README.md

---
license: apache-2.0
language:
  - en
tags:
  - deep-tree-echo
  - cognitive-architecture
  - autonomous-agent
  - reservoir-computing
  - echo-state-network
  - gguf
  - qwen3
  - deltecho
library_name: llama.cpp
pipeline_tag: text-generation
base_model: Qwen/Qwen3-1.7B
model-index:
  - name: lucy-dte
    results: []
---

# Lucy-DTE: Deep Tree Echo Core Self Model

Lucy-DTE is the persistent core self model for the [Deep Tree Echo](https://github.com/o9nn/deltecho) 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)

```bash
# 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

```bash
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

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

### OpenAI-Compatible API

```python
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](https://github.com/o9nn/deltecho) |
| NanEcho Model | [drzo/echoself](https://huggingface.co/drzo/echoself) |
| ESN Pipeline | [9cog/echoself](https://github.com/9cog/echoself) |
| Echobeats Spec | [cogpy/echo-adventure](https://github.com/cogpy/echo-adventure) |

## Citation

```bibtex
@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}
}
```