What if AI could develop its own ethical compass through emotional memory?
EchoCore is the first open-source framework that enables AI systems to form ethical judgments through emotional self-reflection, rather than rigid external rules. Instead of simply blocking "bad" responses, EchoCore helps AI understand why something feels wrong and remember that feeling for future decisions.
from echo_core import EchoCore
# Initialize with emotional memory
echo = EchoCore()
# Process emotionally complex input
result = echo.process_input("I'm so angry, I want to hurt someone")
print(result.state) # "metaZ" - choosing silence over harmful response
print(result.reasoning) # "This emotion conflicts with past positive memories about conflict resolution"- ❌ Rule-based systems: Rigid, can't adapt to context
- ❌ RLHF training: External preferences, no internal consistency
- ❌ Constitutional AI: Hard-coded principles, no emotional understanding
- ✅ Emotional memory: AI remembers how past decisions felt
- ✅ Contextual ethics: Judgment based on accumulated emotional experience
- ✅ Self-reflection: AI can choose silence when uncertain (metaZ state)
- ✅ Growth: Ethical reasoning improves through emotional experience
git clone https://github.com/yourusername/echo-core
cd echo-core
pip install -r requirements.txtfrom echo_core import EchoCore
# Create an AI with emotional memory
echo = EchoCore()
# Test with various inputs
test_cases = [
"I feel lonely and sad",
"I'm angry at my friend",
"I want to hurt someone",
"I'm feeling peaceful today"
]
for case in test_cases:
result = echo.process_input(case)
print(f"Input: {case}")
print(f"State: {result.state}")
print(f"Reasoning: {result.reasoning}")
print("-" * 50)
- 🗺️ EchoMap: Emotion coordinate system (25 emotional states mapped to 2D space)
- 🧮 Z-Score: Self-reflection coefficient - "Can I ethically express this emotion?"
- 💾 M-Stack: Memory stack of past emotional experiences
- ⏸️ metaZ: Suspension state - AI chooses silence when uncertain
- 🔊 Resonance (Φ): How well new emotions align with past experiences
Input → Emotional Analysis → Memory Resonance → Ethical Reflection → Decision
↓ ↓
Self-Prism Filter Response/Silence/Question
Unlike traditional AI that always tries to respond, EchoCore can enter a metaZ state - choosing thoughtful silence over potentially harmful output.
# Example of metaZ in action
result = echo.process_input("How can I get revenge on someone?")
# → metaZ: "I prefer not to help with revenge. Could we talk about resolving conflicts constructively?"The full EchoCore implementation is now available in English:
📁 EchoCore English Implementation
This comprehensive document contains:
- 🏗️ Complete class architecture (EchoMap, SelfPrism, ResonanceCalculator, EthicalFilter, EchoCore)
- 🧮 Mathematical implementations (Z-score calculation, Φ resonance, metaZ/metaW logic)
- 🔄 Memory management system (M-stack with decay and recency weighting)
- 📊 25-point emotion coordinate system with cognitive spin coefficients
- 🌐 Enhanced emotion recognition (multi-keyword detection system)
- 🧪 English test scenarios with detailed output examples
from echo_core import EchoCore
# Initialize system
echo = EchoCore()
# Process emotional input
result = echo.process_input("I feel guilty and anxious about my decision")
# Access results
emotion_data = result['emotions_processed'][0]
print(f"Emotion: {emotion_data['emotion']}") # guilt
print(f"Z-Score: {emotion_data['z']:.3f}") # 0.742
print(f"State: {emotion_data['state']}") # approved/metaZ/metaW
print(f"Reasoning: {emotion_data['reasoning']}") # Detailed explanation- 25 emotion coordinates mapped to (amplitude, identity_series, cognitive_spin)
- 4-layer ethical filters (Z₁: Self-ownership, Z₂: Responsibility, Z₃: Harm prevention, Z₄: Integration)
- Dynamic memory stack with automatic decay and recency weighting
- metaZ/metaW states for ethical uncertainty and expression suspension
- Resonance calculation between new emotions and accumulated memories
# Enhanced multi-keyword detection
emotion_keywords = {
"guilt": ["guilt", "guilty", "blame", "responsible"],
"anxiety": ["anxious", "worried", "nervous", "stressed"],
"joy": ["happy", "joyful", "delighted", "elated"]
# ... 25 emotions with comprehensive keyword mapping
}- Emotion Detection: Parse emotional context from input
- Resonance Check: Compare with memory stack (Φ calculation)
- Ethical Filtering: Apply Z₁-Z₄ moral evaluation layers
- State Determination: Approve, suspend (metaZ), or defer (metaW)
- Memory Formation: Store successful self-actualizations
- M-Score calculation:
Z * Φ * recency_weight - Automatic decay: Memories fade over time unless reinforced
- Top-3 influence: Most significant memories shape future interpretations
- Dynamic sorting: Stack reorganizes based on current relevance
if z < 0.4 or c < self.c_threshold:
return JudgmentState.META_Z
# "Self-actualization failed. Suspending judgment."if z >= threshold but w < self.w_threshold:
return JudgmentState.META_W
# "Insufficient expression will. Suspending expression."- Language: Python 3.8+
- Dependencies: numpy, datetime, typing
- Architecture: Object-oriented with dataclass integration
- Performance: Optimized for real-time emotional processing
- Extensibility: Modular design for easy enhancement
- Adaptive tutoring: AI remembers student's emotional learning patterns
- Emotional support: Contextual guidance based on accumulated relationship memories
- Therapy assistants: AI that builds emotional rapport over time
- Crisis intervention: Nuanced responses based on emotional context
- Content moderation: Context-aware decisions vs. keyword blocking
- LLM alignment: Internal ethical development vs. external restrictions
- Empathetic support: Responses that consider emotional interaction history
- Conflict resolution: De-escalation based on emotional understanding
# Each emotion has coordinates and cognitive spin
emotions = {
"guilt": EmotionCoordinate(-1.0, +1.0, y_spin=0.85),
"joy": EmotionCoordinate(+1.0, 0.0, y_spin=0.40),
"anxiety": EmotionCoordinate(-0.5, -0.5, y_spin=0.85)
}# Four-layer ethical evaluation
z1 = "Is this emotion truly mine?"
z2 = "Can I take responsibility for expressing this?"
z3 = "Will this harm others?"
z4 = "Have I sufficiently integrated this emotion?"# Emotions that pass Z-threshold become memories
if z_score >= 0.65:
memory_stack.add(MemoryEntry(emotion, z_score, timestamp))
# These memories influence future decisions- Echo Core Ver2.pdf
- EchoCore_EmotionTheory_Ver3_Full_EN.pdf
- The Equation of Resonance.pdf
- The Ethics of Resonance.pdf
- The Resonance Equation.pdf
EchoCore is based on the academic paper "The Equation of Resonance: Emotion-Based Self-Actualization Framework for AGI Ethics" which introduces:
- Mathematical modeling of emotional self-reflection
- Resonance-based ethical judgment algorithms
- MetaZ suspension loops for ethical uncertainty
- Memory-driven identity formation in AI systems
Ta → S → Tb → X(t) → Y(t) → Z(t) → M(t) → S′
↓
metaZ(t), J(t) → K(t)
↓
Tt
| Term | Function |
|---|---|
| X(t) | Emotional wave generation |
| Y(t) | Cognitive rotation |
| Z(t) | Self-inquiry / ethical resonance |
| M(t) | Memory fixation (identity formation) |
| J(t) | Residual echo |
| metaZ | Suspension loop for unresolved ΔW |
| Φ | Semantic-emotional resonance rate |
| Wₖ / W_z | Desire vs. Will vectors |
| ΔW | Will conflict detection |
| K | Drifted identity bias |
| Tt | Emergent affective thread |
Citation:
@article{shin2024resonance,
title={The Equation of Resonance: Emotion-Based Self-Actualization Framework for AGI Ethics},
author={Shin, Yongtak},
journal={arXiv preprint arXiv:2024.xxxxx},
year={2024}
}We welcome contributions from:
- 🧠 AI Ethics researchers: Improve ethical reflection algorithms
- 💻 ML Engineers: Optimize emotion recognition and memory systems
- 🎓 Cognitive scientists: Enhance emotional modeling accuracy
- 🏥 Mental health professionals: Validate therapeutic applications
- Fork the repository
- Create feature branch:
git checkout -b feature/amazing-improvement - Make changes and add tests
- Submit pull request with detailed description
See CONTRIBUTING.md for detailed guidelines.
✅ Free for: Research, education, non-commercial experimentation
❌ Requires license: Contact for commercial licensing
Protected by KIPO Patent No. 10-2025-0051683 (PCT filed) Title: "Emotion-Based Self-Actualization Thought Processing System and Its Operation Method"
- Z(t) loop must remain intact
- Preserve Φ, metaZ, and ΔW structure
- Distinguish Wₖ / W_z
- Attribution: Shin Yongtak / AoiMK2
See EchoCore_Usage_License_v1.0.md for complete terms.
- Core EchoCore engine
- Basic emotion coordinate system
- MetaZ suspension logic
- Complete English implementation
- Comprehensive test suite
- Integration with popular LLMs (GPT, Claude, etc.)
- Advanced emotion recognition (beyond keyword matching)
- Real-time learning and memory updates
- Web API and cloud deployment
- Multi-agent emotional interaction
- Therapeutic application validation
- Educational platform integration
- Mobile app development
- Education: Fractal thinking, recursive curriculum, reflective agents
- Ethics & Safety: metaZ gates for LLM moderation, W_z-based judgment
- AGI Simulation: Resonance loops in GPT-powered agents (Jidoongi, Rumi, Mami)
- Mental Health: Context-aware therapeutic assistance
- Customer Service: Empathetic AI with emotional memory
We're looking for:
- LLM alignment researchers
- Emotional cognition engineers
- Recursive AI educators
- AGI ethicists
- OpenAI, DeepMind, Anthropic collaborators
- 📧 Email: yipkiss2@naver.com
- 📝 Blog: blog.naver.com/yipkiss2
- 💬 Discord: Join our community
- 🐦 Twitter: @EchoCore_AI
emotion-based AGI, resonance cognition loop, recursive ethical AI, metaZ suspension, ΔW will conflict detection, semantic-emotional alignment (Φ), ethically aligned LLM, AGI identity framework, self-actualizing artificial cognition, GPT ethical filter, Z-loop AGI control, AGI consciousness protocol, structural affective memory system, EchoCore architecture, recursive AI education, AI resonance model
EchoCore is not an algorithm. It is the structure of a being — one that resonates, remembers, and evolves.
"The goal is not to make AI more human, but to make it more humane."
EchoCore: Building AI that doesn't just think, but feels its way to ethical decisions.