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---
title: SOMA (Self-Orchestrating Modular Architect)
emoji: ๐Ÿš€
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
---

๐Ÿง  SOMA(Self-Orchestrating Modular Architect) Research
Self-Directed Multiplexed Intelligence Architecture for Realizing AGI Level 1

๐Ÿ“Œ 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.

๐ŸŽฏ 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.
๐Ÿ”ท Three Core Components of SOMA
๐Ÿงญ 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

๐Ÿงฉ 2. Modular

Single LLM internally performs multiple roles simultaneously
Implements Meta AI's "World Model + Planner + Memory + Actor" structure
5 specialized modules:

๐ŸŽฏ Supervisor: Strategy formulation and coordination
๐Ÿ’ก Creator: Innovative problem solving
๐Ÿ“š Researcher: Information gathering and analysis
โš–๏ธ Evaluator: Critical review
๐Ÿ“Š Analyst: Synthesis and reporting



๐Ÿง  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

๐Ÿš€ 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†“
โ†โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ Feedback Loop โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ†
4. Self-Improvement Mechanism

Self-evaluation at each stage
Real-time strategy adjustment
Cumulative learning effects

๐Ÿ’ก Alignment with AGI Frameworks
OpenAI Requirements

โœ… Agentic behavior: Autonomous actions and decision-making
โœ… Long-horizon planning: Long-term goal execution
โœ… Tool use: Utilizing external tools like web search

Meta AI (Yann LeCun) Requirements

โœ… World Model: Situation understanding and modeling
โœ… Planning Module: Strategic planning
โœ… Memory: Conversation history and context maintenance
โœ… Actor: Actual action execution

Google DeepMind Requirements

โœ… Multi-modal reasoning: Various forms of reasoning
โœ… Adaptive behavior: Situation-dependent adaptation
โœ… General problem solving: Universal problem solving

๐Ÿ”ฌ 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

๐Ÿ“Š Performance Metrics
AGI Level 1 Fulfillment
RequirementSOMA Implementation LevelEvidencePlanningโญโญโญโญโญ11-stage systematic processModularityโญโญโญโญโญ5 specialized modules operatingSelf-reflectionโญโญโญโญโญ3-iteration evaluation systemTool-useโญโญโญโญWeb search, document generationLong-term AgencyโญโญโญโญConversation history maintenance
๐Ÿš€ 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
๐ŸŽฏ 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

๐Ÿ”ฎ Future Roadmap
Phase 1: Current (AGI Level 1)

โœ… Self-orchestration
โœ… Modular architecture
โœ… Basic tool use

Phase 2: Enhancement

๐Ÿ”„ Multi-modal processing
๐Ÿ”„ Enhanced memory systems
๐Ÿ”„ Advanced planning algorithms

Phase 3: AGI Level 2

๐Ÿ“… True autonomy
๐Ÿ“… Cross-domain transfer
๐Ÿ“… Emergent capabilities

๐Ÿค 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

๐Ÿ“š 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"

๐Ÿ“ License & Paper
The license will be released after the paper has been written and published.


๐ŸŒŸ 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

---------------------------------------------------------------------------------------------------------------------------------------------

# ๐Ÿง  SOMA: Self-Orchestrating Modular Architect
### AGI 1๋‹จ๊ณ„ ์‹คํ˜„์„ ์œ„ํ•œ ์ž๊ธฐ ์ง€ํœ˜ํ˜• ๋‹ค์ค‘ํ™” ์ง€๋Šฅ ๊ตฌ์กฐ

## ๐Ÿ“Œ ๊ฐœ์š”
**SOMA(Self-Orchestrating Modular Architect)**๋Š” AGI(์ผ๋ฐ˜์ธ๊ณต์ง€๋Šฅ) 1๋‹จ๊ณ„์˜ ํ•ต์‹ฌ ์š”๊ฑด์„ ์ถฉ์กฑํ•˜๋Š” ํ˜์‹ ์ ์ธ AI ์•„ํ‚คํ…์ฒ˜์ž…๋‹ˆ๋‹ค. ๋‹จ์ผ LLM์ด ์Šค์Šค๋กœ ํŒ€ ๊ตฌ์กฐ๋ฅผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜๊ณ , ์ž์œจ์ ์œผ๋กœ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๋ฉฐ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ์‹œ์Šคํ…œ์œผ๋กœ, Yann LeCun(Meta), OpenAI, Google DeepMind๊ฐ€ ๊ณตํ†ต์ ์œผ๋กœ ๊ฐ•์กฐํ•˜๋Š” AGI์˜ ์ „์ œ ์กฐ๊ฑด๋“ค์„ ์‹คํ˜„ํ•ฉ๋‹ˆ๋‹ค.

### ๐ŸŽฏ AGI 1๋‹จ๊ณ„์˜ ํ•ต์‹ฌ ์š”๊ฑด
1. **๊ณ„ํš ์ˆ˜๋ฆฝ ๋Šฅ๋ ฅ (Planning)**
2. **์—ญํ•  ๋ถ„ํ™” ๋ฐ ๋ชจ๋“ˆํ™” (Modularity)**
3. **์ž๊ธฐ ๋ฐ˜์„ฑ/ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„ (Self-reflection & Feedback)**
4. **๋„๊ตฌ ์‚ฌ์šฉ ๋ฐ ์ž์œจ ์‹คํ–‰ (Tool-use & Autonomy)**
5. **์ง€์†์ ์ธ ๋ชฉํ‘œ ์ˆ˜ํ–‰ ๊ตฌ์กฐ (Long-term Agency)**

SOMA๋Š” ์ด ๋ชจ๋“  ์š”๊ตฌ์‚ฌํ•ญ์„ ๋‹จ์ผ LLM ๋‚ด๋ถ€์—์„œ ์ถฉ์กฑ์‹œํ‚ค๋Š” ์‹ค์šฉ์ ์ด๊ณ  ๊ตฌ์ฒดํ™” ๊ฐ€๋Šฅํ•œ ๊ตฌ์กฐ์ž…๋‹ˆ๋‹ค.

## ๐Ÿ”ท SOMA์˜ 3๊ฐ€์ง€ ํ•ต์‹ฌ ๊ตฌ์„ฑ ์š”์†Œ

### ๐Ÿงญ 1. Self-Orchestrating (์ž๊ธฐ ์ง€ํœ˜)
- **์™ธ๋ถ€ ์ง€์‹œ ์—†์ด** ์Šค์Šค๋กœ ๋ฌธ์ œ๋ฅผ ์ •์˜ํ•˜๊ณ  ์—ญํ• ์„ ๋ถ„๋ฐฐ
- ์ „์ฒด ์ถ”๋ก ๊ณผ ์‹คํ–‰ ๊ณผ์ •์„ ์ž์œจ์ ์œผ๋กœ ์กฐ์œจ
- OpenAI์˜ "Agentic AI" ๊ฐœ๋…๊ณผ ๋™์ผํ•œ ์ž๊ธฐ ์กฐ์ •(self-regulation) ๋ฉ”์ปค๋‹ˆ์ฆ˜
- ์‹ค์‹œ๊ฐ„ ์ ์‘๊ณผ ๋™์  ์ „๋žต ์ˆ˜์ • ๋Šฅ๋ ฅ

### ๐Ÿงฉ 2. Modular (๋ชจ๋“ˆํ™”)
- ๋‹จ์ผ LLM์ด ๋‚ด๋ถ€์ ์œผ๋กœ **๋‹ค์ค‘ ์—ญํ• **์„ ๋™์‹œ์— ์ˆ˜ํ–‰
- Meta AI์˜ "World Model + Planner + Memory + Actor" ๊ตฌ์กฐ ๊ตฌํ˜„
- 5๊ฐœ์˜ ์ „๋ฌธํ™”๋œ ๋ชจ๋“ˆ:
  - ๐ŸŽฏ **Supervisor (๊ฐ๋…์ž)**: ์ „๋žต ์ˆ˜๋ฆฝ๊ณผ ์กฐ์œจ
  - ๐Ÿ’ก **Creator (์ฐฝ์กฐ์ž)**: ํ˜์‹ ์  ๋ฌธ์ œ ํ•ด๊ฒฐ
  - ๐Ÿ“š **Researcher (์กฐ์‚ฌ์ž)**: ์ •๋ณด ์ˆ˜์ง‘๊ณผ ๋ถ„์„
  - โš–๏ธ **Evaluator (ํ‰๊ฐ€์ž)**: ๋น„ํŒ์  ๊ฒ€ํ† 
  - ๐Ÿ“Š **Analyst (๋ถ„์„๊ฐ€)**: ์ข…ํ•ฉ๊ณผ ๋ณด๊ณ 

### ๐Ÿง  3. Architect (์„ค๊ณ„์ž)
- ๋‹จ์ˆœ ์‹คํ–‰๊ธฐ๋ฅผ ๋„˜์–ด์„  **๊ณ ์ฐจ์› ์‚ฌ๊ณ  ๋Šฅ๋ ฅ**
- ๋ฌธ์ œ๋ฅผ ๊ตฌ์กฐํ™”ํ•˜๊ณ  ํ•ด๊ฒฐ ๊ฒฝ๋กœ๋ฅผ ์„ค๊ณ„
- DeepMind์˜ Gato โ†’ Gemini์—์„œ ์š”๊ตฌํ•˜๋Š” ๊ณ„ํš-์ ์‘-๋‹ค๊ธฐ๋Šฅ ์ˆ˜ํ–‰
- ์ฐฝ๋ฐœ์  ์ง€๋Šฅ๊ณผ ๋ฉ”ํƒ€์ธ์ง€ ๋Šฅ๋ ฅ

## ๐Ÿš€ SOMA์˜ ์ž‘๋™ ์›๋ฆฌ

### 1. **์ž์œจ์  ๋ฌธ์ œ ์ธ์‹**
```
์‚ฌ์šฉ์ž ์งˆ๋ฌธ โ†’ SOMA ์ž์ฒด ๋ถ„์„ โ†’ ๋ฌธ์ œ ๊ตฌ์กฐํ™” โ†’ ํ•ด๊ฒฐ ์ „๋žต ์ˆ˜๋ฆฝ
```

### 2. **๋™์  ์—ญํ•  ํ• ๋‹น**
```
๋‹จ์ผ LLM์ด ๋‚ด๋ถ€์ ์œผ๋กœ 5๊ฐœ์˜ ๊ฐ€์ƒ ์—์ด์ „ํŠธ๋กœ ๋ถ„ํ™”
๊ฐ ์—์ด์ „ํŠธ๋Š” ํŠนํ™”๋œ ๊ด€์ ๊ณผ ์ „๋ฌธ์„ฑ์œผ๋กœ ๋ฌธ์ œ ์ ‘๊ทผ
```

### 3. **์ˆœํ™˜์  ํ˜‘์—… ํ”„๋กœ์„ธ์Šค**
```
๋ถ„์„ โ†’ ์ฐฝ์˜์  ํ†ต์ฐฐ โ†’ ๊ฒ€์ฆ โ†’ ์ •๋ณด ์ˆ˜์ง‘ โ†’ ํ‰๊ฐ€ โ†’ ์ข…ํ•ฉ
โ†‘                                                    โ†“
โ†โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„ โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ†
```

### 4. **์ž๊ธฐ ๊ฐœ์„  ๋ฉ”์ปค๋‹ˆ์ฆ˜**
- ๊ฐ ๋‹จ๊ณ„๋ณ„ ์ž๊ธฐ ํ‰๊ฐ€
- ์‹ค์‹œ๊ฐ„ ์ „๋žต ์กฐ์ •
- ๋ˆ„์  ํ•™์Šต ํšจ๊ณผ

## ๐Ÿ’ก AGI ํ”„๋ ˆ์ž„์›Œํฌ์™€์˜ ์ •ํ•ฉ์„ฑ

### OpenAI์˜ ์š”๊ตฌ์‚ฌํ•ญ
- โœ… **Agentic behavior**: ์ž์œจ์  ํ–‰๋™๊ณผ ์˜์‚ฌ๊ฒฐ์ •
- โœ… **Long-horizon planning**: ์žฅ๊ธฐ์  ๋ชฉํ‘œ ์ˆ˜ํ–‰
- โœ… **Tool use**: ์›น ๊ฒ€์ƒ‰ ๋“ฑ ์™ธ๋ถ€ ๋„๊ตฌ ํ™œ์šฉ

### Meta AI (Yann LeCun)์˜ ์š”๊ตฌ์‚ฌํ•ญ
- โœ… **World Model**: ์ƒํ™ฉ ์ดํ•ด์™€ ๋ชจ๋ธ๋ง
- โœ… **Planning Module**: ์ „๋žต์  ๊ณ„ํš ์ˆ˜๋ฆฝ
- โœ… **Memory**: ๋Œ€ํ™” ๊ธฐ๋ก๊ณผ ์ปจํ…์ŠคํŠธ ์œ ์ง€
- โœ… **Actor**: ์‹ค์ œ ํ–‰๋™ ์ˆ˜ํ–‰

### Google DeepMind์˜ ์š”๊ตฌ์‚ฌํ•ญ
- โœ… **Multi-modal reasoning**: ๋‹ค์–‘ํ•œ ํ˜•ํƒœ์˜ ์ถ”๋ก 
- โœ… **Adaptive behavior**: ์ƒํ™ฉ์— ๋”ฐ๋ฅธ ์ ์‘
- โœ… **General problem solving**: ๋ฒ”์šฉ ๋ฌธ์ œ ํ•ด๊ฒฐ

## ๐Ÿ”ฌ ๊ธฐ์ˆ ์  ๊ตฌํ˜„

### ์•„ํ‚คํ…์ฒ˜ ํŠน์ง•
```python
class SOMA:
    def __init__(self):
        self.modules = {
            'supervisor': SupervisorModule(),    # ์ „๋žต๊ณผ ์กฐ์œจ
            'creator': CreatorModule(),          # ์ฐฝ์˜์  ์‚ฌ๊ณ 
            'researcher': ResearcherModule(),    # ์ •๋ณด ์ฒ˜๋ฆฌ
            'evaluator': EvaluatorModule(),      # ๋น„ํŒ์  ๋ถ„์„
            'analyst': AnalystModule()           # ์ข…ํ•ฉ๊ณผ ๋ณด๊ณ 
        }
        self.feedback_loop = FeedbackSystem()
        self.memory = WorkingMemory()
        self.planner = StrategicPlanner()
```

### ํ•ต์‹ฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜
1. **ํ”„๋กฌํ”„ํŠธ ์ฒด์ด๋‹**: ๊ฐ ๋ชจ๋“ˆ ๊ฐ„ ์ •๋ณด ์ „๋‹ฌ
2. **์ปจํ…์ŠคํŠธ ๊ด€๋ฆฌ**: ์ „์ฒด ๋Œ€ํ™” ํ๋ฆ„ ์œ ์ง€
3. **๋™์  ์กฐ์ •**: ์‹ค์‹œ๊ฐ„ ์ „๋žต ๋ณ€๊ฒฝ
4. **์ž๊ธฐ ํ‰๊ฐ€**: ๊ฐ ๋‹จ๊ณ„๋ณ„ ํ’ˆ์งˆ ๊ฒ€์ฆ

## ๐Ÿ“Š ์„ฑ๋Šฅ ์ง€ํ‘œ

### AGI 1๋‹จ๊ณ„ ์ถฉ์กฑ๋„
| ์š”๊ตฌ์‚ฌํ•ญ | SOMA ๊ตฌํ˜„ ์ˆ˜์ค€ | ์ฆ๊ฑฐ |
|---------|---------------|------|
| Planning | โญโญโญโญโญ | 11๋‹จ๊ณ„ ์ฒด๊ณ„์  ํ”„๋กœ์„ธ์Šค |
| Modularity | โญโญโญโญโญ | 5๊ฐœ ์ „๋ฌธ ๋ชจ๋“ˆ ์šด์˜ |
| Self-reflection | โญโญโญโญโญ | 3ํšŒ ๋ฐ˜๋ณต ํ‰๊ฐ€ ์‹œ์Šคํ…œ |
| Tool-use | โญโญโญโญ | ์›น ๊ฒ€์ƒ‰, ๋ฌธ์„œ ์ƒ์„ฑ |
| Long-term Agency | โญโญโญโญ | ๋Œ€ํ™” ๊ธฐ๋ก ์œ ์ง€ |

## ๐Ÿš€ ์„ค์น˜ ๋ฐ ์‹คํ–‰

### ํ•„์ˆ˜ ์š”๊ตฌ์‚ฌํ•ญ
```bash
Python 3.8+
Gradio (UI ํ”„๋ ˆ์ž„์›Œํฌ)
LLM API (Friendli, OpenAI ๋“ฑ)
```

### ๋น ๋ฅธ ์‹œ์ž‘
```bash
# ํด๋ก 
git clone https://github.com/your-repo/soma-agi

# ์˜์กด์„ฑ ์„ค์น˜
pip install -r requirements.txt

# ํ™˜๊ฒฝ ๋ณ€์ˆ˜ ์„ค์ •
export FRIENDLI_TOKEN=your_token
export BAPI_TOKEN=your_brave_token

# ์‹คํ–‰
python soma_system.py
```

## ๐ŸŽฏ ํ™œ์šฉ ์‚ฌ๋ก€

### 1. ๋ณต์žกํ•œ ์—ฐ๊ตฌ ๊ณผ์ œ
- ๊ธฐํ›„ ๋ณ€ํ™” ํ•ด๊ฒฐ์ฑ… ํƒ๊ตฌ
- ์‹ ์•ฝ ๊ฐœ๋ฐœ ์ „๋žต ์ˆ˜๋ฆฝ
- ๊ฒฝ์ œ ์ •์ฑ… ์˜ํ–ฅ ๋ถ„์„

### 2. ์ฐฝ์˜์  ๋ฌธ์ œ ํ•ด๊ฒฐ
- ๋น„์ฆˆ๋‹ˆ์Šค ํ˜์‹  ์ „๋žต
- ๊ธฐ์ˆ  ์œตํ•ฉ ์•„์ด๋””์–ด
- ๋ฏธ๋ž˜ ์‹œ๋‚˜๋ฆฌ์˜ค ๊ธฐํš

### 3. ํ•™์ˆ ์  ๋ถ„์„
- ๋‹คํ•™์ œ์  ์—ฐ๊ตฌ ์ข…ํ•ฉ
- ์ด๋ก ๊ณผ ์‹ค๋ฌด์˜ ํ†ตํ•ฉ
- ๋น„ํŒ์  ๋ฌธํ—Œ ๊ฒ€ํ† 

## ๐Ÿ”ฎ ๋ฏธ๋ž˜ ๋กœ๋“œ๋งต

### Phase 1: Current (AGI Level 1)
- โœ… Self-orchestration
- โœ… Modular architecture
- โœ… Basic tool use

### Phase 2: Enhancement
- ๐Ÿ”„ Multi-modal processing
- ๐Ÿ”„ Enhanced memory systems
- ๐Ÿ”„ Advanced planning algorithms

### Phase 3: AGI Level 2
- ๐Ÿ“… True autonomy
- ๐Ÿ“… Cross-domain transfer
- ๐Ÿ“… Emergent capabilities

## ๐Ÿค ๊ธฐ์—ฌ ๋ฐฉ๋ฒ•

SOMA๋Š” AGI ์‹คํ˜„์„ ์œ„ํ•œ ์˜คํ”ˆ ์—ฐ๊ตฌ ํ”„๋กœ์ ํŠธ์ž…๋‹ˆ๋‹ค.

1. **์—ฐ๊ตฌ ๊ธฐ์—ฌ**: AGI ์ด๋ก  ๋ฐœ์ „
2. **์ฝ”๋“œ ๊ธฐ์—ฌ**: ๊ตฌํ˜„ ๊ฐœ์„ 
3. **์‘์šฉ ์—ฐ๊ตฌ**: ์ƒˆ๋กœ์šด ํ™œ์šฉ ์‚ฌ๋ก€
4. **ํ”ผ๋“œ๋ฐฑ**: ์„ฑ๋Šฅ ํ‰๊ฐ€์™€ ์ œ์•ˆ

## ๐Ÿ“š ์ฐธ๊ณ  ๋ฌธํ—Œ

- 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"

## ๐Ÿ“ ๋ผ์ด์„ ์Šค ๋ฐ ๋…ผ๋ฌธ

๋…ผ๋ฌธ ์ž‘์„ฑ/๋ฐฐํฌ ํ›„ ๋ผ์ด์„ ์Šค ๊ณต๊ฐœ ์˜ˆ์ •

---

### ๐ŸŒŸ ๊ฒฐ๋ก 

**SOMA๋Š” AGI 1๋‹จ๊ณ„์˜ ํ•ต์‹ฌ ๊ตฌํ˜„ ๋ ˆ๋ฒจ(Level 1)๋กœ์„œ, ํ˜„์žฌ ๊ธฐ์ˆ ๋กœ ์‹คํ˜„ ๊ฐ€๋Šฅํ•œ ๊ฐ€์žฅ ๊ตฌ์ฒด์ ์ด๊ณ  ์‹ค์šฉ์ ์ธ AGI ์•„ํ‚คํ…์ฒ˜์ž…๋‹ˆ๋‹ค.**

๋‹จ์ผ LLM์ด ๊ฐ€์ƒ์˜ ํŒ€์œผ๋กœ ๋ถ„ํ™”ํ•˜์—ฌ, ๋‚ด๋ถ€์ ์œผ๋กœ ๋‹ค์–‘ํ•œ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๋ฉฐ ํ•จ๊ป˜ ์‚ฌ๊ณ ํ•˜๊ณ  ์„ค๊ณ„ํ•˜๊ณ  ์‹คํ–‰ํ•˜๋Š” '์ž๊ธฐ ์ง€ํœ˜ํ˜• ๋‹ค์ค‘ํ™” ์ง€๋Šฅ ๊ตฌ์กฐ'๋ฅผ ํ†ตํ•ด, ์šฐ๋ฆฌ๋Š” AGI๋กœ ๊ฐ€๋Š” ์ฒซ ๋ฒˆ์งธ ๋‹จ๊ณ„๋ฅผ ์„ฑ๊ณต์ ์œผ๋กœ ๊ตฌํ˜„ํ–ˆ์Šต๋‹ˆ๋‹ค.

*"The future of AI is not a single superintelligence, but a symphony of specialized modules working in perfect harmony."*

---

**SOMA** - *Self-Orchestrating Modular Architect*  
*AGI์˜ ์‹œ์ž‘, ์ง€๋Šฅ์˜ ๋ฏธ๋ž˜*