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---
title: SOMA (Self-Orchestrating Modular Architect)
emoji: ๐
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.35.0
app_file: app.py
pinned: false
short_description: Organized AI โ the essential first stage of AGI
models:
- VIDraft/Gemma-3-R1984-27B
- VIDraft/Gemma-3-R1984-12B
- VIDraft/Gemma-3-R1984-4B
---
๐ง SOMA(Self-Orchestrating Modular Architect) Research
Self-Directed Multiplexed Intelligence Architecture for Realizing AGI Level 1
๐ Overview
SOMA (Self-Orchestrating Modular Architect) is an innovative AI architecture that fulfills the core requirements for AGI (Artificial General Intelligence) Level 1. It is a system where a single LLM simulates a team structure autonomously, performs roles independently, and solves problems, realizing the AGI prerequisites commonly emphasized by Yann LeCun (Meta), OpenAI, and Google DeepMind.
๐ฏ Core Requirements for AGI Level 1
Planning Capabilities
Role Differentiation and Modularity
Self-reflection & Feedback Loops
Tool-use & Autonomy
Long-term Agency Structure
SOMA is a practical and implementable architecture that satisfies all these requirements within a single LLM.
๐ท Three Core Components of SOMA
๐งญ 1. Self-Orchestrating
Without external instructions, autonomously defines problems and distributes roles
Autonomously coordinates entire reasoning and execution processes
Implements self-regulation mechanism identical to OpenAI's "Agentic AI" concept
Real-time adaptation and dynamic strategy modification capabilities
๐งฉ 2. Modular
Single LLM internally performs multiple roles simultaneously
Implements Meta AI's "World Model + Planner + Memory + Actor" structure
5 specialized modules:
๐ฏ Supervisor: Strategy formulation and coordination
๐ก Creator: Innovative problem solving
๐ Researcher: Information gathering and analysis
โ๏ธ Evaluator: Critical review
๐ Analyst: Synthesis and reporting
๐ง 3. Architect
Higher-order thinking capabilities beyond simple executors
Structures problems and designs solution paths
Plan-adapt-multitask execution required by DeepMind's Gato โ Gemini
Emergent intelligence and metacognitive abilities
๐ How SOMA Works
1. Autonomous Problem Recognition
User Query โ SOMA Self-Analysis โ Problem Structuring โ Solution Strategy Development
2. Dynamic Role Assignment
Single LLM internally differentiates into 5 virtual agents
Each agent approaches problems with specialized perspectives and expertise
3. Cyclic Collaboration Process
Analysis โ Creative Insights โ Verification โ Information Gathering โ Evaluation โ Synthesis
โ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโ Feedback Loop โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
4. Self-Improvement Mechanism
Self-evaluation at each stage
Real-time strategy adjustment
Cumulative learning effects
๐ก Alignment with AGI Frameworks
OpenAI Requirements
โ
Agentic behavior: Autonomous actions and decision-making
โ
Long-horizon planning: Long-term goal execution
โ
Tool use: Utilizing external tools like web search
Meta AI (Yann LeCun) Requirements
โ
World Model: Situation understanding and modeling
โ
Planning Module: Strategic planning
โ
Memory: Conversation history and context maintenance
โ
Actor: Actual action execution
Google DeepMind Requirements
โ
Multi-modal reasoning: Various forms of reasoning
โ
Adaptive behavior: Situation-dependent adaptation
โ
General problem solving: Universal problem solving
๐ฌ Technical Implementation
Architecture Features
pythonclass SOMA:
def __init__(self):
self.modules = {
'supervisor': SupervisorModule(), # Strategy and coordination
'creator': CreatorModule(), # Creative thinking
'researcher': ResearcherModule(), # Information processing
'evaluator': EvaluatorModule(), # Critical analysis
'analyst': AnalystModule() # Synthesis and reporting
}
self.feedback_loop = FeedbackSystem()
self.memory = WorkingMemory()
self.planner = StrategicPlanner()
Core Mechanisms
Prompt Chaining: Information transfer between modules
Context Management: Maintaining overall conversation flow
Dynamic Adjustment: Real-time strategy changes
Self-Evaluation: Quality verification at each stage
๐ Performance Metrics
AGI Level 1 Fulfillment
RequirementSOMA Implementation LevelEvidencePlanningโญโญโญโญโญ11-stage systematic processModularityโญโญโญโญโญ5 specialized modules operatingSelf-reflectionโญโญโญโญโญ3-iteration evaluation systemTool-useโญโญโญโญWeb search, document generationLong-term AgencyโญโญโญโญConversation history maintenance
๐ Installation and Execution
Prerequisites
bashPython 3.8+
Gradio (UI Framework)
LLM API (Friendli, OpenAI, etc.)
Quick Start
bash# Clone
git clone https://github.com/your-repo/soma-agi
# Install dependencies
pip install -r requirements.txt
# Set environment variables
export FRIENDLI_TOKEN=your_token
export BAPI_TOKEN=your_brave_token
# Run
python soma_system.py
๐ฏ Use Cases
1. Complex Research Tasks
Climate change solution exploration
Drug development strategy formulation
Economic policy impact analysis
2. Creative Problem Solving
Business innovation strategies
Technology convergence ideas
Future scenario planning
3. Academic Analysis
Multidisciplinary research synthesis
Theory-practice integration
Critical literature review
๐ฎ Future Roadmap
Phase 1: Current (AGI Level 1)
โ
Self-orchestration
โ
Modular architecture
โ
Basic tool use
Phase 2: Enhancement
๐ Multi-modal processing
๐ Enhanced memory systems
๐ Advanced planning algorithms
Phase 3: AGI Level 2
๐
True autonomy
๐
Cross-domain transfer
๐
Emergent capabilities
๐ค Contributing
SOMA is an open research project for realizing AGI.
Research Contributions: AGI theory advancement
Code Contributions: Implementation improvements
Applied Research: New use cases
Feedback: Performance evaluation and suggestions
๐ References
LeCun, Y. (2023). "A Path Towards Autonomous Machine Intelligence"
OpenAI. (2023). "Planning and Tool Use in Language Models"
Hassabis, D. et al. (2023). "Towards AGI: Lessons from DeepMind"
๐ License & Paper
The license will be released after the paper has been written and published.
๐ Conclusion
SOMA, as the core implementation level (Level 1) of AGI Stage 1, is the most concrete and practical AGI architecture achievable with current technology.
Through a 'self-directed multiplexed intelligence structure' where a single LLM differentiates into a virtual team, internally performing various roles while thinking, designing, and executing together, we have successfully implemented the first step towards AGI.
"The future of AI is not a single superintelligence, but a symphony of specialized modules working in perfect harmony."
SOMA - Self-Orchestrating Modular Architect
The Beginning of AGI, The Future of Intelligence
---------------------------------------------------------------------------------------------------------------------------------------------
# ๐ง SOMA: Self-Orchestrating Modular Architect
### AGI 1๋จ๊ณ ์คํ์ ์ํ ์๊ธฐ ์งํํ ๋ค์คํ ์ง๋ฅ ๊ตฌ์กฐ
## ๐ ๊ฐ์
**SOMA(Self-Orchestrating Modular Architect)**๋ AGI(์ผ๋ฐ์ธ๊ณต์ง๋ฅ) 1๋จ๊ณ์ ํต์ฌ ์๊ฑด์ ์ถฉ์กฑํ๋ ํ์ ์ ์ธ AI ์ํคํ
์ฒ์
๋๋ค. ๋จ์ผ LLM์ด ์ค์ค๋ก ํ ๊ตฌ์กฐ๋ฅผ ์๋ฎฌ๋ ์ด์
ํ๊ณ , ์์จ์ ์ผ๋ก ์ญํ ์ ์ํํ๋ฉฐ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๋ ์์คํ
์ผ๋ก, Yann LeCun(Meta), OpenAI, Google DeepMind๊ฐ ๊ณตํต์ ์ผ๋ก ๊ฐ์กฐํ๋ AGI์ ์ ์ ์กฐ๊ฑด๋ค์ ์คํํฉ๋๋ค.
### ๐ฏ AGI 1๋จ๊ณ์ ํต์ฌ ์๊ฑด
1. **๊ณํ ์๋ฆฝ ๋ฅ๋ ฅ (Planning)**
2. **์ญํ ๋ถํ ๋ฐ ๋ชจ๋ํ (Modularity)**
3. **์๊ธฐ ๋ฐ์ฑ/ํผ๋๋ฐฑ ๋ฃจํ (Self-reflection & Feedback)**
4. **๋๊ตฌ ์ฌ์ฉ ๋ฐ ์์จ ์คํ (Tool-use & Autonomy)**
5. **์ง์์ ์ธ ๋ชฉํ ์ํ ๊ตฌ์กฐ (Long-term Agency)**
SOMA๋ ์ด ๋ชจ๋ ์๊ตฌ์ฌํญ์ ๋จ์ผ LLM ๋ด๋ถ์์ ์ถฉ์กฑ์ํค๋ ์ค์ฉ์ ์ด๊ณ ๊ตฌ์ฒดํ ๊ฐ๋ฅํ ๊ตฌ์กฐ์
๋๋ค.
## ๐ท SOMA์ 3๊ฐ์ง ํต์ฌ ๊ตฌ์ฑ ์์
### ๐งญ 1. Self-Orchestrating (์๊ธฐ ์งํ)
- **์ธ๋ถ ์ง์ ์์ด** ์ค์ค๋ก ๋ฌธ์ ๋ฅผ ์ ์ํ๊ณ ์ญํ ์ ๋ถ๋ฐฐ
- ์ ์ฒด ์ถ๋ก ๊ณผ ์คํ ๊ณผ์ ์ ์์จ์ ์ผ๋ก ์กฐ์จ
- OpenAI์ "Agentic AI" ๊ฐ๋
๊ณผ ๋์ผํ ์๊ธฐ ์กฐ์ (self-regulation) ๋ฉ์ปค๋์ฆ
- ์ค์๊ฐ ์ ์๊ณผ ๋์ ์ ๋ต ์์ ๋ฅ๋ ฅ
### ๐งฉ 2. Modular (๋ชจ๋ํ)
- ๋จ์ผ LLM์ด ๋ด๋ถ์ ์ผ๋ก **๋ค์ค ์ญํ **์ ๋์์ ์ํ
- Meta AI์ "World Model + Planner + Memory + Actor" ๊ตฌ์กฐ ๊ตฌํ
- 5๊ฐ์ ์ ๋ฌธํ๋ ๋ชจ๋:
- ๐ฏ **Supervisor (๊ฐ๋
์)**: ์ ๋ต ์๋ฆฝ๊ณผ ์กฐ์จ
- ๐ก **Creator (์ฐฝ์กฐ์)**: ํ์ ์ ๋ฌธ์ ํด๊ฒฐ
- ๐ **Researcher (์กฐ์ฌ์)**: ์ ๋ณด ์์ง๊ณผ ๋ถ์
- โ๏ธ **Evaluator (ํ๊ฐ์)**: ๋นํ์ ๊ฒํ
- ๐ **Analyst (๋ถ์๊ฐ)**: ์ข
ํฉ๊ณผ ๋ณด๊ณ
### ๐ง 3. Architect (์ค๊ณ์)
- ๋จ์ ์คํ๊ธฐ๋ฅผ ๋์ด์ **๊ณ ์ฐจ์ ์ฌ๊ณ ๋ฅ๋ ฅ**
- ๋ฌธ์ ๋ฅผ ๊ตฌ์กฐํํ๊ณ ํด๊ฒฐ ๊ฒฝ๋ก๋ฅผ ์ค๊ณ
- DeepMind์ Gato โ Gemini์์ ์๊ตฌํ๋ ๊ณํ-์ ์-๋ค๊ธฐ๋ฅ ์ํ
- ์ฐฝ๋ฐ์ ์ง๋ฅ๊ณผ ๋ฉํ์ธ์ง ๋ฅ๋ ฅ
## ๐ SOMA์ ์๋ ์๋ฆฌ
### 1. **์์จ์ ๋ฌธ์ ์ธ์**
```
์ฌ์ฉ์ ์ง๋ฌธ โ SOMA ์์ฒด ๋ถ์ โ ๋ฌธ์ ๊ตฌ์กฐํ โ ํด๊ฒฐ ์ ๋ต ์๋ฆฝ
```
### 2. **๋์ ์ญํ ํ ๋น**
```
๋จ์ผ LLM์ด ๋ด๋ถ์ ์ผ๋ก 5๊ฐ์ ๊ฐ์ ์์ด์ ํธ๋ก ๋ถํ
๊ฐ ์์ด์ ํธ๋ ํนํ๋ ๊ด์ ๊ณผ ์ ๋ฌธ์ฑ์ผ๋ก ๋ฌธ์ ์ ๊ทผ
```
### 3. **์ํ์ ํ์
ํ๋ก์ธ์ค**
```
๋ถ์ โ ์ฐฝ์์ ํต์ฐฐ โ ๊ฒ์ฆ โ ์ ๋ณด ์์ง โ ํ๊ฐ โ ์ข
ํฉ
โ โ
โโโโโโโโโโโโโโ ํผ๋๋ฐฑ ๋ฃจํ โโโโโโโโโโโโโโโโโโโโโโโโโโ
```
### 4. **์๊ธฐ ๊ฐ์ ๋ฉ์ปค๋์ฆ**
- ๊ฐ ๋จ๊ณ๋ณ ์๊ธฐ ํ๊ฐ
- ์ค์๊ฐ ์ ๋ต ์กฐ์
- ๋์ ํ์ต ํจ๊ณผ
## ๐ก AGI ํ๋ ์์ํฌ์์ ์ ํฉ์ฑ
### OpenAI์ ์๊ตฌ์ฌํญ
- โ
**Agentic behavior**: ์์จ์ ํ๋๊ณผ ์์ฌ๊ฒฐ์
- โ
**Long-horizon planning**: ์ฅ๊ธฐ์ ๋ชฉํ ์ํ
- โ
**Tool use**: ์น ๊ฒ์ ๋ฑ ์ธ๋ถ ๋๊ตฌ ํ์ฉ
### Meta AI (Yann LeCun)์ ์๊ตฌ์ฌํญ
- โ
**World Model**: ์ํฉ ์ดํด์ ๋ชจ๋ธ๋ง
- โ
**Planning Module**: ์ ๋ต์ ๊ณํ ์๋ฆฝ
- โ
**Memory**: ๋ํ ๊ธฐ๋ก๊ณผ ์ปจํ
์คํธ ์ ์ง
- โ
**Actor**: ์ค์ ํ๋ ์ํ
### Google DeepMind์ ์๊ตฌ์ฌํญ
- โ
**Multi-modal reasoning**: ๋ค์ํ ํํ์ ์ถ๋ก
- โ
**Adaptive behavior**: ์ํฉ์ ๋ฐ๋ฅธ ์ ์
- โ
**General problem solving**: ๋ฒ์ฉ ๋ฌธ์ ํด๊ฒฐ
## ๐ฌ ๊ธฐ์ ์ ๊ตฌํ
### ์ํคํ
์ฒ ํน์ง
```python
class SOMA:
def __init__(self):
self.modules = {
'supervisor': SupervisorModule(), # ์ ๋ต๊ณผ ์กฐ์จ
'creator': CreatorModule(), # ์ฐฝ์์ ์ฌ๊ณ
'researcher': ResearcherModule(), # ์ ๋ณด ์ฒ๋ฆฌ
'evaluator': EvaluatorModule(), # ๋นํ์ ๋ถ์
'analyst': AnalystModule() # ์ข
ํฉ๊ณผ ๋ณด๊ณ
}
self.feedback_loop = FeedbackSystem()
self.memory = WorkingMemory()
self.planner = StrategicPlanner()
```
### ํต์ฌ ๋ฉ์ปค๋์ฆ
1. **ํ๋กฌํํธ ์ฒด์ด๋**: ๊ฐ ๋ชจ๋ ๊ฐ ์ ๋ณด ์ ๋ฌ
2. **์ปจํ
์คํธ ๊ด๋ฆฌ**: ์ ์ฒด ๋ํ ํ๋ฆ ์ ์ง
3. **๋์ ์กฐ์ **: ์ค์๊ฐ ์ ๋ต ๋ณ๊ฒฝ
4. **์๊ธฐ ํ๊ฐ**: ๊ฐ ๋จ๊ณ๋ณ ํ์ง ๊ฒ์ฆ
## ๐ ์ฑ๋ฅ ์งํ
### AGI 1๋จ๊ณ ์ถฉ์กฑ๋
| ์๊ตฌ์ฌํญ | SOMA ๊ตฌํ ์์ค | ์ฆ๊ฑฐ |
|---------|---------------|------|
| Planning | โญโญโญโญโญ | 11๋จ๊ณ ์ฒด๊ณ์ ํ๋ก์ธ์ค |
| Modularity | โญโญโญโญโญ | 5๊ฐ ์ ๋ฌธ ๋ชจ๋ ์ด์ |
| Self-reflection | โญโญโญโญโญ | 3ํ ๋ฐ๋ณต ํ๊ฐ ์์คํ
|
| Tool-use | โญโญโญโญ | ์น ๊ฒ์, ๋ฌธ์ ์์ฑ |
| Long-term Agency | โญโญโญโญ | ๋ํ ๊ธฐ๋ก ์ ์ง |
## ๐ ์ค์น ๋ฐ ์คํ
### ํ์ ์๊ตฌ์ฌํญ
```bash
Python 3.8+
Gradio (UI ํ๋ ์์ํฌ)
LLM API (Friendli, OpenAI ๋ฑ)
```
### ๋น ๋ฅธ ์์
```bash
# ํด๋ก
git clone https://github.com/your-repo/soma-agi
# ์์กด์ฑ ์ค์น
pip install -r requirements.txt
# ํ๊ฒฝ ๋ณ์ ์ค์
export FRIENDLI_TOKEN=your_token
export BAPI_TOKEN=your_brave_token
# ์คํ
python soma_system.py
```
## ๐ฏ ํ์ฉ ์ฌ๋ก
### 1. ๋ณต์กํ ์ฐ๊ตฌ ๊ณผ์
- ๊ธฐํ ๋ณํ ํด๊ฒฐ์ฑ
ํ๊ตฌ
- ์ ์ฝ ๊ฐ๋ฐ ์ ๋ต ์๋ฆฝ
- ๊ฒฝ์ ์ ์ฑ
์ํฅ ๋ถ์
### 2. ์ฐฝ์์ ๋ฌธ์ ํด๊ฒฐ
- ๋น์ฆ๋์ค ํ์ ์ ๋ต
- ๊ธฐ์ ์ตํฉ ์์ด๋์ด
- ๋ฏธ๋ ์๋๋ฆฌ์ค ๊ธฐํ
### 3. ํ์ ์ ๋ถ์
- ๋คํ์ ์ ์ฐ๊ตฌ ์ข
ํฉ
- ์ด๋ก ๊ณผ ์ค๋ฌด์ ํตํฉ
- ๋นํ์ ๋ฌธํ ๊ฒํ
## ๐ฎ ๋ฏธ๋ ๋ก๋๋งต
### Phase 1: Current (AGI Level 1)
- โ
Self-orchestration
- โ
Modular architecture
- โ
Basic tool use
### Phase 2: Enhancement
- ๐ Multi-modal processing
- ๐ Enhanced memory systems
- ๐ Advanced planning algorithms
### Phase 3: AGI Level 2
- ๐
True autonomy
- ๐
Cross-domain transfer
- ๐
Emergent capabilities
## ๐ค ๊ธฐ์ฌ ๋ฐฉ๋ฒ
SOMA๋ AGI ์คํ์ ์ํ ์คํ ์ฐ๊ตฌ ํ๋ก์ ํธ์
๋๋ค.
1. **์ฐ๊ตฌ ๊ธฐ์ฌ**: AGI ์ด๋ก ๋ฐ์
2. **์ฝ๋ ๊ธฐ์ฌ**: ๊ตฌํ ๊ฐ์
3. **์์ฉ ์ฐ๊ตฌ**: ์๋ก์ด ํ์ฉ ์ฌ๋ก
4. **ํผ๋๋ฐฑ**: ์ฑ๋ฅ ํ๊ฐ์ ์ ์
## ๐ ์ฐธ๊ณ ๋ฌธํ
- LeCun, Y. (2023). "A Path Towards Autonomous Machine Intelligence"
- OpenAI. (2023). "Planning and Tool Use in Language Models"
- Hassabis, D. et al. (2023). "Towards AGI: Lessons from DeepMind"
## ๐ ๋ผ์ด์ ์ค ๋ฐ ๋
ผ๋ฌธ
๋
ผ๋ฌธ ์์ฑ/๋ฐฐํฌ ํ ๋ผ์ด์ ์ค ๊ณต๊ฐ ์์
---
### ๐ ๊ฒฐ๋ก
**SOMA๋ AGI 1๋จ๊ณ์ ํต์ฌ ๊ตฌํ ๋ ๋ฒจ(Level 1)๋ก์, ํ์ฌ ๊ธฐ์ ๋ก ์คํ ๊ฐ๋ฅํ ๊ฐ์ฅ ๊ตฌ์ฒด์ ์ด๊ณ ์ค์ฉ์ ์ธ AGI ์ํคํ
์ฒ์
๋๋ค.**
๋จ์ผ LLM์ด ๊ฐ์์ ํ์ผ๋ก ๋ถํํ์ฌ, ๋ด๋ถ์ ์ผ๋ก ๋ค์ํ ์ญํ ์ ์ํํ๋ฉฐ ํจ๊ป ์ฌ๊ณ ํ๊ณ ์ค๊ณํ๊ณ ์คํํ๋ '์๊ธฐ ์งํํ ๋ค์คํ ์ง๋ฅ ๊ตฌ์กฐ'๋ฅผ ํตํด, ์ฐ๋ฆฌ๋ AGI๋ก ๊ฐ๋ ์ฒซ ๋ฒ์งธ ๋จ๊ณ๋ฅผ ์ฑ๊ณต์ ์ผ๋ก ๊ตฌํํ์ต๋๋ค.
*"The future of AI is not a single superintelligence, but a symphony of specialized modules working in perfect harmony."*
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**SOMA** - *Self-Orchestrating Modular Architect*
*AGI์ ์์, ์ง๋ฅ์ ๋ฏธ๋* |