python helical_demo.py

import hashlib
from typing import List, Optional
from dataclasses import dataclass

# --- CONFIGURATION ---
BLOCK_CAPACITY = 100  # "Knapsack" size (e.g., Gas Limit or Byte Limit)
STAKE_MULTIPLIER = 1.5  # Alpha (Recursion penalty)

@dataclass
class Transaction:
    sender: str
    fee: float
    stake: float
    bytecode: str  # Simulated bytecode
    
    # These would be calculated by the parser, but we simulate them for the demo
    simulated_complexity: int 
    simulated_depth: int

    @property
    def tx_hash(self) -> str:
        """Deterministically generate hash for tie-breaking (Claim 3)"""
        raw = f"{self.sender}{self.fee}{self.bytecode}"
        return hashlib.sha256(raw.encode()).hexdigest()

class ExecutiveControlModule:
    """
    Implements Claim 1: Stateless Structural Validation
    """
    def validate_structure(self, tx: Transaction) -> bool:
        # 1. Structural Parser (AST) - Simulated here
        # In a real compiler, this would traverse the AST node by node.
        chi = tx.simulated_complexity  # Cyclomatic Complexity
        delta = tx.simulated_depth     # Recursion Depth
        
        # 2. Calculate Total Structural Weight (The Formula)
        # Weight = Base_Cost * (Depth ^ Alpha)
        structural_weight = chi * (delta ** STAKE_MULTIPLIER)
        
        # 3. The Complexity Stake Check (Claim 1)
        # "Allocate memory only after score satisfies threshold"
        required_stake = structural_weight  # 1:1 ratio for simplicity
        
        print(f"[*] Analyzing TX from {tx.sender}...")
        print(f"    - Structure: Chi={chi}, Delta={delta} -> Weight={structural_weight:.2f}")
        print(f"    - Stake: {tx.stake} (Required: {required_stake:.2f})")
        
        if tx.stake >= required_stake:
            print("    -> PASSED: Stake sufficient. Proceeding to JIT.")
            return True
        else:
            print("    -> REJECTED: Atomic Rejection (Insufficient Stake).")
            return False

class SpeculativeJIT:
    """
    Implements Claim 7: Pre-compile strictly AFTER validation
    """
    def compile(self, tx: Transaction):
        # Only runs if ECM returns True.
        # Simulates compiling bytecode to native machine code.
        print(f"    -> JIT: Compiling {tx.sender}'s code to native x86 during idle time...")
        return True

class EfficiencySequencer:
    """
    Implements Claim 3: Knapsack Optimization based on Efficiency Density
    """
    def __init__(self):
        self.mempool: List[Transaction] = []

    def add_to_pool(self, tx: Transaction):
        # Calculate Efficiency Density (D) = Fee / Complexity
        # Note: We use the 'structural_weight' as the denominator
        weight = tx.simulated_complexity * (tx.simulated_depth ** STAKE_MULTIPLIER)
        density = tx.fee / max(weight, 1) # Avoid div by zero
        
        # Attach density to the object for sorting (in a real system, this is a struct field)
        tx.density = density
        self.mempool.append(tx)

    def build_block(self) -> List[Transaction]:
        print("\n[!] SEQUENCER STARTING: Solving Knapsack Problem...")
        
        # 1. Sort by Efficiency Density (Descending)
        # Tie-Breaker: Simplicity (Lower Complexity), then Hash (Lexicographical)
        self.mempool.sort(key=lambda x: (-x.density, x.simulated_complexity, x.tx_hash))
        
        block = []
        current_load = 0
        
        for tx in self.mempool:
            weight = tx.simulated_complexity # Simplified load metric
            
            if current_load + weight <= BLOCK_CAPACITY:
                block.append(tx)
                current_load += weight
                print(f"    + Added {tx.sender} (Density: {tx.density:.2f}) | Load: {current_load}/{BLOCK_CAPACITY}")
            else:
                print(f"    - Skipped {tx.sender} (Density: {tx.density:.2f}) | Fits? No.")
                
        return block

# --- SIMULATION ---
if __name__ == "__main__":
    ecm = ExecutiveControlModule()
    jit = SpeculativeJIT()
    sequencer = EfficiencySequencer()
    
    # 1. The "Rich Hacker" (Agent A)
    # High Fee ($200), Massive Complexity (100), Deep Recursion (bad structure)
    hacker_tx = Transaction(
        sender="Agent A (Hacker)", 
        fee=200.0, 
        stake=500.0, # He tries to bond it
        bytecode="while(true){...}",
        simulated_complexity=100, 
        simulated_depth=5 # Deep nesting
    )
    
    # 2. The "Smart Optimizer" (Agent B)
    # Low Fee ($50), Low Complexity (10), Flat structure
    optimizer_tx = Transaction(
        sender="Agent B (Optimizer)", 
        fee=50.0, 
        stake=20.0, 
        bytecode="return x+y;",
        simulated_complexity=10, 
        simulated_depth=1
    )
    
    # 3. An "Under-Collateralized" Spammer (Agent C)
    # Tries to spam without paying the bond
    spammer_tx = Transaction(
        sender="Agent C (Broke Spammer)", 
        fee=10.0, 
        stake=1.0, # Too low!
        bytecode="loop...loop",
        simulated_complexity=50, 
        simulated_depth=2
    )

    print("--- STEP 1: ECM INPUT VALIDATION ---")
    valid_txs = []
    for tx in [hacker_tx, optimizer_tx, spammer_tx]:
        if ecm.validate_structure(tx):
            # Claim 7: JIT only runs if validation passes
            jit.compile(tx)
            sequencer.add_to_pool(tx)

    print("--- STEP 2: BLOCK CONSTRUCTION ---")
    final_block = sequencer.build_block()
    
    print("\n--- FINAL RESULT ---")
    print(f"Block contains {len(final_block)} transactions.")
    print("Notice: Agent B (Density 5.0) got in.")
    print("Notice: Agent A (Density ~0.18) might get in if space permits, but is deprioritized.")
    print("Notice: Agent C was rejected at the door.")