Artifact Reason

Multi-agent reasoning system for scientific discovery, autonomous theorem generation, symbolic modeling, and formal proof verification.

Overview

Artifact Reason is a production-grade reasoning system that combines multi-agent architecture, symbolic regression, formal proof verification, and autonomous research capabilities to discover, validate, and publish novel mathematical theorems.

Core Capabilities:

• Multi-agent hypothesis generation and validation
• Symbolic equation discovery from data patterns
• Formal proof verification via Lean 4 interface
• Autonomous theorem generation and research iteration
• Academic paper generation in LaTeX/PDF format
• Distributed task processing for horizontal scaling

Quick Start

Prerequisites

• Python 3.12 or higher
• pip package manager
• Optional: pdflatex (for PDF generation)
• Optional: Redis (for distributed workers)

Installation

# Clone repository
git clone https://github.com/Artifact-Virtual/REASON.git
cd REASON

# Install dependencies
pip install -r requirements.txt

# Verify installation
python run_system.py

Basic Usage

Run Complete Analysis:

python run_system.py

Start API Server:

uvicorn main:app --reload

Launch Interactive Frontend:

streamlit run frontend/app.py

Run Autonomous Research:

python run_research.py

System Architecture

┌─────────────────┐
│   Input Data    │
└────────┬────────┘
         │
         ▼
┌─────────────────────────────────────────┐
│     Multi-Agent Reasoning System        │
│  ┌──────────┐  ┌──────────┐  ┌────────┐│
│  │Hypothesis│  │Validator │  │  Meta  ││
│  │Generator │  │  Agent   │  │Reasoner││
│  └──────────┘  └──────────┘  └────────┘│
└────────┬────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────┐
│       Symbolic Regression Engine        │
│  (PySR with multiple strategies)        │
└────────┬────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────┐
│      Theorem Generation & Iteration     │
│  ┌──────────┐  ┌──────────┐  ┌────────┐│
│  │ Generate │→ │ Validate │→ │ Refine ││
│  │ Theorems │  │ w/ Lean  │  │  Loop  ││
│  └──────────┘  └──────────┘  └────────┘│
└────────┬────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────┐
│        Academic Paper Generation        │
│  (LaTeX + PDF with diagrams)            │
└────────┬────────────────────────────────┘
         │
         ▼
┌─────────────────┐
│  Final Report   │
└─────────────────┘

Key Components

Multi-Agent System

• Hypothesis Generator: Creates diverse mathematical hypotheses
• Validator: Cross-validates hypotheses for consistency
• Meta-Reasoner: Analyzes reasoning quality and strategies
• Hallucination Detector: Identifies and flags unreliable outputs
• Consensus Builder: Aggregates agent opinions with confidence metrics

Autonomous Research System

• Theorem Generator: Generates novel conjectures from patterns
• Iteration Engine: Validates theorems through multi-iteration cycles
• Paper Generator: Produces publication-ready LaTeX/PDF documents
• Research Orchestrator: Manages end-to-end research workflows
• Worker System: Distributes tasks across multiple workers (Celery + Redis)

Specialized Math Modules

• Calculus Engine: Derivatives, integrals, Taylor series, gradients
• Optimization Engine: argmax, argmin, softmax, gradient descent
• Physics Engine: Kinematic equations, conservation laws

Proof System

• Lean 4 interface for formal verification
• Simulation mode fallback when Lean unavailable
• Automated proof generation and validation

API Reference

See docs/API.md for complete API documentation.

Quick Example

POST /reason

import httpx

response = httpx.post("http://localhost:8000/reason", json={
    "data": [1, 4, 9, 16, 25, 36],
    "context": "Perfect squares sequence"
})

result = response.json()

Python Integration

from core.reasoning_orchestrator import EnhancedReasoningOrchestrator
import asyncio

async def analyze_pattern():
    orchestrator = EnhancedReasoningOrchestrator()
    results = await orchestrator.orchestrate_reasoning(
        data=[1, 1, 2, 3, 5, 8],
        context="Fibonacci sequence"
    )
    return results

results = asyncio.run(analyze_pattern())

Project Structure

See docs/ARCHITECTURE.md for detailed architecture documentation.

Testing

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=core --cov=llm --cov=proofs

Test Coverage: 23/23 tests passing (100%)

Performance Metrics

• Conjecture Generation: ~50ms per pattern
• Iteration Cycle: ~100ms per iteration
• Paper Generation: ~2-3ms per LaTeX document (without PDF compilation)
• End-to-End Research: <1 second per conjecture

Documentation

• CHANGELOG.md: Version history and release notes
• docs/ARCHITECTURE.md: System design and architecture
• docs/API.md: Complete API reference
• docs/DEPLOYMENT.md: Deployment guide
• docs/USER_GUIDE.md: End-user documentation
• IMPLEMENTATION_SUMMARY.md: Technical implementation details

Roadmap

See ROADMAP.md for development plans.

License

Proprietary. See LICENSE.md for details.

Citation

@software{artifact_reason2026,
  title={Artifact Reason: Multi-Agent Reasoning System for Scientific Discovery},
  author={Artifact Virtual},
  year={2026},
  version={1.1.0},
  url={https://github.com/Artifact-Virtual/REASON}
}

Author

Artifact Virtual — Building production systems, not demos.

---

Version: 1.1.0
Status: Production Ready
Python: 3.12+
License: Proprietary