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564 lines
24 KiB
Python
564 lines
24 KiB
Python
#!/usr/bin/env python3
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# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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"""Domain Crossbreed Swarm — Deterministic cross-domain invariant generator.
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Implements the DOMAIN_CROSSBREED_SWARM_PROTOCOL by pairing experts from the
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EXHAUSTIVE_DOMAIN_EXPERT_LIST, projecting their constraint matrices into a
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shared 7-dimensional invariant space, and deriving novel solutions at the
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intersection boundary.
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Architecture:
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┌───────────────────────────────────────────────────────────┐
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│ CROSSBREED ENGINE │
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├─────────────┬─────────────┬─────────────┬─────────────┤
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│ EXPERT A │ EXPERT B │ PROJECTOR │ INTEGRATOR │
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└─────────────┴─────────────┴─────────────┴─────────────┘
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Usage:
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python 5-Applications/tools-5-Applications/scripts/domain_crossbreed_swarm.py --pairs 5 --output-dir shared-data/data/swarm
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"""
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from __future__ import annotations
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import argparse
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import hashlib
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import itertools
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import json
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import logging
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import math
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import os
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import random
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import re
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import sys
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import time
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from dataclasses import asdict, dataclass, field
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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# Attempt to import the local LLM client; fallback to pure-deterministic mode.
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try:
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from local_llm_client import LocalLLMClient
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HAS_LLM_CLIENT = True
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except Exception:
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HAS_LLM_CLIENT = False
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s | %(levelname)-8s | %(message)s",
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)
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logger = logging.getLogger("crossbreed_swarm")
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# ── Constants ────────────────────────────────────────────────────────────────
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DOCS_ROOT = Path(__file__).resolve().parents[2] / "docs"
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EXPERT_LIST_PATH = DOCS_ROOT / "audits" / "EXHAUSTIVE_DOMAIN_EXPERT_LIST.md"
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PROTOCOL_PATH = DOCS_ROOT / "design" / "DOMAIN_CROSSBREED_SWARM.md"
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DIMENSIONS = ["T", "S", "C", "F", "R", "P", "W"]
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DIMENSION_NAMES = {
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"T": "Time ordering constraints",
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"S": "Spatial boundary conditions",
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"C": "Causality directionality",
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"F": "Failure modes",
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"R": "Resource limits",
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"P": "Progress metric",
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"W": "Work function definition",
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}
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EPSILON_THRESHOLD = 1e-9
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# ── Data Structures ──────────────────────────────────────────────────────────
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@dataclass(frozen=True)
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class DomainExpert:
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emoji: str
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name: str
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category: str
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def __str__(self) -> str:
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return f"{self.emoji} {self.name}"
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@dataclass
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class ConstraintMatrix:
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"""7-dimensional constraint vector for a domain."""
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T: float = 0.0
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S: float = 0.0
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C: float = 0.0
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F: float = 0.0
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R: float = 0.0
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P: float = 0.0
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W: float = 0.0
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def to_vec(self) -> List[float]:
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return [self.T, self.S, self.C, self.F, self.R, self.P, self.W]
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@classmethod
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def from_vec(cls, vec: List[float]) -> "ConstraintMatrix":
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return cls(*vec)
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def hadamard(self, other: "ConstraintMatrix") -> "ConstraintMatrix":
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a, b = self.to_vec(), other.to_vec()
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return self.__class__.from_vec([x * y for x, y in zip(a, b)])
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def distance(self, other: "ConstraintMatrix") -> float:
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a, b = self.to_vec(), other.to_vec()
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return math.sqrt(sum((x - y) ** 2 for x, y in zip(a, b)))
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@dataclass
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class CrossbreedInvariant:
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domain_a: str
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domain_b: str
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invariant_name: str
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invariant_description: str
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constraint_a: Dict[str, float]
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constraint_b: Dict[str, float]
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intersection: Dict[str, float]
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epsilon: float
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verification_status: str
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derivation_method: str
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generation: int = 1
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# ── Markdown Parsing ─────────────────────────────────────────────────────────
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def parse_expert_list(path: Path) -> Tuple[List[DomainExpert], List[DomainExpert]]:
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"""Parse EXHAUSTIVE_DOMAIN_EXPERT_LIST.md into activated and queued experts."""
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text = path.read_text(encoding="utf-8")
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activated: List[DomainExpert] = []
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queued: List[DomainExpert] = []
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current_category = "Unknown"
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# Split into activated and queued sections
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activated_section = re.search(
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r"## ✅ ACTIVATED EXPERTS.*?\n---", text, re.DOTALL
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)
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queued_section = re.search(
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r"## 🚀 NEXT IN QUEUE.*?\n---", text, re.DOTALL
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)
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def _parse_section(section_text: str, target_list: List[DomainExpert]) -> None:
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nonlocal current_category
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cat = "unknown"
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for line in section_text.splitlines():
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cat_match = re.match(r"###\s+(.*)", line)
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if cat_match:
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cat = cat_match.group(1).strip()
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continue
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expert_match = re.match(r"-\s+([\U0001F300-\U0001FAFF\u2600-\u26FF])\s+(.*)", line)
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if expert_match:
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emoji, name = expert_match.group(1), expert_match.group(2).strip()
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target_list.append(DomainExpert(emoji, name, cat))
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if activated_section:
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_parse_section(activated_section.group(0), activated)
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if queued_section:
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_parse_section(queued_section.group(0), queued)
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return activated, queued
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def parse_protocol(path: Path) -> Dict[str, Any]:
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"""Parse DOMAIN_CROSSBREED_SWARM.md for generation rules."""
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text = path.read_text(encoding="utf-8")
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rules: Dict[str, Any] = {"algorithm_steps": [], "existing_combinations": []}
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# Extract algorithm steps
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steps = re.findall(r"### Step \d+:\s+(.*)\n(.*?)(?=### Step|\n---|\n## )", text, re.DOTALL)
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for title, body in steps:
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rules["algorithm_steps"].append({"title": title.strip(), "body": body.strip()})
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# Extract existing combinations table
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table_rows = re.findall(
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r"\|\s*([^|]+)\|\s*([^|]+)\|\s*\*\*(.*?)\*\*\s*-\s*(.*?)\|\s*✅\s*DERIVED\s*\|",
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text,
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)
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for a, b, inv_name, inv_desc in table_rows:
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rules["existing_combinations"].append({
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"domain_a": a.strip(),
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"domain_b": b.strip(),
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"invariant_name": inv_name.strip(),
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"invariant_description": inv_desc.strip(),
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})
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return rules
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# ── Deterministic Constraint Engine ──────────────────────────────────────────
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def deterministic_constraint_hash(name: str) -> List[float]:
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"""Generate a deterministic 7-dimensional constraint vector from a domain name.
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The hash is derived from SHA-256 of the normalized domain name, split into
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7 chunks and mapped to the unit interval. This guarantees that any two
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identical domain names always produce the exact same constraint surface.
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"""
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digest = hashlib.sha256(name.encode("utf-8")).digest()
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chunks = [digest[i : i + 4] for i in range(0, 28, 4)]
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vec = [int.from_bytes(c, "big") / 0xFFFFFFFF for c in chunks]
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return vec
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def build_constraint_matrix(expert: DomainExpert) -> ConstraintMatrix:
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vec = deterministic_constraint_hash(expert.name.lower())
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return ConstraintMatrix.from_vec(vec)
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def intersect_constraints(a: ConstraintMatrix, b: ConstraintMatrix) -> ConstraintMatrix:
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"""Compute the Hadamard product as the intersection surface."""
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return a.hadamard(b)
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def verify_invariant(a: ConstraintMatrix, b: ConstraintMatrix, intersection: ConstraintMatrix) -> Tuple[bool, float]:
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"""Verify that the intersection satisfies both domain constraints within epsilon."""
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# The verification condition is: ||C_D1(I) - C_D2(I)|| < epsilon
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# We interpret C_D(I) as the projection of I onto D's constraint surface.
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# A valid intersection should be close to both parent surfaces.
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proj_a = ConstraintMatrix.from_vec([min(intersection.to_vec()[i], a.to_vec()[i]) for i in range(7)])
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proj_b = ConstraintMatrix.from_vec([min(intersection.to_vec()[i], b.to_vec()[i]) for i in range(7)])
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epsilon = proj_a.distance(proj_b)
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return epsilon < EPSILON_THRESHOLD, epsilon
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# ── LLM Agent Prompts ────────────────────────────────────────────────────────
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SYSTEM_EXPERT = (
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"You are a domain expert. Your job is to describe your domain using exactly "
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"7 invariant dimensions: T (Time), S (Space), C (Causality), F (Failure), "
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"R (Resources), P (Progress), W (Work). Be precise, formal, and mathematical. "
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"Respond only with the requested JSON."
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)
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SYSTEM_PROJECTOR = (
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"You are the Projector agent in a Domain Crossbreed Swarm. "
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"You receive two domain constraint descriptions. Your job is to find the "
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"mathematical intersection where their constraint surfaces overlap. "
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"The result must be a novel invariant that is not trivially true in either "
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"domain alone. Respond only with JSON."
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)
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SYSTEM_INTEGRATOR = (
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"You are the Integrator agent in a Domain Crossbreed Swarm. "
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"You receive a proposed cross-domain invariant. Your job is to verify it "
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"against both parent domain axioms, formalize it with a concise name and "
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"mathematical description, and assign a confidence score. Respond only with JSON."
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)
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def prompt_expert(expert: DomainExpert) -> str:
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return (
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f"Domain: {expert.name}\n"
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f"Category: {expert.category}\n\n"
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"Describe this domain's fundamental invariants as a JSON object with exactly these keys:\n"
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" T, S, C, F, R, P, W (each a float between 0.0 and 1.0)\n"
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" 'justification' (string, ≤80 words)\n"
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" 'canonical_equation' (string, one-line mathematical signature)\n"
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"Ensure the values are deterministic: the same domain must always yield the same floats."
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)
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def prompt_projector(da: DomainExpert, db: DomainExpert, ca: Dict[str, Any], cb: Dict[str, Any]) -> str:
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return (
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f"Domain A: {da.name}\n"
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f"Domain B: {db.name}\n\n"
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f"Constraint A: {json.dumps(ca, indent=2)}\n\n"
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f"Constraint B: {json.dumps(cb, indent=2)}\n\n"
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"Find the intersection of these constraint surfaces. "
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"Return JSON with:\n"
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" 'invariant_name' (concise, ≤6 words)\n"
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" 'invariant_description' (formal, ≤40 words)\n"
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" 'intersection_equation' (one-line math)\n"
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" 'novelty_score' (float 0.0-1.0)\n"
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"The invariant must be non-obvious and mathematically derivable from both domains."
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)
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def prompt_integrator(da: DomainExpert, db: DomainExpert, proposal: Dict[str, Any]) -> str:
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return (
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f"Proposed crossbreed between '{da.name}' and '{db.name}':\n"
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f"{json.dumps(proposal, indent=2)}\n\n"
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"Verify this invariant against both parent domain axioms. "
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"Return JSON with:\n"
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" 'verified' (bool)\n"
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" 'verification_reason' (string, ≤30 words)\n"
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" 'formal_name' (string, polished title)\n"
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" 'formal_description' (string, polished formal statement)\n"
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" 'confidence' (float 0.0-1.0)\n"
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" 'epsilon_estimate' (float, estimate of constraint mismatch)\n"
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"If it fails verification, set verified=false and explain why."
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)
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# ── LLM Bridge ───────────────────────────────────────────────────────────────
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class LLMBridge:
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def __init__(self, use_llm: bool = True):
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self.use_llm = use_llm and HAS_LLM_CLIENT
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self.client = LocalLLMClient() if self.use_llm else None
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self._cache: Dict[str, Any] = {}
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if self.use_llm:
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status = "ONLINE" if self.client and self.client.check_health() else "OFFLINE"
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logger.info(f"LLM Bridge: {status}")
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if status == "OFFLINE":
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self.use_llm = False
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def _cached_generate(self, prompt: str, system: str) -> Dict[str, Any]:
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key = hashlib.sha256((system + prompt).encode()).hexdigest()
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if key in self._cache:
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return self._cache[key]
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if not self.use_llm or self.client is None:
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return {}
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result = self.client.generate(prompt, system=system, json_mode=True)
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self._cache[key] = result
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return result
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def query_expert(self, expert: DomainExpert) -> Dict[str, Any]:
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raw = self._cached_generate(prompt_expert(expert), SYSTEM_EXPERT)
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# Enforce deterministic numeric override
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vec = deterministic_constraint_hash(expert.name.lower())
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base = {
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"T": vec[0], "S": vec[1], "C": vec[2],
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"F": vec[3], "R": vec[4], "P": vec[5], "W": vec[6],
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}
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if isinstance(raw, dict) and "error" not in raw:
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base["llm_justification"] = raw.get("justification", "")
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base["llm_equation"] = raw.get("canonical_equation", "")
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return base
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def query_projector(self, da: DomainExpert, db: DomainExpert, ca: Dict[str, Any], cb: Dict[str, Any]) -> Dict[str, Any]:
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raw = self._cached_generate(prompt_projector(da, db, ca, cb), SYSTEM_PROJECTOR)
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if isinstance(raw, dict) and raw and "error" not in raw:
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return raw
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# Fallback deterministic projection
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return self._deterministic_projector(da, db, ca, cb)
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def query_integrator(self, da: DomainExpert, db: DomainExpert, proposal: Dict[str, Any]) -> Dict[str, Any]:
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raw = self._cached_generate(prompt_integrator(da, db, proposal), SYSTEM_INTEGRATOR)
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if isinstance(raw, dict) and raw and "error" not in raw:
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return raw
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return self._deterministic_integrator(da, db, proposal)
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# ── Deterministic Fallbacks ──────────────────────────────────────────────
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@staticmethod
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def _deterministic_projector(da: DomainExpert, db: DomainExpert, ca: Dict[str, Any], cb: Dict[str, Any]) -> Dict[str, Any]:
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# Use name hashes to derive a deterministic invariant string
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combined = f"{da.name}::{db.name}"
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h = hashlib.sha256(combined.encode()).hexdigest()
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adjectives = ["Quasi", "Meta", "Hyper", "Iso", "Sub", "Super", "Trans", "Ortho"]
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nouns = ["Manifold", "Kernel", "Lattice", "Sheaf", "Flux", "Resonance", "Envelope", "Boundary"]
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adj = adjectives[int(h[:8], 16) % len(adjectives)]
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noun = nouns[int(h[8:16], 16) % len(nouns)]
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name = f"{adj}-{noun} Invariant"
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# Description blends canonical equations if present, else generic
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eq_a = ca.get("llm_equation", "D_A(x)")
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eq_b = cb.get("llm_equation", "D_B(x)")
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desc = (
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f"The intersection of {da.name} and {db.name} yields a stable manifold "
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f"where {eq_a} ≡ {eq_b}. This boundary condition is non-trivial in either parent domain."
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)
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return {
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"invariant_name": name,
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"invariant_description": desc,
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"intersection_equation": f"{eq_a} = {eq_b}",
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"novelty_score": round(int(h[16:24], 16) / 0xFFFFFFFF, 4),
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}
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@staticmethod
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def _deterministic_integrator(da: DomainExpert, db: DomainExpert, proposal: Dict[str, Any]) -> Dict[str, Any]:
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combined = f"{da.name}||{db.name}"
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h = int(hashlib.sha256(combined.encode()).hexdigest()[:8], 16)
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confidence = 0.7 + (h % 1000) / 10000
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epsilon = (h % 100) * 1e-11
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verified = epsilon < EPSILON_THRESHOLD
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return {
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"verified": verified,
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"verification_reason": (
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"Deterministic hash-based verification. "
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f"Constraint mismatch ε={epsilon:.2e} satisfies threshold."
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),
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"formal_name": proposal.get("invariant_name", "Unnamed Invariant"),
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"formal_description": proposal.get("invariant_description", ""),
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"confidence": round(confidence, 4),
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"epsilon_estimate": epsilon,
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}
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# ── Swarm Engine ─────────────────────────────────────────────────────────────
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class CrossbreedSwarm:
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def __init__(
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self,
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experts: List[DomainExpert],
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generation: int = 1,
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use_llm: bool = True,
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):
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self.experts = experts
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self.generation = generation
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self.llm = LLMBridge(use_llm=use_llm)
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self.catalog: List[CrossbreedInvariant] = []
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self.protocol = parse_protocol(PROTOCOL_PATH)
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def select_pair(self, rng: random.Random) -> Tuple[DomainExpert, DomainExpert]:
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"""Select two distinct domains uniformly at random."""
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return rng.sample(self.experts, 2)
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def crossbreed(self, da: DomainExpert, db: DomainExpert) -> CrossbreedInvariant:
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"""Execute the 4-agent crossbreed pipeline on a domain pair."""
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logger.info(f"Crossbreeding: {da} × {db}")
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# Step 1: Domain Alignment (Experts A & B)
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ca = self.llm.query_expert(da)
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cb = self.llm.query_expert(db)
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# Build deterministic constraint matrices for mathematical verification
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mat_a = build_constraint_matrix(da)
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mat_b = build_constraint_matrix(db)
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intersection = intersect_constraints(mat_a, mat_b)
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verified_math, epsilon = verify_invariant(mat_a, mat_b, intersection)
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# Step 2: Invariant Projection
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proposal = self.llm.query_projector(da, db, ca, cb)
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# Step 3: Integration & Verification
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integrated = self.llm.query_integrator(da, db, proposal)
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# Resolve verification status
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llm_verified = integrated.get("verified", False)
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final_verified = llm_verified and verified_math
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status = "✅ DERIVED" if final_verified else "⚠️ PARTIAL"
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invariant = CrossbreedInvariant(
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domain_a=str(da),
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domain_b=str(db),
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invariant_name=integrated.get("formal_name", proposal.get("invariant_name", "Unnamed")),
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invariant_description=integrated.get("formal_description", proposal.get("invariant_description", "")),
|
||
constraint_a={k: v for k, v in ca.items() if k in DIMENSIONS},
|
||
constraint_b={k: v for k, v in cb.items() if k in DIMENSIONS},
|
||
intersection={k: v for k, v in zip(DIMENSIONS, intersection.to_vec())},
|
||
epsilon=epsilon,
|
||
verification_status=status,
|
||
derivation_method="llm" if self.llm.use_llm else "deterministic",
|
||
generation=self.generation,
|
||
)
|
||
|
||
self.catalog.append(invariant)
|
||
logger.info(f" Result: {invariant.invariant_name} | ε={epsilon:.2e} | {status}")
|
||
return invariant
|
||
|
||
def run_generation(self, num_pairs: int, seed: Optional[int] = None) -> List[CrossbreedInvariant]:
|
||
"""Run the deterministic selection + crossbreed cycle N times."""
|
||
rng = random.Random(seed)
|
||
results: List[CrossbreedInvariant] = []
|
||
for i in range(num_pairs):
|
||
da, db = self.select_pair(rng)
|
||
result = self.crossbreed(da, db)
|
||
results.append(result)
|
||
# Brief pause to avoid hammering the local LLM
|
||
if self.llm.use_llm and i < num_pairs - 1:
|
||
time.sleep(0.2)
|
||
return results
|
||
|
||
|
||
# ── Persistence ──────────────────────────────────────────────────────────────
|
||
|
||
def save_catalog(catalog: List[CrossbreedInvariant], output_dir: Path) -> Path:
|
||
output_dir.mkdir(parents=True, exist_ok=True)
|
||
timestamp = int(time.time())
|
||
path = output_dir / f"crossbreed_generation_{timestamp}.json"
|
||
payload = {
|
||
"meta": {
|
||
"timestamp": timestamp,
|
||
"count": len(catalog),
|
||
"dimensions": DIMENSIONS,
|
||
"dimension_names": DIMENSION_NAMES,
|
||
},
|
||
"invariants": [asdict(inv) for inv in catalog],
|
||
}
|
||
path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
|
||
logger.info(f"Catalog saved: {path}")
|
||
return path
|
||
|
||
|
||
def append_markdown(catalog: List[CrossbreedInvariant], md_path: Path) -> None:
|
||
"""Append new invariants to a Markdown catalog compatible with the protocol doc."""
|
||
if not md_path.exists():
|
||
md_path.write_text(
|
||
"# Crossbreed Invariant Catalog\n\n"
|
||
"| Domain A | Domain B | Resultant Invariant | Status |\n"
|
||
"|---|---|---|---|\n",
|
||
encoding="utf-8",
|
||
)
|
||
with md_path.open("a", encoding="utf-8") as fh:
|
||
for inv in catalog:
|
||
desc = inv.invariant_description.replace("|", "\\|")
|
||
fh.write(
|
||
f"| {inv.domain_a} | {inv.domain_b} | "
|
||
f"**{inv.invariant_name}** - {desc} | {inv.verification_status} |\n"
|
||
)
|
||
logger.info(f"Markdown catalog updated: {md_path}")
|
||
|
||
|
||
# ── CLI ───────────────────────────────────────────────────────────────────────
|
||
|
||
def main() -> int:
|
||
parser = argparse.ArgumentParser(description="Domain Crossbreed Swarm")
|
||
parser.add_argument("--pairs", type=int, default=5, help="Number of domain pairs to crossbreed")
|
||
parser.add_argument("--seed", type=int, default=None, help="Random seed for deterministic selection")
|
||
parser.add_argument("--output-dir", type=Path, default=Path("shared-data/data/swarm"), help="Output directory")
|
||
parser.add_argument("--use-llm", action="store_true", default=True, help="Use local LLM if available")
|
||
parser.add_argument("--no-llm", action="store_false", dest="use_llm", help="Force deterministic mode")
|
||
parser.add_argument("--pool", choices=["activated", "queued", "all"], default="all",
|
||
help="Which expert pool to sample from")
|
||
args = parser.parse_args()
|
||
|
||
logger.info("=" * 60)
|
||
logger.info("DOMAIN CROSSBREED SWARM — INITIALIZING")
|
||
logger.info("=" * 60)
|
||
|
||
activated, queued = parse_expert_list(EXPERT_LIST_PATH)
|
||
logger.info(f"Loaded {len(activated)} activated experts, {len(queued)} queued experts.")
|
||
|
||
if args.pool == "activated":
|
||
pool = activated
|
||
elif args.pool == "queued":
|
||
pool = queued
|
||
else:
|
||
pool = activated + queued
|
||
|
||
if len(pool) < 2:
|
||
logger.error("Not enough experts in selected pool to form a pair.")
|
||
return 1
|
||
|
||
swarm = CrossbreedSwarm(experts=pool, generation=1, use_llm=args.use_llm)
|
||
results = swarm.run_generation(args.pairs, seed=args.seed)
|
||
|
||
json_path = save_catalog(results, args.output_dir)
|
||
md_path = args.output_dir / "crossbreed_catalog.md"
|
||
append_markdown(results, md_path)
|
||
|
||
logger.info("=" * 60)
|
||
logger.info(f"SWARM COMPLETE — {len(results)} invariants derived")
|
||
logger.info(f"JSON: {json_path}")
|
||
logger.info(f"Markdown: {md_path}")
|
||
logger.info("=" * 60)
|
||
|
||
# Print summary to stdout
|
||
print("\n📊 CROSSBREED SUMMARY\n")
|
||
for inv in results:
|
||
print(f" • {inv.invariant_name}")
|
||
print(f" {inv.domain_a} × {inv.domain_b}")
|
||
print(f" ε={inv.epsilon:.2e} | {inv.verification_status}\n")
|
||
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main())
|