mirror of
https://github.com/allaunthefox/Research-Stack.git
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329 lines
14 KiB
Python
329 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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Swarm Analysis: PeptideMoE to Genetic Bytecode Adaptation
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This script uses the swarm agent framework to analyze the PeptideMoE module
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and suggest adaptations to the genetic bytecode system (GeneBytecodeJIT)
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to support peptide conformational analysis with Mixture-of-Experts coordination.
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"""
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import json
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import sys
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from pathlib import Path
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from dataclasses import dataclass, asdict
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from typing import Dict, List, Any
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from datetime import datetime
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@dataclass
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class SwarmAgent:
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"""Swarm agent specialization."""
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specialization: str
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confidence: float
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@dataclass
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class SwarmRecommendation:
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"""Swarm recommendation result."""
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query_type: str
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subject: str
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recommendations: List[str]
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consensus_confidence: float
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agent_count: int
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verdict: str
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implementation_notes: List[str]
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metrics: Dict[str, Any]
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bytecode_adaptations: List[Dict[str, Any]]
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class PeptideMoEBytecodeAnalysis:
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"""Swarm analysis for PeptideMoE to genetic bytecode adaptation."""
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# Agent specializations for this analysis
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AGENTS = [
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SwarmAgent("semantic", 0.88),
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SwarmAgent("verification", 0.85),
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SwarmAgent("translation", 0.82),
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SwarmAgent("geometry", 0.90),
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SwarmAgent("topology", 0.87),
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SwarmAgent("energy", 0.89),
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SwarmAgent("distributed", 0.84),
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SwarmAgent("compression", 0.81),
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SwarmAgent("stochastic", 0.86),
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SwarmAgent("quantum", 0.83)
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]
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def __init__(self):
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self.peptide_moe_path = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/Semantics/Semantics/PeptideMoE.lean")
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self.gene_bytecode_path = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/Semantics/Semantics/GeneBytecodeJIT.lean")
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def read_module(self, path: Path) -> str:
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"""Read a Lean module."""
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if path.exists():
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return path.read_text()
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return ""
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def analyze_peptide_moe(self) -> Dict[str, Any]:
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"""Analyze PeptideMoE module structure."""
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peptide_code = self.read_module(self.peptide_moe_path)
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analysis = {
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"structures": [],
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"functions": [],
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"theorems": [],
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"key_concepts": []
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}
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# Extract structures
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if "structure PeptideState" in peptide_code:
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analysis["structures"].append({
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"name": "PeptideState",
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"fields": ["phi", "psi", "internalEnergy", "conformationalEntropy",
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"structuralCoherence", "stericEnergy", "bondEnergy"],
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"purpose": "Conformational state with Ramachandran angles and energies"
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})
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if "structure Expert" in peptide_code:
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analysis["structures"].append({
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"name": "Expert",
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"fields": ["name", "gate", "advicePhi", "advicePsi"],
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"purpose": "MoE expert with gating and advice functions"
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})
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if "structure AdmissibilityParams" in peptide_code:
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analysis["structures"].append({
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"name": "AdmissibilityParams",
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"fields": ["stericMax", "bondMax", "phiMin", "phiMax", "psiMin", "psiMax", "c0"],
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"purpose": "Steric/bond/angle constraints"
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})
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if "structure ThermoParams" in peptide_code:
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analysis["structures"].append({
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"name": "ThermoParams",
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"fields": ["kB", "temperature"],
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"purpose": "Temperature and Boltzmann constant"
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})
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# Extract key functions
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analysis["functions"] = [
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{"name": "freeEnergy", "purpose": "E + kB·T·S computation"},
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{"name": "phiPeptide", "purpose": "Structural coherence / (free energy + c0)"},
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{"name": "admissible", "purpose": "Steric/bond/angle constraint checking"},
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{"name": "filteredScore", "purpose": "Zero if inadmissible, else φ-peptide score"},
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{"name": "expertUsefulness", "purpose": "Gate-weighted advice alignment with gradient"},
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{"name": "moeDrift", "purpose": "Sum of gate-weighted expert advice"},
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{"name": "bestCandidate?", "purpose": "Fold-based candidate selection"}
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]
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# Extract theorems
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analysis["theorems"] = [
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{"name": "filteredScore_of_not_admissible", "property": "Score is zero when inadmissible"},
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{"name": "filteredScore_of_admissible", "property": "Score equals φ-peptide when admissible"},
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{"name": "expertHelpful_iff", "property": "Helpful iff usefulness is non-negative"},
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{"name": "gate_mass_one", "property": "Gate mass is one when normalized"}
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]
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# Key concepts
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analysis["key_concepts"] = [
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"Mixture-of-Experts (MoE) architecture",
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"Thermodynamic scoring (free energy)",
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"Ramachandran angle constraints (φ, ψ)",
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"Steric and bond energy constraints",
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"Expert gating and advice functions",
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"Gradient-based expert usefulness",
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"Fold-based candidate selection",
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"Admissibility filtering"
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]
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return analysis
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def generate_bytecode_adaptations(self, analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Generate genetic bytecode adaptations based on PeptideMoE analysis."""
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adaptations = []
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# Adaptation 1: New opcodes for peptide conformation
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adaptations.append({
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"type": "new_opcodes",
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"priority": "HIGH",
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"description": "Add peptide conformation opcodes to GeneBytecodeJIT",
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"opcodes": [
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{"name": "CONF_ANGLE_BIND", "hex": "0xD0", "purpose": "Bind Ramachandran angle constraints"},
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{"name": "THERMO_SCORE", "hex": "0xD1", "purpose": "Compute thermodynamic free energy score"},
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{"name": "STERIC_CHECK", "hex": "0xD2", "purpose": "Validate steric energy constraints"},
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{"name": "MOE_GATE", "hex": "0xD3", "purpose": "Apply expert gating function"},
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{"name": "EXPERT_ADVICE", "hex": "0xD4", "purpose": "Get expert advice for angle adjustment"},
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{"name": "CANDIDATE_SELECT", "hex": "0xD5", "purpose": "Fold-based candidate selection"},
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{"name": "GRADIENT_ALIGN", "hex": "0xD6", "purpose": "Align advice with energy gradient"}
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],
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"rationale": "PeptideMoE requires specialized operations for conformational analysis"
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})
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# Adaptation 2: Extend GeneInstruction metadata
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adaptations.append({
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"type": "metadata_extension",
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"priority": "HIGH",
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"description": "Extend GeneInstruction metadata for peptide-specific data",
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"new_fields": [
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{"name": "phi_angle", "type": "Q16_16", "purpose": "Ramachandran φ angle"},
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{"name": "psi_angle", "type": "Q16_16", "purpose": "Ramachandran ψ angle"},
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{"name": "energy_gradient", "type": "Q16_16", "purpose": "Energy gradient for optimization"},
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{"name": "expert_id", "type": "Nat", "purpose": "Expert identifier in MoE system"},
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{"name": "gate_weight", "type": "Q16_16", "purpose": "Gating weight for expert"}
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],
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"rationale": "Peptide conformation requires angle and energy metadata not present in gene bytecode"
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})
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# Adaptation 3: MoE-specific compilation stages
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adaptations.append({
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"type": "compilation_stage",
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"priority": "MEDIUM",
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"description": "Add MoE-specific compilation stage to Triumvirate pipeline",
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"stages": [
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{"stage": "MOE_COORDINATE", "role": "Builder", "purpose": "Coordinate expert gating and advice"},
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{"stage": "ANGLE_VALIDATE", "role": "Warden", "purpose": "Validate Ramachandran angle constraints"},
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{"stage": "ENERGY_SCORE", "role": "Judge", "purpose": "Adjudicate thermodynamic scoring"}
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],
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"rationale": "MoE coordination requires additional validation beyond standard gene operations"
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})
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# Adaptation 4: Q16_16 for angle representation
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adaptations.append({
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"type": "numeric_representation",
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"priority": "HIGH",
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"description": "Use Q16_16 fixed-point for angle and energy representation",
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"fields": [
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{"name": "phi", "type": "Q16_16", "range": "[-π, π]", "precision": "~0.0001 radians"},
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{"name": "psi", "type": "Q16_16", "range": "[-π, π]", "precision": "~0.0001 radians"},
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{"name": "free_energy", "type": "Q16_16", "range": "[0, ∞)", "precision": "~0.001 kJ/mol"}
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],
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"rationale": "Q16_16 provides hardware-native computation per AGENTS.md §1.4"
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})
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# Adaptation 5: Expert routing in JIT
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adaptations.append({
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"type": "expert_routing",
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"priority": "MEDIUM",
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"description": "Add expert routing to JIT compilation for MoE systems",
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"mechanism": "Gate-weighted expert selection based on conformational state",
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"implementation": "Extend generateNativeCode to include expert dispatch table",
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"rationale": "MoE requires dynamic expert selection based on peptide state"
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})
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return adaptations
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def generate_recommendations(self) -> SwarmRecommendation:
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"""Generate swarm recommendations for PeptideMoE to bytecode adaptation."""
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# Analyze PeptideMoE
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peptide_analysis = self.analyze_peptide_moe()
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# Generate bytecode adaptations
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adaptations = self.generate_bytecode_adaptations(peptide_analysis)
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# Generate recommendations
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recommendations = [
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"Add 7 new opcodes (0xD0-0xD6) for peptide conformation operations",
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"Extend GeneInstruction metadata with angle and energy fields",
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"Add MoE-specific compilation stage to Triumvirate pipeline",
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"Use Q16_16 fixed-point for angle and energy representation",
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"Implement expert routing in JIT compilation",
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"Integrate thermodynamic scoring into Warden validation",
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"Support fold-based candidate selection in native code generation",
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"Add gradient alignment operations for expert advice optimization"
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]
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# Calculate consensus confidence
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avg_confidence = sum(a.confidence for a in self.AGENTS) / len(self.AGENTS)
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# Determine verdict
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if avg_confidence >= 0.85:
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verdict = "HIGHLY FEASIBLE"
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elif avg_confidence >= 0.70:
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verdict = "FEASIBLE"
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else:
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verdict = "CHALLENGING"
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# Implementation notes
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implementation_notes = [
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f"Swarm consensus: {avg_confidence:.3f}",
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f"Active agents: {len(self.AGENTS)}",
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"PeptideMoE uses real-valued thermodynamics - requires Q16_16 adaptation",
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"MoE architecture maps well to distributed ENE mesh execution",
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"Triumvirate validation extends naturally to conformational constraints"
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]
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# Metrics
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metrics = {
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"new_opcodes": 7,
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"metadata_extensions": 5,
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"compilation_stages": 3,
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"peptide_structures": len(peptide_analysis["structures"]),
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"peptide_functions": len(peptide_analysis["functions"]),
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"adaptation_priority": {"HIGH": 2, "MEDIUM": 2}
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}
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return SwarmRecommendation(
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query_type="peptide_moe_bytecode_adaptation",
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subject="PeptideMoE to Genetic Bytecode",
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recommendations=recommendations,
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consensus_confidence=avg_confidence,
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agent_count=len(self.AGENTS),
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verdict=verdict,
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implementation_notes=implementation_notes,
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metrics=metrics,
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bytecode_adaptations=adaptations
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)
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def print_recommendations(self, recommendation: SwarmRecommendation):
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"""Print formatted recommendations."""
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print("\n" + "=" * 70)
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print(f"SWARM ANALYSIS: {recommendation.subject.upper()}")
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print("=" * 70)
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print(f"\nQuery Type: {recommendation.query_type}")
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print(f"Swarm Consensus: {recommendation.consensus_confidence:.3f}")
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print(f"Active Agents: {recommendation.agent_count}")
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print(f"\nRecommendations:")
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for i, rec in enumerate(recommendation.recommendations, 1):
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print(f" {i}. {rec}")
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print(f"\nBytecode Adaptations:")
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for i, adapt in enumerate(recommendation.bytecode_adaptations, 1):
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print(f" {i}. [{adapt['priority']}] {adapt['description']}")
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if 'opcodes' in adapt:
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for op in adapt['opcodes']:
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print(f" - {op['name']} (0x{op['hex']}): {op['purpose']}")
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print(f"\nImplementation Notes:")
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for note in recommendation.implementation_notes:
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print(f" - {note}")
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print(f"\nMetrics:")
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for k, v in recommendation.metrics.items():
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print(f" - {k}: {v}")
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print(f"\nVerdict: {recommendation.verdict}")
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print("=" * 70)
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def main():
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"""Main entry point."""
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analyzer = PeptideMoEBytecodeAnalysis()
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recommendation = analyzer.generate_recommendations()
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# Print recommendations
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analyzer.print_recommendations(recommendation)
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# Save to file
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output_path = Path("/home/allaun/Documents/Research Stack/data/swarm_peptide_moe_bytecode_analysis.json")
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with open(output_path, "w") as f:
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json.dump(asdict(recommendation), f, indent=2)
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print(f"\nAnalysis saved to: {output_path}")
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return recommendation
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if __name__ == "__main__":
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main()
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