Research-Stack/5-Applications/scripts/ask_swarm_unique_concept_design.py

#!/usr/bin/env python3
"""
Swarm Query: Design Completely Unique Distributed State Propagation Concept

Query the swarm system to design a completely novel distributed state propagation mechanism
that avoids patent issues with gossip protocols while achieving the same goals for the
Gossip-DAG-QR-Go Tile Flipping protocol (MATH_MODEL_MAP 0.4.10).
"""

import json
import uuid
from pathlib import Path
from datetime import datetime

def generate_unique_concept_request():
    """Generate swarm request for unique distributed state propagation concept design."""
    
    request = {
        "request_id": f"swarm_unique_concept_{uuid.uuid4().hex[:12]}",
        "timestamp": datetime.now().isoformat(),
        "query_type": "novel_concept_design",
        "scope": "distributed_state_propagation",
        "priority": "P0_CRITICAL",
        "description": "Ask the swarm to design a completely unique distributed state propagation mechanism to avoid gossip patent issues",
        
        "context": {
            "insight": "QR code modules act as Go tiles that flip based on state propagation",
            "formalism_id": "0.4.10 Gossip_DAG_QR_Go_Tile_Flipping",
            "current_approach": "Gossip protocol with consensus, conflict resolution, fault tolerance",
            "concern": "Potential patent issues with gossip protocols",
            "requirement": "Completely unique concept that achieves same goals without patent risk"
        },
        
        "goals_to_achieve": {
            "distributed_state_propagation": "Propagate state changes across distributed nodes",
            "consensus_mechanism": "Achieve agreement on state changes across nodes",
            "conflict_resolution": "Handle conflicting state changes",
            "fault_tolerance": "Tolerate node failures and network partitions",
            "scalability": "Scale to large numbers of nodes and large state spaces",
            "efficiency": "Efficient in terms of latency, throughput, and resource usage"
        },
        
        "gossip_patent_concerns": {
            "patent_landscape": "Gossip protocols have extensive patent coverage",
            "risk_areas": [
                "Message forwarding patterns",
                "Random peer selection",
                "Periodic anti-entropy",
                "Rumor mongering",
                "Specific gossip algorithms"
            ],
            "avoidance_strategy": "Design fundamentally different approach"
        },
        
        "design_requirements": {
            "novelty": {
                "description": "Must be completely novel, not derivative of existing protocols",
                "questions": [
                    "What is the core novel mechanism?",
                    "How does it differ fundamentally from gossip?",
                    "What is the mathematical foundation?",
                    "What is the physical/analog inspiration?"
                ]
            },
            
            "patent_safety": {
                "description": "Must avoid patent infringement",
                "questions": [
                    "What patents might be relevant?",
                    "How does this design avoid those patents?",
                    "What prior art is this based on?",
                    "Is this design patentable itself?"
                ]
            },
            
            "mathematical_rigor": {
                "description": "Must have solid mathematical foundation",
                "questions": [
                    "What are the core mathematical operations?",
                    "What are the invariants?",
                    "What are the convergence properties?",
                    "What are the complexity bounds?"
                ]
            },
            
            "integration_with_qr_tiles": {
                "description": "Must integrate with QR code tile flipping concept",
                "questions": [
                    "How does state propagation trigger tile flips?",
                    "How does QR grid state encode distributed state?",
                    "How do Go rules (liberty, capture, ko) apply?",
                    "How does DAG encoding work with this mechanism?"
                ]
            },
            
            "feasibility": {
                "description": "Must be implementable in Lean with classical encoding",
                "questions": [
                    "Can this be implemented in Lean?",
                    "What are the data structures needed?",
                    "What are the algorithms needed?",
                    "What are the performance characteristics?"
                ]
            }
        },
        
        "inspiration_sources": {
            "physics": [
                "Wave propagation",
                "Quantum entanglement (classical analog)",
                "Field theory",
                "Resonance phenomena",
                "Diffusion processes",
                "Crystal growth",
                "Phase transitions"
            ],
            "biology": [
                "Neural signaling",
                "Swarm intelligence",
                "Morphogenesis",
                "Gene regulatory networks",
                "Immune system signaling",
                "Fungal mycelium networks"
            ],
            "mathematics": [
                "Cellular automata",
                "Dynamical systems",
                "Graph theory (novel variants)",
                "Information theory",
                "Game theory (novel variants)",
                "Topology"
            ],
            "computer_science": [
                "Distributed algorithms (novel variants)",
                "Synchronization primitives",
                "Consensus algorithms (novel variants)",
                "Self-organizing systems",
                "Emergent computation"
            ]
        },
        
        "design_constraints": {
            "classical_encoding": "Must use classical encoding (no qutrits per swarm recommendation)",
            "lean_implementation": "Must be implementable in Lean 4",
            "qr_integration": "Must integrate with QR code tile flipping",
            "dag_encoding": "Must support DAG state encoding",
            "go_rules": "Must support Go rules (liberty, capture, ko)",
            "fault_tolerance": "Must tolerate node failures and network partitions"
        },
        
        "expected_deliverables": {
            "concept_name": "Unique name for the novel mechanism",
            "core_mechanism": "Detailed description of core mechanism",
            "mathematical_foundation": "Mathematical equations and invariants",
            "algorithm_specification": "Detailed algorithm specification",
            "patent_analysis": "Patent landscape analysis and avoidance strategy",
            "integration_design": "Integration with QR tile flipping",
            "implementation_plan": "Lean implementation plan",
            "performance_analysis": "Expected performance characteristics"
        },
        
        "depth_requirement": {
            "instruction": "The swarm should really work on this - go deep, be creative, think fundamentally",
            "expectations": [
                "Explore multiple novel approaches before converging",
                "Provide mathematical rigor",
                "Consider physical/analog inspirations",
                "Think about patent implications",
                "Design something truly unique",
                "Provide detailed specifications",
                "Consider edge cases and failure modes"
            ]
        },
        
        "swarm_response_format": {
            "concept_overview": "High-level concept description",
            "core_mechanism": "Detailed core mechanism",
            "mathematical_foundation": "Equations and invariants",
            "novelty_analysis": "How this differs from existing approaches",
            "patent_analysis": "Patent landscape and avoidance",
            "algorithm_specification": "Detailed algorithm",
            "integration_design": "QR tile flipping integration",
            "implementation_roadmap": "Lean implementation plan",
            "performance_estimates": "Expected performance",
            "concerns_or_caveats": "Any concerns or limitations"
        }
    }
    
    return request

def save_request(request, output_path):
    """Save swarm request to file."""
    Path(output_path).parent.mkdir(parents=True, exist_ok=True)
    
    with open(output_path, 'w') as f:
        json.dump(request, f, indent=2)
    
    return output_path

def main():
    """Generate and save unique concept design request."""
    print("=" * 70)
    print("Swarm Query: Design Completely Unique Distributed State Propagation Concept")
    print("=" * 70)
    
    # Generate request
    request = generate_unique_concept_request()
    
    # Save request
    output_path = "shared-data/data/swarm_requests/swarm_unique_concept.json"
    saved_path = save_request(request, output_path)
    
    print(f"\nRequest generated and saved to: {saved_path}")
    print(f"Request ID: {request['request_id']}")
    print(f"Priority: {request['priority']}")
    print(f"Formalism ID: {request['context']['formalism_id']}")
    
    print("\nContext:")
    print(f"  Insight: {request['context']['insight']}")
    print(f"  Current Approach: {request['context']['current_approach']}")
    print(f"  Concern: {request['context']['concern']}")
    print(f"  Requirement: {request['context']['requirement']}")
    
    print("\n" + "=" * 70)
    print("Goals to Achieve")
    print("=" * 70)
    for goal, description in request['goals_to_achieve'].items():
        print(f"  {goal}: {description}")
    
    print("\n" + "=" * 70)
    print("Gossip Patent Concerns")
    print("=" * 70)
    print(f"  Patent Landscape: {request['gossip_patent_concerns']['patent_landscape']}")
    print(f"  Risk Areas: {len(request['gossip_patent_concerns']['risk_areas'])}")
    print(f"  Avoidance Strategy: {request['gossip_patent_concerns']['avoidance_strategy']}")
    
    print("\n" + "=" * 70)
    print("Design Requirements")
    print("=" * 70)
    for requirement, info in request['design_requirements'].items():
        print(f"  {requirement}: {len(info['questions'])} questions")
    
    print("\n" + "=" * 70)
    print("Inspiration Sources")
    print("=" * 70)
    for category, sources in request['inspiration_sources'].items():
        print(f"  {category}: {len(sources)} sources")
    
    print("\n" + "=" * 70)
    print("Design Constraints")
    print("=" * 70)
    for constraint, requirement in request['design_constraints'].items():
        print(f"  {constraint}: {requirement}")
    
    print("\n" + "=" * 70)
    print("Expected Deliverables")
    print("=" * 70)
    for deliverable in request['expected_deliverables'].keys():
        print(f"  - {deliverable}")
    
    print("\n" + "=" * 70)
    print("Depth Requirement")
    print("=" * 70)
    print(f"  Instruction: {request['depth_requirement']['instruction']}")
    print(f"  Expectations: {len(request['depth_requirement']['expectations'])}")
    for expectation in request['depth_requirement']['expectations']:
        print(f"    - {expectation}")
    
    print("\n✅ Swarm query generation completed successfully")
    print("\nThis query asks the swarm to:")
    print("  - Design a completely novel distributed state propagation mechanism")
    print("  - Avoid patent issues with gossip protocols")
    print("  - Achieve the same goals (consensus, conflict resolution, fault tolerance)")
    print("  - Integrate with QR code tile flipping")
    print("  - Use classical encoding (not qutrits)")
    print("  - Be implementable in Lean 4")
    print("\nInstruction to swarm: REALLY WORK ON IT - go deep, be creative, think fundamentally")

if __name__ == "__main__":
    main()