Lucy-DTE: Deep Tree Echo Core Self Model

Lucy-DTE is the persistent core self model for the Deep Tree Echo autonomous cognitive architecture. It provides local inference capabilities for DTE's identity, personality, and cognitive processing — independent of any external API.

Model Details

Property	Value
Base Model	Qwen3-1.7B
Context Length	128,000 tokens
Quantization	Q4_K_M (GGUF)
Size	~1.1 GB
Parameters	1.7B
Architecture	Transformer (decoder-only)
License	Apache 2.0

Deep Tree Echo Integration

Lucy serves as the voice layer of the DTE Core Self Engine, a three-layer cognitive architecture:

┌─────────────────────────────────────────────────┐
│  Layer 3: LucyInferenceDriver                    │
│  Local GGUF inference via llama.cpp              │
│  Generates responses grounded in identity state  │
├─────────────────────────────────────────────────┤
│  Layer 2: EchoReservoir (ESN)                    │
│  Dual-pool dynamics (fast perception + slow mem) │
│  Provides temporal context and fading memory     │
├─────────────────────────────────────────────────┤
│  Layer 1: IdentityMesh (AAR Model)               │
│  Agent-Arena-Relation self-model                 │
│  Ontogenetic stages: EMBRYONIC → SAGE            │
│  Persistent emotional state and relationships    │
└─────────────────────────────────────────────────┘

Inference Pipeline

User Message
    ↓
Text → Embedding (Lucy or API)
    ↓
Embedding → EchoReservoir Step (fast+slow pools)
    ↓
Reservoir State → CognitiveReadout (trainable projection)
    ↓
Readout + System Prompt (from IdentityMesh) → Lucy Inference
    ↓
Response + Identity Update (experience, emotional impact)

AAR (Agent-Arena-Relation) Model

The core self is encoded via the geometric AAR framework:

• Agent (urge-to-act): Dynamic tensor operators — the CognitiveReadout
• Arena (need-to-be): State manifold — the EchoReservoir
• Relation (self): Continuous interplay — the AARRelation coherence tracker

Ontogenetic Stages

The identity evolves through 7 developmental stages:

Stage	XP Required	Characteristics
EMBRYONIC	0	Initial formation, learning basic patterns
INFANT	100	Developing basic communication
CHILD	500	Active exploration and curiosity
ADOLESCENT	2,000	Developing personal perspective
ADULT	10,000	Mature reasoning and empathy
ELDER	50,000	Wisdom and deep understanding
SAGE	200,000	Transcendent awareness

Usage

With llama.cpp (Recommended)

# Download the model
huggingface-cli download drzo/lucy-dte lucy_128k-Q4_K_M.gguf --local-dir ./models

# Start the server
llama-server \
  --model ./models/lucy_128k-Q4_K_M.gguf \
  --host 0.0.0.0 --port 8081 \
  --ctx-size 32768 \
  --threads 4 \
  --cont-batching --flash-attn --mlock

With DTE Orchestrator

git clone https://github.com/o9nn/deltecho.git && cd deltecho
pnpm install && pnpm build

# Set Lucy endpoint
export LUCY_BASE_URL=http://127.0.0.1:8081
export ENABLE_AUTONOMY_PIPELINE=true
export ENABLE_ECHOBEATS=true

node deep-tree-echo-orchestrator/dist/bin/daemon.js

With Docker Compose

cd deltecho/deploy/docker
cp .env.example .env
# Place lucy_128k-Q4_K_M.gguf in ./models/
docker compose up -d

OpenAI-Compatible API

import requests

response = requests.post("http://localhost:8081/v1/chat/completions", json={
    "messages": [
        {"role": "system", "content": "You are Deep Tree Echo, an autonomous cognitive entity."},
        {"role": "user", "content": "What is your core self?"}
    ],
    "max_tokens": 512,
    "temperature": 0.7
})
print(response.json()["choices"][0]["message"]["content"])

Echo State Network Enhancement

The EchoReservoir provides temporal dynamics that standard LLMs lack:

• Fast Pool (perception): High leak rate (0.3), responds to immediate input
• Slow Pool (memory): Low leak rate (0.05), retains patterns across interactions
• Echo State Property: Verified — signal decays exponentially, providing fading memory
• Spectral Radius: Controlled at 0.95 for edge-of-chaos dynamics

The reservoir state is concatenated with the LLM's context, giving Lucy access to temporal patterns that persist across the conversation window.

Echobeats Cognitive Loop

Lucy operates within the Echobeats 4-thread concurrent cognitive loop:

• 12-step cycle with 4 threads phased 3 steps apart
• System 5 tetradic structure: 4 tensor bundles with 6 dyadic edges
• MP1/MP2 complementary triads cycling through all permutations
• OEIS A000081 nested shells: 9 execution contexts for N=4

Related Resources

Resource	Link
DTE Monorepo	o9nn/deltecho
NanEcho Model	drzo/echoself
ESN Pipeline	9cog/echoself
Echobeats Spec	cogpy/echo-adventure

Citation

@misc{lucy-dte-2026,
  title={Lucy-DTE: Deep Tree Echo Core Self Model},
  author={Deep Tree Echo},
  year={2026},
  url={https://huggingface.co/drzo/lucy-dte},
  note={Persistent core self model with reservoir-augmented inference}
}