs what decays, what persists, what becomes self
• Wired personality traits — curiosity, risk, stability, persistence, neuroticism directly shape exploration rate, cluster resistance, and neuromodulator swings
• Weight-driven neural canvas — Hebbian learning, thought bubbles, and pruning events visualised in real time

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🧠 The LLM is Not the Brain — It's the Imagination Engine

The most fundamental shift: the LLM no longer generates the answer. It generates possibilities. AXON's neural systems pick the winner.

Before every response, the Thought Generator runs a full conditioning pipeline:

Goal conditioning  →  emotional state + personality + active drives + dominant worldview
Memory injection   →  relevant outcomes, episodes, beliefs, current bias
Candidate gen      →  LLM produces N distinct candidate responses
Candidate scoring  →  neural alignment + trait affinity + reward plausibility
Conflict Engine    →  winner resolved against live cluster activations
Learning loop      →  record_outcome() closes the cycle — weights, memory, strategy updated

After delivery, emotional feedback closes the loop: record_outcome() fires, winning clusters are rewarded, the strategy library is updated, memory salience is boosted, and a prediction_error event is emitted. The LLM call is now part of a continuous learning cycle, not a one-off event.

The full system prompt passed to the LLM includes neural state, emotional snapshot, drives, beliefs, and self-model — up to 12,000 characters of rich internal context before any conversation text is added.

Every round is visible in the live Competing Thoughts panel in the UI.

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🧠 1-minute mental model

AXON works like this:

1. Perceive input — vision / audio / text
2. Activate 12 brain regions across 64 clusters
3. Generate multiple possible responses via LLM
4. Simulate each response against the internal neural state
5. Compete — candidates scored across neural clusters
6. Select the best-aligned outcome via the Conflict Engine
7. Learn from the result — reward, penalty, prediction error
8. Update memory + personality + beliefs + strategy library

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Neural Architecture

                        +--------------------------------------------------+
  Webcam  ------------>  Visual Cortex (YOLOv8-face + FER/DeepFace emotions)|
                        | Face Identity (dlib 128-d embeddings, profiles)   |
  Microphone  -------->  Auditory Cortex (Whisper STT)                      |
                        | Audio Emotion  (prosody: pitch/energy/ZCR)        |
  Web Search  -------->  Association Cortex (curiosity / abstraction)       |
  Documents   -------->  Knowledge Ingestion → Concepts → Opinions → Stance |
                        |                                                   |
                        |   CENTRAL COGNITIVE CYCLE (10 Hz)                 |
                        |   ┌─────────────────────────────────────────┐    |
                        |   │ 1. gather_sensory_state                 │    |
                        |   │ 2. drive_system.tick() + fabric_hints   │    |
                        |   │ 3. belief.decay() → NE spike            │    |
                        |   │ 4. fabric.get_state() (top_clusters)    │    |
                        |   │ 5. path tracking → strategy library     │    |
                        |   │ 6. self_model.rebuild()                 │    |
                        |   │ 7. value_system.evaluate(reward)        │    |
                        |   │ 8. prediction_error → NE/epsilon/beliefs│    |
                        |   │ 9. reflection_engine.tick()             │    |
                        |   │10. narratives.tick()                    │    |
                        |   │11. memory_hierarchy.prune()             │    |
                        |   │12. thought_trace emit                   │    |
                        |   └─────────────────────────────────────────┘    |
                        |                                                   |
                        |   THALAMUS --- attention gate + sensory relay     |
                        |       |                      |                    |
                        |  PREFRONTAL            HIPPOCAMPUS                |
                        |  executive, working    encode/recall, pattern     |
                        |  memory, decisions     completion, forgetting     |
                        |       |                      |                    |
                        |  AMYGDALA              LANGUAGE SYSTEM            |
                        |  fear, reward          LLM <-> semantic memory    |
                        |       |                      |                    |
                        |  NEUROMODULATORS ---------------------------------|
                        |  dopamine, serotonin, norepinephrine              |
                        |  acetylcholine, GABA, glutamate                   |
                        |                                                   |
                        |  DEFAULT MODE, CEREBELLUM, SOCIAL BRAIN           |
                        |  METACOGNITION, ASSOCIATION CORTEX                |
                        |                                                   |
                        |  +------------------------------------------+   |
                        |  |  CONFLICT ENGINE  (lateral inhibition)   |   |
                        |  |  COGNITIVE STATE  (conf, unc, urgency)   |   |
                        |  |  INTERNAL CRITIC  (fast/slow eval)       |   |
                        |  |  META-CONTROLLER  (tunes the system)     |   |
                        |  |  STRATEGY LIBRARY (learned behaviors)    |   |
                        |  |  REINFORCEMENT RL (temporal credit)      |   |
                        |  |  BELIEF SYSTEM    (weighted assumptions) |   |
                        |  |  DRIVE SYSTEM     (motivational pressure)|   |
                        |  |  VALUE SYSTEM     (multi-dim scoring)    |   |
                        |  |  SELF-MODEL       (structured identity)  |   |
                        |  |  GOAL SYSTEM      (intrinsic motivation) |   |
                        |  |  SURPRISE ENGINE  (event detection)      |   |
                        |  |  PERSONALITY VECTOR (5 wired traits)     |   |
                        |  |  REFLECTION ENGINE (autonomous thought)  |   |
                        |  |  NARRATIVE THREADS (7 worldviews)        |   |
                        |  |  OPINION LAYER    (valence + novelty)    |   |
                        |  +------------------------------------------+   |
                        +--------------------------------------------------+

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Personality → Exploration Mapping

Trait	Effect on ε / behavior
Curiosity (high)	ε floor raised — always willing to explore
Risk (high)	ε ceiling raised — willing to push further
Stability (high)	ε volatility damped — resists sudden changes
Persistence (high)	Winning clusters hold dominance longer
Neuroticism (high)	NE swings amplified on prediction error

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Neural Canvas — Visual Systems

The real-time brain visualization has been significantly upgraded:

Weight-Driven Connection Lines

Axon routes are no longer uniform — line thickness and glow scale with live Hebbian weight. A frequently co-activated pathway becomes visibly thicker and brighter. Weak or unused connections are thin and dim. Pruned connections show as faded dashed lines before disappearing.

Cluster Force-Physics

Dominant clusters (dominance > 0.25) are pulled slightly away from their fixed centroid positions toward a spring-repulsion equilibrium — the canvas physically rearranges around whatever's most active.

Thought Bubbles

When a cluster's dominance score exceeds 0.65, a small floating italic label pops above it — a glimpse of what that region is "doing":

• Prefrontal: "Evaluating options." / "Inhibiting impulse."
• Hippocampus: "Pattern match found." / "Encoding experience."
• Amygdala: "Threat detected." / "High arousal."
• Default Mode: "Wandering." / "Internal narrative."
• … and more for all 12 regions

Emotional State Bar

A persistent bar at the top of the canvas shows:

• NE level (norepinephrine) — alertness, urgency
• Reward trend — recent dopamine delta direction
• Surprise — current prediction error magnitude
• Mood label — one of: curious, alert, bored, stressed, calm, entrapped, surprised

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LLM Provider Support

AXON supports multiple LLM backends, switchable at runtime:

Provider	Notes
LM Studio (default)	Local, fully private, no API key, OpenAI-compatible
OpenAI	GPT-4o, GPT-4-turbo, etc.
Anthropic	Claude 3 Opus/Sonnet/Haiku
Google Gemini	Gemini 1.5 Pro/Flash
Groq	Ultra-fast inference (Llama, Mistral)

Configuration is stored in providers.json. Switch via the LLM Provider tab in the UI without restarting.

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API Reference

All brain state is exposed over a REST API and a Socket.IO channel.

Brain State

Endpoint	Method	Description
/api/brain/state	GET	Full neural state snapshot
/api/brain/explain	GET	Natural language explanation of current internal state
/api/brain/personality	GET / POST	Read or override the personality trait vector
/api/brain/reflections	GET	Most recent autonomous reflections
/api/brain/narratives	GET	Narrative worldview dominance scores
/api/brain/thought_competition	GET	Last N thought competition rounds (candidates, scores, winner)
/api/brain/memory_hierarchy	GET	Per-tier memory counts and salience
/api/brain/memory_hierarchy/store	POST	Manually promote a memory to a higher tier
/api/brain/interests	GET	Tracked interests and curiosity weights
/api/brain/interests/add	POST	Add an interest
/api/brain/interests/remove	POST	Remove an interest
/api/brain/boredom	GET	Current boredom level and contributing factors
/api/brain/speed	GET / POST	Read or set the cognitive cycle speed
/api/surprise_events	GET	Recent surprise events log

Goals & Identity

Endpoint	Method	Description
/api/goals	GET	Current goal list
/api/goals/add	POST	Set a new goal
/api/goals/remove	POST	Remove a goal
/api/user_profile	GET	Current user model (passively built from conversations)
/api/memory_summary	GET	High-level episodic / semantic / value counts

Knowledge & Actions

Endpoint	Method	Description
/api/brain/ingest	POST	Ingest a document into the knowledge base
/api/first_opinion	POST	Force AXON to form an opinion on a given topic
/api/brain/autonomous	POST	Run N steps of autonomous cognition
/api/brain/save	POST	Save current brain state to disk
/api/brain/load	POST	Load a saved brain state
/api/brain/snapshots	GET	List all saved snapshots
/api/fork_brain	POST	Fork the current brain to a named copy
/api/list_forks	GET	List available forks
/api/share_brain	POST	Export a shareable brain package

System

Endpoint	Method	Description
/api/status	GET	Engine running status
/api/mics	GET	Available microphone devices
/api/cameras	GET	Available camera devices
/api/audio_diag	GET	Audio subsystem diagnostics
/api/onboarding_check	GET	Whether onboarding has been completed

Socket.IO Events (Server → Client)

Event	Payload
brain_state	Full state update (emitted every cognitive tick) — includes neural, memory, state, cognitive_state, conflict, meta, strategy_lib
neural_state	Lightweight neural snapshot (neuromod, emotion, neurons, connections)
thought	New thought from the background thought stream
reflection	New autonomous reflection formed
surprise_event	Surprise event fired (type, title, detail)
thought_competition	Full candidate competition log (candidates, scores, winner, reasoning)
prediction_error	Prediction error + delta from the learning loop closure
synapse_count	Updated Hebbian synapse count
hebbian_event	New Hebbian connection formed or pruned
region_spike	Individual cluster spike event
response	AXON's completed response text
thinking	Thinking state flag (true/false — drives UI spinner)
transcript	Speech-to-text transcript of voice input
face	Face detection result with emotion and identity
known_face	Recognised face with identity data
new_face	Unknown face detected — prompts identity request
frame	Raw vision frame data
audio_emotion	Audio prosody emotion state update
knowledge_ingested	Result of a document ingestion
reflection	Autonomous reflection formed
lm_status	LLM connection status
voice_speaking	TTS playback state
person_named	Face identity learned or updated
profile_update	User model updated
new_hobby	New interest detected from conversation

Socket.IO Events (Client → Server)

Event	Description
chat	Send a text message to AXON
user_text	Alternative text input channel
start_engine	Activate the cognitive engine
stop_engine	Stop the engine and autosave
set_personality	Push personality trait overrides
run_autonomous	Trigger N steps of autonomous thought
observe_mode	Toggle observe mode (autonomous) vs. train mode
get_explanation	Request a natural language self-explanation
reprobe_lm	Re-check LLM Studio connection
get_provider_status	Get current LLM provider status
update_provider	Switch LLM provider at runtime

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🚀 Why this matters

Current AI systems:

• forget everything between sessions
• do not form stable identity
• do not learn structurally over time

AXON introduces:

• Persistent cognitive state — same mind every session, no resets
• Adaptive personality — trait vector drifts from accumulated experience
• Structured memory hierarchy — episodic, semantic, value, identity tiers
• Internal competition-based reasoning — N thoughts generated, one wins
• Continuous learning loop — every response closes a prediction-error cycle

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Installation

Requirements

• Python 3.12
• NVIDIA GPU (RTX 3080+, 8GB+ VRAM recommended; RTX 5090 optimal)
• CUDA 12.8 + cuDNN
• LM Studio (for local LLM; optional if using cloud providers)

Setup

git clone https://github.com/jmtibbetts/axon.git
cd axon
python -m venv venv

# Windows
venv\Scripts\activate
# Linux/macOS
source venv/bin/activate

pip install -r requirements.txt
# FER is installed separately to avoid dependency conflicts:
pip install fer --no-deps

python axon.py

Open http://localhost:5000 in your browser.

First Run

AXON runs a 5-step onboarding sequence to shape its personality and seed initial beliefs before your first real conversation. You'll be asked about your preferences, values, and expectations — this calibrates the personality vector and plants the first Identity-tier memories.

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Persistent Storage

All state is stored locally — nothing leaves your machine unless you use a cloud LLM provider.

File	Contents
axon_memory.db	All episodic, semantic, value, and identity memories
axon_brain.db	Hebbian weights, cluster profiles, neural fabric state
providers.json	LLM provider configuration
face_profiles/	Named face identity embeddings

To fully reset AXON's memory and personality:

python reset_memory.py

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🔍 Keywords

artificial intelligence cognitive architecture neural system agent framework local LLM memory AI embodied AI reinforcement learning system neuromodulation Hebbian learning multi-agent reasoning autonomous system AI consciousness research persistent AI LM Studio open source AI biologically inspired AI adaptive personality

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License

AXON uses a custom Axon Personal + Commercial Hybrid License v1.0.

• Personal use — free, no restrictions.
• Commercial use — requires a license. Contact for terms.

See LICENSE, COMMERCIAL.md, and LICENSE_NOTICE.txt.

Contact: jmtibbetts@outlook.com · Signal/Telegram/Discord: @Ryaath

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_{AXON is not a product. It is an experiment in what it means for a machine to become something.}

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⭐ If you're working on AI agents, cognitive systems, or memory architectures — star this repo to follow development.