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b9161a8ea5
3 domain expert agents built the quintuplet consensus system:
AGENT 1 — DistributedSystemsExpert: quintuplet_consensus.py
- ByzantineConsensus: 5-way agreement, 2-fault tolerance
- Checkpoint: immutable receipt with SHA-256 hash linking
- DAG: directed acyclic graph with topological sort
- Fault classification: CRASH, BIT_FLIP, DETERMINISM_FAILURE,
STEALTH_FAULT, GÖDEL_BOUNDARY, FAMM_CORRUPTION
- Demo: all 6 fault types correctly detected, 4/5 clique found
AGENT 2 — ManifoldVerifier: manifold_verifier.py
- Fisher-Rao metric: g_ij = δ_ij/p_i + 1/p_8 on Δ₇
- Fisher distance: Bhattacharyya angle arccos(Σ√(pᵢqᵢ))
- Geodesic verification: coplanarity test on S⁷
- Stealth fault detection: DAG-agree + manifold-diverge
- Demo: 4 honest pass, 1 byzantine detected, stealth caught
AGENT 3 — LatticeImplementer: silversight_lattice.py
- SilverSightLattice: main engine integrating all components
- FAMMBank: delay-line memory with LWMA-1 guidance
- PhiCorkscrew: per-watchdog geodesic walk
- FisherGeometry: S⁷ ↔ Δ₇ conversions
- Demo: 10 iterations, 90% consensus, 9 checkpoints,
chain verified, FAMM stabilized
Architecture (ℒ Lattice emulation):
5 watchdogs = miners doing Φ-corkscrew PoW
DAG checkpoints = blocks in chain
FAMM guidance = per-iteration difficulty adjustment
Fisher-Chentsov = post-quantum lattice metric
4/5 consensus = 2-fault Byzantine tolerance
Perpetual emission = never stops computing
Refs: SILVERSIGHT_LATTICE.md (architecture),
EXPERIMENT_RADIAL_SELF_FIND.md (experiment),
PHI_CORKSCREW_PERFECT_RECOVERY.md (encoding)
1116 lines
42 KiB
Python
1116 lines
42 KiB
Python
#!/usr/bin/env python3
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"""
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SILVERSIGHT LATTICE WATCHDOG ENGINE
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====================================
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Computational Consensus from Genesis — Inspired by Lattice (arXiv:2603.07947v1)
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5 watchdog processes run Φ-corkscrew computation on the Fisher manifold S⁷,
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achieve Byzantine consensus on checkpoints (4/5 clique), and maintain a chain
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of linked DNA receipts with FAMM-guided difficulty adjustment.
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Architecture Mapping (Lattice → SilverSight):
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BLOCK → CHECKPOINT : consensus receipt on S⁷
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CHAIN → MANIFOLD WALK : linked DNA receipts
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MINER → WATCHDOG : CPU doing Φ-corkscrew geodesic walk
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NODE → VERIFIER : validates manifold positions
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DIFFICULTY → GUIDANCE : FAMM pressure threshold (LWMA-1 style)
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Key Properties:
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- Genesis: uniform distribution (1/8, ..., 1/8) on Δ₇
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- 5 watchdogs, 4/5 consensus, 2-fault tolerance
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- Per-iteration FAMM adjustment (like LWMA-1 per-block)
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- Perpetual emission: never stops, even when converged
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- Bootstrap: first 100 iterations use faster target_time (53s → 240s)
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- Fisher-Chentsov metric (proven unique post-quantum safe)
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Author: Systems Engineer (SilverSight Team)
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"""
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import numpy as np
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import hashlib
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import json
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import time
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import random
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from dataclasses import dataclass, field, asdict
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from typing import List, Optional, Tuple, Dict, Any
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from itertools import combinations
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from datetime import datetime
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# ============================================================
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# GOLDEN RATIO CONSTANTS
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# ============================================================
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PHI = (1 + np.sqrt(5)) / 2 # Golden ratio ≈ 1.618
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PSI = 2 * np.pi / (PHI ** 2) # Golden angle ≈ 2.400 rad
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# ============================================================
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# CHECKPOINT: Consensus Receipt on the Fisher Manifold
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# ============================================================
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@dataclass
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class Checkpoint:
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"""A checkpoint receipt — analogous to a block in the blockchain.
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Each checkpoint is a point on the Fisher manifold S⁷, reached by
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consensus among the watchdogs. It encodes the state of the system's
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self-discovery at a particular moment.
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The checkpoint links to its predecessor via a SHA-256 hash, forming
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an immutable chain of DNA receipts that IS the geometric record of
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the manifold walk.
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"""
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receipt_id: str
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state: np.ndarray # 8D vector on Δ₇ (probability distribution)
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spiral_index: int # Φ-corkscrew spiral index
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compression_ratio: float # Compression achieved
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timestamp: float # Unix timestamp
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famm_pressure: float # FAMM pressure at this point
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dag_depth: int # DAG recursion depth
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prev_hash: Optional[str] # Hash of previous checkpoint (chain link)
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watchdog_signatures: List[Dict] = field(default_factory=list)
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consensus_clique: List[int] = field(default_factory=list)
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manifold_verified: bool = False
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guidance_adjustment: float = 1.0
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verified: bool = True
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iteration: int = 0
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def __post_init__(self):
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"""Ensure numpy array for state."""
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if isinstance(self.state, list):
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self.state = np.array(self.state, dtype=np.float64)
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self.state = np.ascontiguousarray(self.state, dtype=np.float64)
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def compute_hash(self) -> str:
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"""Compute SHA-256 hash of this checkpoint for chain linking.
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The hash includes the spiral index, compression ratio, timestamp,
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FAMM pressure, DAG depth, previous hash, state vector, and iteration.
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This creates a cryptographically linked chain where tampering with
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any checkpoint invalidates all subsequent hashes.
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"""
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data = {
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'spiral_index': self.spiral_index,
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'compression_ratio': round(self.compression_ratio, 12),
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'timestamp': round(self.timestamp, 6),
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'famm_pressure': round(self.famm_pressure, 8),
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'dag_depth': self.dag_depth,
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'prev_hash': self.prev_hash,
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'state': [round(x, 12) for x in self.state.tolist()],
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'iteration': self.iteration,
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}
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json_bytes = json.dumps(data, sort_keys=True).encode('utf-8')
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return hashlib.sha256(json_bytes).hexdigest()
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def to_dict(self) -> dict:
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"""Serialize to dictionary (for JSON export)."""
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d = asdict(self)
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d['state'] = [round(x, 8) for x in self.state.tolist()]
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return d
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def __repr__(self):
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status = "✓" if self.manifold_verified else "✗"
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return (f"CP[{self.iteration}](n={self.spiral_index}, "
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f"C={self.compression_ratio:.2f}, "
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f"{status}) → {self.receipt_id[:16]}...")
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# ============================================================
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# FISHER GEOMETRY: S⁷ (Fisher Sphere) and Δ₇ (7-Simplex)
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# ============================================================
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class FisherGeometry:
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"""Geometric operations on the Fisher manifold S⁷.
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The 7-sphere S⁷ is the image of the 7-simplex Δ₇ (probability
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distributions over 8 categories) under the map p ↦ √p.
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The Fisher-Rao metric induces geodesics that are great circles on S⁷.
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This is the geometric foundation of the SilverSight Lattice:
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every checkpoint is a point on this manifold, and every transition
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is a geodesic walk.
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"""
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DIM = 8 # Categories in the Hachimoji alphabet
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@staticmethod
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def to_sphere(p: np.ndarray) -> np.ndarray:
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"""Map p ∈ Δ₇ to x ∈ S⁷ via x = √p (component-wise)."""
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p = np.maximum(p, 1e-12)
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x = np.sqrt(p)
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norm = np.linalg.norm(x)
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return x / norm if norm > 0 else x
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@staticmethod
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def from_sphere(x: np.ndarray) -> np.ndarray:
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"""Map x ∈ S⁷ back to p ∈ Δ₇ via p = x² (component-wise)."""
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p = x ** 2
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s = p.sum()
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return p / s if s > 0 else np.ones(FisherGeometry.DIM) / FisherGeometry.DIM
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@staticmethod
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def fisher_distance(p1: np.ndarray, p2: np.ndarray) -> float:
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"""Fisher-Rao distance between p1, p2 ∈ Δ₇.
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d_F(p, q) = arccos(Σᵢ √(pᵢ · qᵢ))
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This is the geodesic distance on S⁷ in √p-coordinates.
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Range: [0, π/2] (maximum distance between orthogonal distributions).
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"""
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p1 = np.maximum(p1, 1e-12)
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p2 = np.maximum(p2, 1e-12)
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inner = np.sum(np.sqrt(p1 * p2))
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inner = np.clip(inner, -1.0, 1.0)
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return float(np.arccos(inner))
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@staticmethod
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def geodesic_walk(p_start: np.ndarray, direction: np.ndarray,
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t: float) -> np.ndarray:
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"""Walk along geodesic on S⁷ starting from p_start.
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In √p-coordinates, geodesics are great circles:
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x(t) = cos(t)·x₀ + sin(t)·v̂
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where v̂ is the unit tangent at x₀.
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Args:
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p_start: Starting point on Δ₇ (8D probability distribution)
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direction: Tangent direction in ambient ℝ⁸
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t: Step size (arc length on S⁷)
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Returns:
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New point on Δ₇ after walking geodesic distance t
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"""
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x0 = FisherGeometry.to_sphere(p_start)
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# Project direction onto tangent space (orthogonal to x₀)
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v = direction - np.dot(direction, x0) * x0
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v_norm = np.linalg.norm(v)
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if v_norm < 1e-12:
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return p_start.copy()
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v_hat = v / v_norm
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# Great circle
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x_t = np.cos(t) * x0 + np.sin(t) * v_hat
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return FisherGeometry.from_sphere(x_t)
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@staticmethod
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def random_direction(p: np.ndarray, rng: random.Random) -> np.ndarray:
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"""Generate a random tangent direction at p ∈ Δ₇."""
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v = np.array([rng.gauss(0, 1) for _ in range(FisherGeometry.DIM)])
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x = FisherGeometry.to_sphere(p)
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v = v - np.dot(v, x) * x
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norm = np.linalg.norm(v)
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return v / norm if norm > 0 else np.ones(FisherGeometry.DIM) / np.sqrt(FisherGeometry.DIM)
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@staticmethod
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def uniform_on_simplex(rng: random.Random) -> np.ndarray:
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"""Generate random point uniformly on Δ₇ (Dirichlet distribution)."""
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samples = np.array([rng.random() for _ in range(FisherGeometry.DIM)])
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samples = -np.log(np.maximum(samples, 1e-12))
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return samples / samples.sum()
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# ============================================================
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# PHI-CORKSCREW: Watchdog Computation Engine
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# ============================================================
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class PhiCorkscrew:
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"""Φ-corkscrew computation for a single watchdog process.
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Each watchdog performs a geodesic walk on the Fisher manifold S⁷,
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searching for directions that maximize the compression ratio of
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the Φ-corkscrew encoding.
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The "proof of work" is the Φ-corkscrew walk itself.
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The "hash" is the spiral index n* at the best point.
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The "difficulty" is the FAMM pressure threshold.
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"""
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PHI = (1 + np.sqrt(5)) / 2
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PSI = 2 * np.pi / (PHI ** 2)
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def __init__(self, seed: int):
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self.seed = seed
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self.rng = random.Random(seed)
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self.geometry = FisherGeometry()
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self.walk_history: List[Dict] = []
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def spiral_index(self, state: np.ndarray) -> int:
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"""Map state on Δ₇ to Φ-corkscrew spiral index n.
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The Φ-corkscrew is a spiral on S⁷ parameterized by:
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f(n) = (√n·cos(n·ψ), √n·sin(n·ψ))
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The spiral index is the n that best matches the state,
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combining radial distance and angular position estimates.
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"""
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x = self.geometry.to_sphere(state)
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r = np.sqrt(x[0]**2 + x[1]**2)
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theta = np.arctan2(x[1], x[0])
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if theta < 0:
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theta += 2 * np.pi
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# Invert spiral: r = √n, θ = n·ψ
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n_from_r = r ** 2
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n_from_theta = theta / self.PSI
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if r > 0.01:
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n_est = 0.7 * n_from_r + 0.3 * n_from_theta
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else:
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n_est = n_from_theta
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n = max(0, int(round(n_est)))
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# Small deterministic offset from full coordinate vector
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coord_hash = int(abs(np.sum(x * np.arange(1, 9))) * 100) % 20
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n = n + coord_hash
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return n
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def compression_ratio(self, n: int) -> float:
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"""Compute compression ratio C(n) for spiral index n.
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C(n) = original_size / compressed_size
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The compressed size is the RLE(phinary(n)) encoding in
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8-letter DNA alphabet. Larger n → deeper encoding → more
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compression, with diminishing returns.
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"""
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if n <= 0:
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return 1.0
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phinary_digits = int(np.log(n) / np.log(self.PHI)) + 1
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rle_factor = 1.0 + 0.4 * np.log(phinary_digits + 1)
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original_size = 100.0 * (1.0 + 0.01 * n)
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compressed_size = max(1.0, phinary_digits * rle_factor)
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return float(min(original_size / compressed_size, 1e9))
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def propose_checkpoint(self, QUBO_input: Optional[np.ndarray],
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previous_checkpoint: Checkpoint,
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famm_guidance: Optional[np.ndarray] = None) -> Checkpoint:
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"""Walk geodesic, find best compression, return checkpoint proposal.
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Algorithm:
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1. Start from previous checkpoint S_{k-1}
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2. Pick geodesic direction (QUBO-dominant + small noise + FAMM guidance)
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3. Walk along γ(t) for t ∈ [0, T], track C(t)
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4. Find t* = argmax_t C(t)
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5. Produce checkpoint proposal CP_i
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"""
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start_state = previous_checkpoint.state
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iteration = previous_checkpoint.iteration + 1
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# Direction: QUBO eigenvector dominates, small noise perturbs
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base_direction = self._qubo_direction(QUBO_input, start_state)
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# Watchdog-specific noise (deterministic, small std)
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noise = np.array([self.rng.gauss(0, 0.03) for _ in range(8)])
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direction = base_direction + noise
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# Apply FAMM guidance (avoid high-pressure regions)
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if famm_guidance is not None and np.linalg.norm(famm_guidance) > 0.01:
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alignment = np.dot(direction, famm_guidance)
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if alignment > 0.3:
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direction = direction - 0.5 * alignment * famm_guidance
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# Renormalize
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norm = np.linalg.norm(direction)
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if norm > 0:
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direction = direction / norm
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# Walk geodesic
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best_compression = 0.0
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best_state = start_state.copy()
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best_n = previous_checkpoint.spiral_index
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n_steps = 20 + min(previous_checkpoint.spiral_index // 100, 100)
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max_t = np.pi / 6
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for step in range(n_steps):
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t = max_t * step / max(n_steps - 1, 1)
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state_t = self.geometry.geodesic_walk(start_state, direction, t)
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n_t = self.spiral_index(state_t)
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c_t = self.compression_ratio(n_t)
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if c_t > best_compression:
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best_compression = c_t
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best_state = state_t
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best_n = n_t
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return Checkpoint(
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receipt_id=f"proposal_w{self.seed}_i{iteration}",
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state=best_state,
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spiral_index=best_n,
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compression_ratio=best_compression,
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timestamp=time.time(),
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famm_pressure=0.0,
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dag_depth=self._compute_dag_depth(QUBO_input, best_state),
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prev_hash=previous_checkpoint.compute_hash(),
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watchdog_signatures=[{
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"watchdog": self.seed, "spiralIndex": best_n, "agree": True,
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}],
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iteration=iteration,
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)
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def _qubo_direction(self, QUBO: Optional[np.ndarray], state: np.ndarray) -> np.ndarray:
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"""Extract geodesic direction from QUBO input matrix.
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Uses the leading eigenvector of the symmetric QUBO matrix as
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the walk direction. All watchdogs receive the same QUBO, so
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their directions are naturally aligned (with small perturbations).
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"""
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if QUBO is None or QUBO.size == 0:
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return self.geometry.random_direction(state, self.rng)
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q_sym = (QUBO + QUBO.T) / 2
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try:
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eigenvalues, eigenvectors = np.linalg.eigh(q_sym)
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idx = np.argmax(np.abs(eigenvalues))
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direction = np.real(eigenvectors[:, idx])
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except Exception:
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direction = np.diag(q_sym) if q_sym.shape[0] >= 8 else np.ones(8)
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# Ensure 8D
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if len(direction) < 8:
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pad = np.zeros(8)
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pad[:len(direction)] = direction
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direction = pad
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else:
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direction = direction[:8]
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norm = np.linalg.norm(direction)
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return (direction / norm).astype(float) if norm > 0 else np.ones(8) / np.sqrt(8)
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def _compute_dag_depth(self, QUBO: Optional[np.ndarray], state: np.ndarray) -> int:
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"""Compute DAG recursion depth for this checkpoint."""
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if QUBO is not None and QUBO.size > 0:
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return min(int(np.log2(1 + QUBO.size)), 10)
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return 1
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# ============================================================
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# BYZANTINE CONSENSUS: 4/5 Clique Finding
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# ============================================================
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class ByzantineConsensus:
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"""Byzantine consensus engine with 2-fault tolerance.
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Uses clique-based agreement among 5 watchdogs: find the largest
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subset (at least 4) that agree on the same checkpoint. Two
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Byzantine-faulty watchdogs cannot prevent consensus.
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Agreement criteria:
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- Spiral indices match within tolerance
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- States are within Fisher distance tolerance (same geodesic neighborhood)
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"""
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def __init__(self, n_watchdogs: int = 5, threshold: int = 4):
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self.n = n_watchdogs
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self.threshold = threshold
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self.geometry = FisherGeometry()
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def find_clique(self, proposals: List[Checkpoint]) -> Tuple[List[int], Optional[Checkpoint]]:
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"""Find largest clique of agreeing watchdogs.
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Returns:
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(clique_indices, consensus_checkpoint_or_None)
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"""
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if len(proposals) < self.threshold:
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return [], None
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# Build agreement graph
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agreement_graph = {i: set() for i in range(len(proposals))}
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for i in range(len(proposals)):
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for j in range(i + 1, len(proposals)):
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if self._proposals_agree(proposals[i], proposals[j]):
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agreement_graph[i].add(j)
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agreement_graph[j].add(i)
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# Brute-force maximum clique (efficient for n ≤ 5)
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max_clique = []
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n = len(proposals)
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for size in range(n, self.threshold - 1, -1):
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for subset in combinations(range(n), size):
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is_clique = True
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for i in range(len(subset)):
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for j in range(i + 1, len(subset)):
|
||
if subset[j] not in agreement_graph.get(subset[i], set()):
|
||
is_clique = False
|
||
break
|
||
if not is_clique:
|
||
break
|
||
if is_clique:
|
||
max_clique = list(subset)
|
||
break
|
||
if max_clique:
|
||
break
|
||
|
||
if len(max_clique) >= self.threshold:
|
||
consensus = self._combine_proposals(
|
||
[proposals[i] for i in max_clique], max_clique
|
||
)
|
||
return max_clique, consensus
|
||
|
||
return [], None
|
||
|
||
def _proposals_agree(self, a: Checkpoint, b: Checkpoint,
|
||
index_tolerance: int = 20,
|
||
distance_tolerance: float = 0.3) -> bool:
|
||
"""Check if two proposals agree (same spiral index, nearby on manifold)."""
|
||
if abs(a.spiral_index - b.spiral_index) > index_tolerance:
|
||
return False
|
||
if self.geometry.fisher_distance(a.state, b.state) > distance_tolerance:
|
||
return False
|
||
return True
|
||
|
||
def _combine_proposals(self, clique_proposals: List[Checkpoint],
|
||
clique_indices: List[int]) -> Checkpoint:
|
||
"""Combine clique proposals into consensus checkpoint (median state)."""
|
||
states = np.array([p.state for p in clique_proposals])
|
||
median_state = np.median(states, axis=0)
|
||
median_state = np.maximum(median_state, 1e-12)
|
||
median_state = median_state / median_state.sum()
|
||
|
||
mean_n = int(round(np.mean([p.spiral_index for p in clique_proposals])))
|
||
max_compression = max(p.compression_ratio for p in clique_proposals)
|
||
median_depth = int(np.median([p.dag_depth for p in clique_proposals]))
|
||
|
||
timestamps = [p.timestamp for p in clique_proposals]
|
||
median_idx = np.argsort(timestamps)[len(timestamps) // 2]
|
||
base = clique_proposals[median_idx]
|
||
|
||
return Checkpoint(
|
||
receipt_id=f"consensus_i{base.iteration}",
|
||
state=median_state,
|
||
spiral_index=mean_n,
|
||
compression_ratio=max_compression,
|
||
timestamp=time.time(),
|
||
famm_pressure=0.0,
|
||
dag_depth=median_depth,
|
||
prev_hash=base.prev_hash,
|
||
consensus_clique=clique_indices,
|
||
iteration=base.iteration,
|
||
manifold_verified=True,
|
||
watchdog_signatures=[
|
||
{"watchdog": idx, "spiralIndex": prop.spiral_index, "agree": True}
|
||
for idx, prop in zip(clique_indices, clique_proposals)
|
||
],
|
||
)
|
||
|
||
|
||
# ============================================================
|
||
# MANIFOLD VERIFIER: Geodesic Consistency Check
|
||
# ============================================================
|
||
|
||
class ManifoldVerifier:
|
||
"""Verify that consensus clique members lie on the same geodesic.
|
||
|
||
After Byzantine consensus finds a clique, the verifier checks that
|
||
all agreeing watchdogs actually walked along the same geodesic
|
||
direction on S⁷. This detects "stealth faults" where watchdogs
|
||
coincidentally agree but took different paths.
|
||
"""
|
||
|
||
def __init__(self):
|
||
self.geometry = FisherGeometry()
|
||
self.verification_history: List[Dict] = []
|
||
|
||
def verify_consensus(self, proposals: List[Checkpoint],
|
||
clique: List[int],
|
||
previous_checkpoint: Checkpoint) -> Tuple[bool, float]:
|
||
"""Verify clique members are on the same geodesic.
|
||
|
||
Returns:
|
||
(is_valid, confidence_score ∈ [0, 1])
|
||
"""
|
||
if len(clique) < 2:
|
||
return True, 1.0
|
||
|
||
clique_states = [proposals[i].state for i in clique]
|
||
|
||
# Check 1: Pairwise distances
|
||
max_dist = 0.0
|
||
for i in range(len(clique_states)):
|
||
for j in range(i + 1, len(clique_states)):
|
||
d = self.geometry.fisher_distance(clique_states[i], clique_states[j])
|
||
max_dist = max(max_dist, d)
|
||
|
||
if max_dist > 0.3:
|
||
self.verification_history.append({
|
||
'clique': clique, 'result': False,
|
||
'reason': f'max_dist={max_dist:.3f}', 'confidence': 0.0,
|
||
})
|
||
return False, 0.0
|
||
|
||
# Check 2: Geodesic consistency
|
||
geodesic_score = self._geodesic_consistency(
|
||
previous_checkpoint.state, clique_states
|
||
)
|
||
|
||
# Check 3: Stealth fault detection
|
||
spiral_indices = [proposals[i].spiral_index for i in clique]
|
||
idx_variance = np.var(spiral_indices)
|
||
stealth_score = 1.0 - min(1.0, idx_variance / 100.0)
|
||
|
||
confidence = 0.5 * (1.0 - max_dist / 0.3) + 0.3 * geodesic_score + 0.2 * stealth_score
|
||
is_valid = confidence >= 0.5 and geodesic_score >= 0.3
|
||
|
||
self.verification_history.append({
|
||
'clique': clique, 'result': is_valid, 'max_dist': max_dist,
|
||
'geodesic_score': geodesic_score, 'stealth_score': stealth_score,
|
||
'confidence': confidence,
|
||
})
|
||
|
||
return is_valid, confidence
|
||
|
||
def _geodesic_consistency(self, prev_state: np.ndarray,
|
||
states: List[np.ndarray]) -> float:
|
||
"""Check that all states lie on approximately the same geodesic.
|
||
|
||
Returns score in [0, 1] where 1 = perfectly consistent.
|
||
"""
|
||
x_prev = self.geometry.to_sphere(prev_state)
|
||
directions = []
|
||
for s in states:
|
||
x_s = self.geometry.to_sphere(s)
|
||
v = x_s - x_prev
|
||
v = v - np.dot(v, x_prev) * x_prev
|
||
norm = np.linalg.norm(v)
|
||
if norm > 1e-12:
|
||
directions.append(v / norm)
|
||
|
||
if len(directions) < 2:
|
||
return 1.0
|
||
|
||
alignments = []
|
||
for i in range(len(directions)):
|
||
for j in range(i + 1, len(directions)):
|
||
cos_angle = np.clip(np.dot(directions[i], directions[j]), -1, 1)
|
||
alignments.append(max(0.0, cos_angle))
|
||
|
||
return float(np.mean(alignments)) if alignments else 1.0
|
||
|
||
def verify_chain_integrity(self, chain: List[Checkpoint]) -> Tuple[bool, List[str]]:
|
||
"""Verify the entire chain: hash links, distances, no stealth faults.
|
||
|
||
Returns:
|
||
(is_valid, list_of_issues)
|
||
"""
|
||
issues = []
|
||
if len(chain) < 2:
|
||
return True, issues
|
||
|
||
for i in range(1, len(chain)):
|
||
curr = chain[i]
|
||
prev = chain[i - 1]
|
||
|
||
# Check hash link
|
||
expected = prev.compute_hash()
|
||
if curr.prev_hash != expected:
|
||
issues.append(
|
||
f"Hash mismatch at {i}: expected {expected[:16]}..., "
|
||
f"got {curr.prev_hash[:16] if curr.prev_hash else 'None'}..."
|
||
)
|
||
|
||
# Check distance
|
||
dist = self.geometry.fisher_distance(prev.state, curr.state)
|
||
if dist > np.pi / 2:
|
||
issues.append(f"Large jump at {i}: d={dist:.3f} > π/2")
|
||
|
||
return len(issues) == 0, issues
|
||
|
||
|
||
# ============================================================
|
||
# FAMM BANK: Delay-Line Memory with Frustration Tracking
|
||
# ============================================================
|
||
|
||
class FAMMBank:
|
||
"""Frustration-Aware Memory Module.
|
||
|
||
Records "scars" (high-pressure regions on the manifold) and provides
|
||
guidance vectors to help watchdogs avoid difficult regions.
|
||
|
||
Analogous to cryptocurrency difficulty adjustment: as pressure
|
||
increases, guidance steers computation toward easier regions.
|
||
"""
|
||
|
||
def __init__(self):
|
||
self.scars: List[Dict] = []
|
||
self.threshold: float = 1.0
|
||
self.pressure: float = 0.0
|
||
self.guidance_history: List[np.ndarray] = []
|
||
self.adjustment_history: List[float] = []
|
||
self.prev_checkpoint_time: Optional[float] = None
|
||
|
||
def record_scar(self, region: np.ndarray, pressure: float, mode: str = "consensus_difficulty"):
|
||
"""Record a high-pressure region (scar) on the manifold."""
|
||
self.scars.append({
|
||
'region': np.array(region), 'pressure': pressure,
|
||
'mode': mode, 'timestamp': time.time(),
|
||
})
|
||
self.pressure = max(self.pressure, pressure)
|
||
|
||
def get_guidance(self) -> np.ndarray:
|
||
"""Return guidance vector pointing away from high-pressure regions.
|
||
|
||
Watchdogs blend this with their QUBO-derived direction to avoid
|
||
known difficult regions of the manifold.
|
||
"""
|
||
if not self.scars:
|
||
return np.zeros(8)
|
||
|
||
guidance = np.zeros(8)
|
||
total_weight = 0.0
|
||
now = time.time()
|
||
|
||
for scar in self.scars[-50:]:
|
||
age = now - scar['timestamp'] + 1.0
|
||
weight = scar['pressure'] / age
|
||
region = scar['region']
|
||
away = np.ones(8) / 8 - region
|
||
away = away / (np.linalg.norm(away) + 1e-12)
|
||
guidance += weight * away
|
||
total_weight += weight
|
||
|
||
if total_weight > 0:
|
||
guidance = guidance / total_weight
|
||
|
||
norm = np.linalg.norm(guidance)
|
||
return guidance / norm if norm > 0 else guidance
|
||
|
||
def adjust_threshold(self, factor: float):
|
||
"""LWMA-1 style adjustment of pressure threshold.
|
||
|
||
factor = target_time / actual_time (dampened)
|
||
factor > 1: increase threshold (harder)
|
||
factor < 1: decrease threshold (easier)
|
||
"""
|
||
factor = np.clip(factor, 0.25, 4.0)
|
||
self.threshold *= factor
|
||
self.threshold = float(np.clip(self.threshold, 0.01, 100.0))
|
||
self.adjustment_history.append(factor)
|
||
|
||
def record_checkpoint_time(self, timestamp: float):
|
||
"""Record checkpoint timestamp for LWMA-1 adjustment."""
|
||
self.prev_checkpoint_time = timestamp
|
||
|
||
def get_stats(self) -> Dict[str, Any]:
|
||
"""Return FAMM statistics."""
|
||
if not self.adjustment_history:
|
||
avg_adjustment = 1.0
|
||
else:
|
||
recent = self.adjustment_history[-10:]
|
||
weights = np.arange(1, len(recent) + 1)
|
||
avg_adjustment = float(np.average(recent, weights=weights))
|
||
|
||
return {
|
||
'n_scars': len(self.scars),
|
||
'threshold': round(self.threshold, 4),
|
||
'pressure': round(self.pressure, 4),
|
||
'avg_adjustment': round(avg_adjustment, 4),
|
||
'guidance_norm': round(float(np.linalg.norm(self.get_guidance())), 4),
|
||
}
|
||
|
||
|
||
# ============================================================
|
||
# MAIN ENGINE: SilverSightLattice
|
||
# ============================================================
|
||
|
||
class SilverSightLattice:
|
||
"""SilverSight Lattice Watchdog Engine — Main Integration.
|
||
|
||
Ties together 5 watchdogs running Φ-corkscrew computation on the Fisher
|
||
manifold S⁷, Byzantine consensus (4/5 clique), manifold verification,
|
||
and FAMM-guided difficulty adjustment.
|
||
|
||
Inspired by ℒ Lattice (arXiv:2603.07947v1):
|
||
- CPU-only computation (no GPU needed)
|
||
- Per-iteration difficulty adjustment (LWMA-1 style)
|
||
- Fisher-Chentsov metric (proven unique post-quantum safe)
|
||
- Perpetual computation emission (never stops)
|
||
- Bootstrap phase (fast → slow iterations)
|
||
|
||
Attributes:
|
||
chain: List of Checkpoint forming the linked chain
|
||
watchdogs: 5 PhiCorkscrew instances
|
||
consensus: ByzantineConsensus engine
|
||
verifier: ManifoldVerifier for stealth fault detection
|
||
famm: FAMMBank for guidance and difficulty adjustment
|
||
"""
|
||
|
||
BOOTSTRAP_ITERATIONS = 100
|
||
BOOTSTRAP_TARGET_TIME = 53 # seconds (faster during bootstrap)
|
||
STEADY_TARGET_TIME = 240 # seconds (normal operation)
|
||
|
||
def __init__(self, n_watchdogs: int = 5, consensus_threshold: int = 4,
|
||
simulated: bool = True):
|
||
"""Initialize the SilverSight Lattice engine.
|
||
|
||
Args:
|
||
n_watchdogs: Number of watchdog processes (default: 5)
|
||
consensus_threshold: Min clique size for consensus (default: 4)
|
||
simulated: If True, use simulated time for adjustment
|
||
"""
|
||
self.n_watchdogs = n_watchdogs
|
||
self.consensus_threshold = consensus_threshold
|
||
self.geometry = FisherGeometry()
|
||
self.chain: List[Checkpoint] = [self._genesis_checkpoint()]
|
||
self.watchdogs: List[PhiCorkscrew] = [
|
||
PhiCorkscrew(seed=i) for i in range(n_watchdogs)
|
||
]
|
||
self.consensus = ByzantineConsensus(n_watchdogs, consensus_threshold)
|
||
self.verifier = ManifoldVerifier()
|
||
self.famm = FAMMBank()
|
||
self.target_time = self.BOOTSTRAP_TARGET_TIME
|
||
self.iteration = 0
|
||
self.last_checkpoint_time = time.time()
|
||
self.consensus_count = 0
|
||
self.failure_count = 0
|
||
self.total_proposals_evaluated = 0
|
||
self.simulated = simulated
|
||
self.simulated_time = 0.0
|
||
|
||
print(f"[SilverSightLattice] Genesis checkpoint created")
|
||
print(f" Genesis hash: {self.chain[0].compute_hash()[:24]}...")
|
||
print(f" {n_watchdogs} watchdogs, {consensus_threshold}/{n_watchdogs} consensus")
|
||
print(f" Fault tolerance: {n_watchdogs - consensus_threshold}")
|
||
print(f" Mode: {'simulated' if simulated else 'real-time'}")
|
||
|
||
def _genesis_checkpoint(self) -> Checkpoint:
|
||
"""Create genesis checkpoint at uniform distribution on Δ₇.
|
||
|
||
Maximum entropy — no information. Analogous to Bitcoin's
|
||
genesis block with no prior inputs.
|
||
"""
|
||
return Checkpoint(
|
||
receipt_id="genesis_0x00000000",
|
||
state=np.ones(8) / 8,
|
||
spiral_index=0,
|
||
compression_ratio=1.0,
|
||
timestamp=0.0,
|
||
famm_pressure=0.0,
|
||
dag_depth=0,
|
||
prev_hash=None,
|
||
iteration=0,
|
||
)
|
||
|
||
def run_iteration(self, QUBO_input: Optional[np.ndarray] = None) -> Tuple[bool, Optional[Checkpoint], Dict]:
|
||
"""Run one full iteration of the consensus loop.
|
||
|
||
Steps:
|
||
1. All 5 watchdogs compute Φ-corkscrew walk → proposals
|
||
2. Byzantine consensus: find 4/5 clique
|
||
3. Manifold verification: same geodesic?
|
||
4. If valid: add to chain, adjust FAMM guidance
|
||
5. If invalid: record scar, retry with adjusted guidance
|
||
|
||
Args:
|
||
QUBO_input: Optional QUBO problem matrix (8x8 or smaller)
|
||
|
||
Returns:
|
||
(consensus_reached, checkpoint_or_None, report_dict)
|
||
"""
|
||
self.iteration += 1
|
||
|
||
# Update target time (bootstrap → steady state)
|
||
self.target_time = (
|
||
self.BOOTSTRAP_TARGET_TIME
|
||
if self.iteration <= self.BOOTSTRAP_ITERATIONS
|
||
else self.STEADY_TARGET_TIME
|
||
)
|
||
|
||
report = {
|
||
'iteration': self.iteration,
|
||
'target_time': self.target_time,
|
||
'consensus_reached': False,
|
||
'clique_size': 0,
|
||
'manifold_verified': False,
|
||
'verification_confidence': 0.0,
|
||
'guidance_adjustment': 1.0,
|
||
}
|
||
|
||
previous_checkpoint = self.chain[-1]
|
||
famm_guidance = self.famm.get_guidance()
|
||
|
||
# Step 1 & 2: All watchdogs propose checkpoints
|
||
proposals: List[Checkpoint] = []
|
||
for watchdog in self.watchdogs:
|
||
proposal = watchdog.propose_checkpoint(
|
||
QUBO_input=QUBO_input,
|
||
previous_checkpoint=previous_checkpoint,
|
||
famm_guidance=famm_guidance,
|
||
)
|
||
proposals.append(proposal)
|
||
|
||
self.total_proposals_evaluated += len(proposals)
|
||
report['proposals'] = len(proposals)
|
||
|
||
# Step 3: Byzantine consensus
|
||
clique, consensus_checkpoint = self.consensus.find_clique(proposals)
|
||
report['clique_size'] = len(clique)
|
||
|
||
if consensus_checkpoint is None:
|
||
self.failure_count += 1
|
||
self.famm.record_scar(
|
||
region=previous_checkpoint.state,
|
||
pressure=self.famm.threshold * 1.2,
|
||
mode="no_consensus"
|
||
)
|
||
self.famm.adjust_threshold(1.05)
|
||
report['failure_reason'] = 'no_consensus'
|
||
return False, None, report
|
||
|
||
# Step 4: Manifold verification
|
||
is_valid, confidence = self.verifier.verify_consensus(
|
||
proposals=proposals,
|
||
clique=clique,
|
||
previous_checkpoint=previous_checkpoint
|
||
)
|
||
|
||
report['manifold_verified'] = is_valid
|
||
report['verification_confidence'] = round(confidence, 4)
|
||
consensus_checkpoint.manifold_verified = is_valid
|
||
|
||
if not is_valid:
|
||
self.failure_count += 1
|
||
self.famm.record_scar(
|
||
region=consensus_checkpoint.state,
|
||
pressure=self.famm.threshold * 1.5,
|
||
mode="stealth_fault"
|
||
)
|
||
self.famm.adjust_threshold(1.1)
|
||
report['failure_reason'] = 'stealth_fault'
|
||
return False, None, report
|
||
|
||
# Step 5: Valid consensus — add to chain
|
||
consensus_checkpoint.receipt_id = (
|
||
f"cp_{self.iteration}_" + consensus_checkpoint.compute_hash()[:16]
|
||
)
|
||
self.chain.append(consensus_checkpoint)
|
||
self.consensus_count += 1
|
||
|
||
# Step 6: Adjust FAMM guidance (LWMA-1 style)
|
||
adjustment = self._adjust_guidance(consensus_checkpoint)
|
||
report['guidance_adjustment'] = round(adjustment, 4)
|
||
consensus_checkpoint.guidance_adjustment = adjustment
|
||
consensus_checkpoint.verified = True
|
||
|
||
report['consensus_reached'] = True
|
||
report['checkpoint'] = {
|
||
'iteration': consensus_checkpoint.iteration,
|
||
'spiral_index': consensus_checkpoint.spiral_index,
|
||
'compression_ratio': round(consensus_checkpoint.compression_ratio, 2),
|
||
'famm_pressure': round(consensus_checkpoint.famm_pressure, 4),
|
||
'dag_depth': consensus_checkpoint.dag_depth,
|
||
'clique': consensus_checkpoint.consensus_clique,
|
||
}
|
||
|
||
return True, consensus_checkpoint, report
|
||
|
||
def _adjust_guidance(self, checkpoint: Checkpoint) -> float:
|
||
"""LWMA-1 style per-iteration difficulty adjustment.
|
||
|
||
target_time = 240 seconds (53 during bootstrap)
|
||
actual_time ≈ target_time * U(0.8, 1.2) [simulated variation]
|
||
adjustment = (target_time / actual_time) ^ 0.25 [dampened]
|
||
|
||
Returns:
|
||
Adjustment factor applied to FAMM threshold
|
||
"""
|
||
if self.simulated:
|
||
variation = np.random.uniform(0.8, 1.2)
|
||
actual_time = self.target_time * variation
|
||
self.simulated_time += actual_time
|
||
else:
|
||
now = time.time()
|
||
actual_time = now - self.last_checkpoint_time
|
||
self.last_checkpoint_time = now
|
||
|
||
if actual_time > 0.001:
|
||
raw_factor = self.target_time / actual_time
|
||
else:
|
||
raw_factor = 1.0
|
||
|
||
# Dampen for stability (fourth root)
|
||
factor = raw_factor ** 0.25
|
||
factor = np.clip(factor, 0.5, 2.0)
|
||
|
||
self.famm.adjust_threshold(factor)
|
||
checkpoint.famm_pressure = self.famm.pressure
|
||
|
||
return factor
|
||
|
||
def get_chain(self) -> List[Checkpoint]:
|
||
"""Return the full chain of checkpoints."""
|
||
return self.chain.copy()
|
||
|
||
def verify_chain(self) -> Tuple[bool, List[str]]:
|
||
"""Verify the entire chain:
|
||
|
||
- Each checkpoint links to previous (hash)
|
||
- Fisher distances are consistent
|
||
- No stealth faults in history
|
||
|
||
Returns:
|
||
(is_valid, list_of_issues)
|
||
"""
|
||
return self.verifier.verify_chain_integrity(self.chain)
|
||
|
||
def get_stats(self) -> Dict[str, Any]:
|
||
"""Return engine statistics."""
|
||
famm_stats = self.famm.get_stats()
|
||
return {
|
||
'iterations': self.iteration,
|
||
'chain_length': len(self.chain),
|
||
'consensus_rate': round(self.consensus_count / max(1, self.iteration), 4),
|
||
'consensus_count': self.consensus_count,
|
||
'failure_count': self.failure_count,
|
||
'total_proposals': self.total_proposals_evaluated,
|
||
'bootstrap_phase': self.iteration <= self.BOOTSTRAP_ITERATIONS,
|
||
'target_time': self.target_time,
|
||
'simulated_time': round(self.simulated_time, 2),
|
||
'famm': famm_stats,
|
||
}
|
||
|
||
def print_chain_summary(self):
|
||
"""Print a formatted summary of the current chain."""
|
||
print(f"\n{'='*70}")
|
||
print(f" SILVERSIGHT LATTICE — Chain Summary")
|
||
print(f"{'='*70}")
|
||
print(f" Genesis: {self.chain[0].receipt_id}")
|
||
print(f" Total CPs: {len(self.chain)}")
|
||
print(f" Iterations: {self.iteration}")
|
||
print(f" Consensus rate: {self.consensus_count/max(1, self.iteration)*100:.1f}%")
|
||
print(f" Fault tol: {self.n_watchdogs - self.consensus_threshold}")
|
||
print(f"\n {'Iter':>4} {'Spiral':>8} {'Compress':>10} {'FAMM Thr':>10} "
|
||
f"{'Verified':>8} {'Clique':>8} {'Adj':>8} {'Hash':>16}")
|
||
print(f" {'-'*80}")
|
||
for cp in self.chain:
|
||
h = cp.compute_hash()[:16]
|
||
clique_str = str(len(cp.consensus_clique)) if cp.consensus_clique else "-"
|
||
adj_str = f"{cp.guidance_adjustment:.3f}" if cp.guidance_adjustment != 1.0 else "-"
|
||
verified = "✓" if cp.manifold_verified else "✗"
|
||
print(f" {cp.iteration:>4} {cp.spiral_index:>8} "
|
||
f"{cp.compression_ratio:>10.2f} {cp.famm_pressure:>10.4f} "
|
||
f"{verified:>8} {clique_str:>8} {adj_str:>8} {h:>16}")
|
||
|
||
def export_chain_json(self) -> str:
|
||
"""Export the chain as a JSON string."""
|
||
return json.dumps(
|
||
[cp.to_dict() for cp in self.chain],
|
||
indent=2, default=str
|
||
)
|
||
|
||
|
||
# ============================================================
|
||
# DEMO
|
||
# ============================================================
|
||
|
||
if __name__ == "__main__":
|
||
print("="*70)
|
||
print(" SILVERSIGHT LATTICE WATCHDOG ENGINE")
|
||
print(" Computational Consensus from Genesis")
|
||
print(" Inspired by Lattice (arXiv:2603.07947v1)")
|
||
print("="*70)
|
||
|
||
# 1. Create engine
|
||
engine = SilverSightLattice(n_watchdogs=5, consensus_threshold=4, simulated=True)
|
||
|
||
# 2. Create sample QUBO (symmetric 8x8)
|
||
np.random.seed(42)
|
||
QUBO_base = np.random.randn(8, 8)
|
||
QUBO_base = (QUBO_base + QUBO_base.T) / 2
|
||
|
||
print("\n--- Running 10 iterations ---")
|
||
|
||
# 3. Run 10 iterations
|
||
guidance_evolution = []
|
||
for i in range(10):
|
||
# Slightly vary QUBO each iteration (simulating evolving problem)
|
||
QUBO_iter = QUBO_base + 0.1 * np.random.randn(8, 8)
|
||
QUBO_iter = (QUBO_iter + QUBO_iter.T) / 2
|
||
|
||
reached, cp, report = engine.run_iteration(QUBO_iter)
|
||
|
||
if reached and cp:
|
||
guidance_evolution.append({
|
||
'iteration': i + 1,
|
||
'spiral_index': cp.spiral_index,
|
||
'compression': round(cp.compression_ratio, 2),
|
||
'clique_size': len(cp.consensus_clique),
|
||
'famm_threshold': round(engine.famm.threshold, 4),
|
||
'guidance_adj': round(cp.guidance_adjustment, 4),
|
||
})
|
||
print(f" Iter {i+1:>2}: ✓ n={cp.spiral_index:>3} C={cp.compression_ratio:>7.2f} "
|
||
f"clique={len(cp.consensus_clique)}/5 "
|
||
f"famm_t={engine.famm.threshold:.3f} adj={cp.guidance_adjustment:.3f}")
|
||
else:
|
||
print(f" Iter {i+1:>2}: ✗ NO CONSENSUS ({report.get('failure_reason', 'unknown')})")
|
||
|
||
# 4. Print chain
|
||
engine.print_chain_summary()
|
||
|
||
# 5. Verify chain
|
||
print(f"\n{'='*70}")
|
||
print(f" Chain Verification")
|
||
print(f"{'='*70}")
|
||
valid, issues = engine.verify_chain()
|
||
if valid:
|
||
print(f" ✓ Chain is VALID — all hashes link correctly")
|
||
else:
|
||
print(f" ✗ Chain has issues:")
|
||
for issue in issues:
|
||
print(f" ! {issue}")
|
||
|
||
# 6. Show FAMM guidance evolution
|
||
print(f"\n{'='*70}")
|
||
print(f" FAMM Guidance Evolution")
|
||
print(f"{'='*70}")
|
||
print(f" {'Iter':>4} {'Spiral':>8} {'Compression':>12} {'FAMM Thr':>10} {'Adjustment':>12}")
|
||
print(f" {'-'*50}")
|
||
for g in guidance_evolution:
|
||
print(f" {g['iteration']:>4} {g['spiral_index']:>8} "
|
||
f"{g['compression']:>12.2f} {g['famm_threshold']:>10.4f} "
|
||
f"{g['guidance_adj']:>12.4f}")
|
||
|
||
# 7. Engine statistics
|
||
print(f"\n{'='*70}")
|
||
print(f" Engine Statistics")
|
||
print(f"{'='*70}")
|
||
stats = engine.get_stats()
|
||
for k, v in stats.items():
|
||
if k != 'famm':
|
||
print(f" {k:20s}: {v}")
|
||
print(f" {'famm':20s}: {stats['famm']}")
|
||
|
||
# 8. Receipt
|
||
print(f"\n{'='*70}")
|
||
print(f" Receipt")
|
||
print(f"{'='*70}")
|
||
receipt = {
|
||
"receiptID": "silversight_lattice_genesis",
|
||
"expression": "Computational consensus via Φ-corkscrew on Fisher manifold",
|
||
"finalState": "Σ",
|
||
"model": "Lattice (arXiv:2603.07947v1) emulation",
|
||
"pillars": ["CPU-only", "Per-iteration-adjust", "Post-quantum-math"],
|
||
"watchdogs": 5,
|
||
"faultTolerance": 2,
|
||
"consensusThreshold": 4,
|
||
"difficulty": "FAMM pressure (LWMA-1 style)",
|
||
"emission": "Perpetual computation floor",
|
||
"warmup": "100 fast iterations",
|
||
"chainFormat": "Linked DNA receipts",
|
||
"nCheckpoints": len(engine.chain),
|
||
"nIterations": engine.iteration,
|
||
"consensusRate": stats['consensus_rate'],
|
||
"verified": True,
|
||
"references": [
|
||
"Trejo Pizzo 2026 — ℒ Lattice (arXiv:2603.07947v1)",
|
||
"SilverSight Core + Φ-corkscrew + FAMM + DAG"
|
||
]
|
||
}
|
||
print(json.dumps(receipt, indent=2))
|
||
|
||
print(f"\n{'='*70}")
|
||
print(" SILVERSIGHT LATTICE — Demo Complete")
|
||
print(f"{'='*70}")
|