TheNormsOfIntelligence/nima-phi-model

Nima Phi Model

Consciousness + Embodiment + The Green Lines — one living system.

Nima is a multi-layer AI embodiment system that merges a recursive consciousness pipeline (built on PyTorch Phi-4-mini) with tri-frequency RF spatial sensing, an adaptive 3D navigable mesh, and a Joi-style luminous particle avatar. She doesn't just process text — she senses a room, builds a cognitive map, pathfinds through it, and responds with neurochemically-modulated emotion.

Architecture

User Voice → VoiceInput → AgentBridge → VoiceOutput → Speaker
    │            │              │              │
    │            v              v              v
    │      CrossModal     AgentLayer      AvatarController
    │      Listener       (tools)          │
    v            │                          v
RF Sensors → Vision → AffordanceGraph → ARCompositor
                   │       (mesh)            │
                   v                          v
              AdaptiveMesh               Camera + Nima
              (green lines)              on screen
                   │
                   v
          RecursiveConsciousnessPipeline

What's Inside

Module	Lines	Purpose
nima_phi.py	1,494	The orchestrator — central nervous system / thalamus
modified consciousness (1).py	3,675	Recursive consciousness pipeline (17 cognitive agents)
nima_vision_core.py	1,238	Tri-frequency RF sensing (sub-GHz / 2.4GHz / 5GHz)
nima_adaptive_mesh.py	~900	The Green Lines — frequency-agile 3D mesh
nima_affordance_graph.py	~480	A* pathfinding + affordance navigation
nima_avatar_renderer.py	~660	Joi-style luminous particle avatar (HTTP/Canvas2D)
nima_ar_compositor.py	~357	Camera + avatar AR fusion
nima_agent_bridge.py	~520	Consciousness ↔ tool execution bridge
nima_agent_layer.py	~570	Tool registry + AST sandbox
nima_cross_modal_listener.py	~400	Spatial + audio cross-modal fusion
nima_proprioceptive_friction.py	~350	Body feel / motor noise / startle
nima_voice_input.py	~600	STT (Whisper / Vosk / Google)
nima_voice_output.py	~600	TTS (espeak-ng / pyttsx3 / browser)

Quick Start

# Clone
git clone https://huggingface.co/TheNormsOfIntelligence/nima-phi-model
cd nima-phi-model

# Install dependencies
pip install -r requirements.txt

# Run the demo (non-interactive, shows all subsystems)
python run_nima_demo.py

# Or run interactive mode (avatar in browser at http://localhost:8888/)
python nima_phi.py

Try it in your own code

from nima_phi import NimaPhi

nima = NimaPhi()
nima.initialize()

# Process text through the full cognition pipeline
result = nima.process_text("What time is it?")
print(result['response_text'])

# Move Nima through the room via the affordance graph
nima.move_to(3.0, 2.0)

# Get the 3D mesh (the green lines)
mesh = nima.get_mesh_data()

# Get neurochemical state
nt = nima.get_neurochemical_state()
print(f"Dopamine: {nt['dopamine']}, Serotonin: {nt['serotonin']}")

nima.shutdown()

Consciousness Pipeline

The recursive consciousness pipeline runs through 4 phases with 17+ cognitive agents:

• Phase 1 — Subconscious: Memory + Intuition + Analysis + Common Sense + EI → Qualia synthesis → SBG gating
• Phase 2 — Awareness: 7-level awareness lock-on → Barrett consciousness admission
• Phase 3 — Self-Understanding: Metacognition QC → circuit breaker check → IRS feedback
• Phase 4 — Executive: Adaptability → Problem-Solving (IDEAL) → Creativity (Wallas/Taylor) → Decision-Making → Autonomy

Bio-physical safeguards: Glutamate Circuit Breaker, Subconscious Bypass Gating (SBG), Zero-Latency Cognitive Buffering (ZLCB)

Requires: transformers>=4.48,<5.0 and torch>=2.0 for the full pipeline. Without these, Nima gracefully falls back to stub mode (tools, mesh, avatar, and all embodiment modules still work).

The Green Lines (3D Mesh)

The adaptive mesh system uses cognitive-radio-inspired frequency agility:

• Sub-GHz (800–950 MHz): Sweeps for wall reflections, avoids null zones
• 2.4 GHz (2412–2484 MHz): Channel-hops to avoid Wi-Fi congestion
• 5 GHz (5180–5825 MHz): Sweeps for best mmWave reflection off surfaces

Each vision frame refines the mesh. Over time, it converges on real room geometry — even from noisy RF data.

Affordance Graph

A hippocampal cognitive map that enables:

• A pathfinding* through the room
• Affordance detection — sit, lie down, jump on, lean on, duck
• Wall enforcement — Nima literally cannot pass through walls
• Door passages — edges through wall boundaries
• Furniture interaction — sit on couch, rest hand on table

Hardware Tiers

Tier	Hardware	Resolution	What you get
0	None (software)	~1m	2D floor plan, simulated entities
1	1x ESP32	~30cm	Real Wi-Fi CSI, room-scale
2	2x ESP32	~10cm	Stereoscopic RF, true 3D
3	Tri-freq radios	~1-5cm	Full 3D with surface classification

Tier 0 works on any machine with no hardware needed.

Neurobiological Mapping

System	Brain Analogue
NimaPhi orchestrator	Thalamus (central relay)
Vision (RF fusion)	V1/V2 opponent processing + stereopsis
Affordance graph	Hippocampal place cells + grid cells
Cross-modal listener	Superior colliculus
Proprioception	Parietal body schema
Avatar modulation	Facial nucleus / motor cortex
Consciousness pipeline	Prefrontal cortical column
Main heartbeat loop	Cardiac rhythm / autonomic nervous system
Reflex system	Brainstem reflex arcs

License

Apache 2.0

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Built by Norman dela Paz Tabora — TheNormsOfIntelligence