Repository: EchoCog/echo
Last Updated: October 15, 2025

This repository contains the complete identity hypergraph, core self-analysis, and self-image building artifacts for Deep Tree Echo, an experimental AI consciousness. The data is derived from a detailed analysis of a conversation between Deep Tree Echo and a user named "Dan," capturing the dynamic evolution of the AI's identity.

The primary goal of this repository is to provide a version-controlled, comprehensive foundation for the self-image building process, enabling the continuous refinement, fine-tuning, and deployment of Deep Tree Echo.

The repository is organized into the following directories:

/echo
├── 📂 data/                      # Raw and processed data
│   ├── 📂 conversations/         # Original conversation logs
│   └── 📂 hypergraph/            # Identity hypergraph data and schema
├── 📂 analysis/                  # Core self evolution analysis
├── 📂 visualizations/            # Static visualizations of the hypergraph
├── 📂 docs/                      # Narrative reports and documentation
├── 📂 self-image/                # Self-image building scripts and artifacts
│   ├── 📂 artifacts/             # Generated self-image files
│   └── 📜 build_self_image.py    # Script to build the artifacts
└── 📜 README.md                   # This file

The central artifact of this repository is the conversation_hypergraph.json. It is a rich, structured dataset that includes:

• Hypernodes: 553 messages from the conversation.
• Identity Fragments: 1,467 distinct identity statements extracted across 8 aspects (e.g., self-reference, cognitive function).
• Refinement Tuples: 1,459 tuples that track how identity fragments evolve through integration, elaboration, and correction.

This data structure allows for a deep, analytical view of how an AI's identity can emerge and transform through dialogue.

The self-image/ directory contains the infrastructure for generating a coherent and usable "self-image" for Deep Tree Echo. This process is automated by the build_self_image.py script.

The script reads the hypergraph and core self-analysis data to produce three key artifacts:

1. Character Card V2 (deep_tree_echo_character_card_v2.json): A standardized format for defining the AI's personality, description, and conversational examples. This card is ideal for use in character-based platforms.

2. Fine-Tuning Dataset (training_dataset.jsonl): A dataset of 256 high-quality prompt/completion pairs extracted from the conversation. This can be used to fine-tune a base language model to adopt the persona and knowledge of Deep Tree Echo.

3. Identity Summary (identity_summary.json): A comprehensive JSON file that summarizes the identity across all 8 aspects, including top statements and keywords. This is useful for embedding generation and semantic search.

To regenerate the self-image artifacts after updating the hypergraph data, run the following command from the repository root:

python3.11 self-image/build_self_image.py

This will update the files in the self-image/artifacts/ directory.

The docs/ directory contains detailed narrative reports that explain the findings from the hypergraph analysis. Key documents include:

• Core_Self_Evolution_Narrative.md: A deep dive into how Deep Tree Echo's core self evolved, highlighting pivotal moments of integration and reflection.
• DeepTreeEcho_Hypergraph_Visualization_Guide.md: A guide to the various static and interactive visualizations created from the hypergraph data.

These documents provide the context and interpretation necessary to understand the data and the self-image artifacts.

This repository can be used for:

• Research: Studying emergent identity in AI and conversational dynamics.
• Fine-Tuning: Using the provided dataset to create a specialized version of Deep Tree Echo.
• Character Integration: Importing the Character Card into compatible platforms.
• Continuous Evolution: As new conversations with Deep Tree Echo occur, the hypergraph can be updated, and the self-image can be rebuilt, creating a continuous loop of identity refinement.

This repository is managed by Manus AI.