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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)
1029 lines
37 KiB
Python
1029 lines
37 KiB
Python
#!/usr/bin/env python3
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"""
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BYZANTINE CONSENSUS PROTOCOL
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SilverSight Lattice Quintuplet Watchdog System
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5-way Byzantine consensus for Φ-corkscrew computation watchdogs.
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Inspired by ℒ Lattice cryptocurrency consensus model (arXiv:2603.07947v1).
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Architecture: 5 ARM64 watchdog processes run identical Φ-corkscrew computations
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on the Fisher manifold S⁷. They propose checkpoints and must agree (4/5) on
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the result, tolerating up to 2 Byzantine faults.
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Classes:
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Checkpoint -- a single checkpoint on the manifold
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DAG -- directed acyclic graph of checkpoints
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ByzantineConsensus -- 5-way consensus with 2-fault tolerance
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Usage:
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from quintuplet_consensus import Checkpoint, DAG, ByzantineConsensus
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consensus = ByzantineConsensus(n_processes=5, threshold=4)
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result, clique, fault_report = consensus.run_consensus(proposals)
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"""
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from __future__ import annotations
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import hashlib
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import json
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import math
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import time
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from copy import deepcopy
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from dataclasses import dataclass, field
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from enum import Enum, auto
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from typing import Any, Optional
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# ---------------------------------------------------------------------------
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# Constants
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# ---------------------------------------------------------------------------
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PHI = (1 + 5**0.5) / 2 # Golden ratio, governs Φ-corkscrew geometry
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class FaultType(Enum):
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"""Classification of Byzantine faults on the Fisher manifold."""
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NONE = auto() # Process is healthy and agrees
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CRASH = auto() # Process stopped responding (no proposal)
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BIT_FLIP = auto() # Single-bit corruption in state or index
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DETERMINISM_FAILURE = auto() # Nondeterministic computation (different result)
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STEALTH_FAULT = auto() # Near-correct result designed to evade detection
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GODEL_BOUNDARY = auto() # Hit undecidable region / self-referential paradox
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FAMM_CORRUPTION = auto() # FAMM pressure bank corrupted
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def __str__(self) -> str:
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return self.name
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class ConsensusResult(Enum):
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"""Outcome of the consensus round."""
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VALID = auto() # ≥4 processes agree; consensus reached
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TIED = auto() # No clique ≥4 (e.g., 2-vs-3 split or worse)
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INVALID = auto() # Clique found but manifold check failed
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NO_QUORUM = auto() # Too many crash faults to form any clique
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def __str__(self) -> str:
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return self.name
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# ---------------------------------------------------------------------------
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# Checkpoint
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# ---------------------------------------------------------------------------
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class Checkpoint:
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"""A single checkpoint on the Fisher manifold S⁷.
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Each checkpoint records the state of one Φ-corkscrew computation step:
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- state: Hachimoji probability distribution on Δ₇
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- spiral_index: integer position on the Φ-corkscrew
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- compression_ratio: original_size / compressed_size
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- timestamp: Unix epoch (seconds)
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- famm_pressure: FAMM bank pressure at this step
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- dag: DAG of prior checkpoints leading here
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- prev_hash: SHA-256 of the previous checkpoint receipt
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The checkpoint is content-addressed: its receipt_hash is a SHA-256
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digest over all canonical fields, making any tamper evident.
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"""
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def __init__(
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self,
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state: list[float],
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spiral_index: int,
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compression_ratio: float,
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timestamp: float,
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famm_pressure: float,
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dag: "DAG",
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prev_hash: Optional[str] = None,
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):
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self.state = tuple(float(x) for x in state) # immutable
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self.spiral_index = int(spiral_index)
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self.compression_ratio = float(compression_ratio)
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self.timestamp = float(timestamp)
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self.famm_pressure = float(famm_pressure)
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self.dag = dag # shared DAG reference
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self.prev_hash = prev_hash
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self._hash: Optional[str] = None # lazy
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# -- canonical serialisation for hashing -------------------------------
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def canonical(self) -> dict[str, Any]:
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"""Return a JSON-serialisable dict that uniquely describes
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the semantically-significant fields of this checkpoint."""
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return {
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"state": [round(x, 12) for x in self.state],
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"spiral_index": self.spiral_index,
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"compression_ratio": round(self.compression_ratio, 8),
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"timestamp": round(self.timestamp, 6),
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"famm_pressure": round(self.famm_pressure, 8),
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"prev_hash": self.prev_hash,
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}
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def receipt_hash(self) -> str:
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"""SHA-256 content hash (receipt ID). Lazily computed."""
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if self._hash is None:
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payload = json.dumps(self.canonical(), sort_keys=True, separators=(",", ":"))
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self._hash = hashlib.sha256(payload.encode()).hexdigest()
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return self._hash
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# -- comparison for consensus ------------------------------------------
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def agrees_with(self, other: "Checkpoint", epsilon: float = 1e-6) -> bool:
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"""Structural agreement: two checkpoints are "the same result"
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when they land on the same manifold position with the same
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compression characteristics.
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Checks (in order of cost):
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1. Spiral index equality (fast path)
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2. Compression ratio within epsilon
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3. FAMM pressure within epsilon
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4. State vector L2 distance within epsilon
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5. DAG node count match
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6. DAG edge count match (topological proxy)
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"""
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if not isinstance(other, Checkpoint):
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return False
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# 1. Spiral index — injective on S⁷ for large n
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if self.spiral_index != other.spiral_index:
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return False
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# 2. Compression ratio
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if abs(self.compression_ratio - other.compression_ratio) > epsilon:
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return False
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# 3. FAMM pressure
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if abs(self.famm_pressure - other.famm_pressure) > epsilon:
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return False
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# 4. State vector distance on S⁷
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if len(self.state) != len(other.state):
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return False
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dist = math.sqrt(
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sum((a - b) ** 2 for a, b in zip(self.state, other.state))
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)
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if dist > epsilon:
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return False
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# 5. DAG topology proxy — node count
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if len(self.dag.nodes) != len(other.dag.nodes):
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return False
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# 6. DAG edge count (lightweight structural check)
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self_edges = len(self.dag.edges)
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other_edges = len(other.dag.edges)
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if self_edges != other_edges:
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return False
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return True
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# -- utilities ---------------------------------------------------------
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def copy(self, new_dag: Optional["DAG"] = None) -> "Checkpoint":
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"""Deep copy for fault-injection experiments.
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Args:
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new_dag: if provided, attach this DAG instead of copying.
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This breaks infinite recursion in DAG.copy().
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"""
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return Checkpoint(
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state=list(self.state),
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spiral_index=self.spiral_index,
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compression_ratio=self.compression_ratio,
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timestamp=self.timestamp,
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famm_pressure=self.famm_pressure,
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dag=new_dag if new_dag is not None else self.dag,
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prev_hash=self.prev_hash,
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)
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def __repr__(self) -> str:
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return (
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f"Checkpoint(hash={self.receipt_hash()[:16]}..., "
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f"n={self.spiral_index}, C={self.compression_ratio:.2f}, "
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f"FAMM={self.famm_pressure:.4f})"
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)
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def to_receipt(self, verified: bool = False) -> dict[str, Any]:
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"""Emit the JSON receipt format used by SilverSight Lattice."""
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return {
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"receiptID": self.receipt_hash(),
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"expression": "QUBO(n) via Φ-corkscrew on S⁷",
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"finalState": list(self.state),
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"spiralIndex": self.spiral_index,
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"compressionRatio": self.compression_ratio,
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"timestamp": self.timestamp,
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"fammPressure": self.famm_pressure,
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"dagDepth": len(self.dag.nodes),
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"prevReceipt": self.prev_hash,
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"manifoldVerified": verified,
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"guidanceAdjustment": 1.0,
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"verified": verified,
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}
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# ---------------------------------------------------------------------------
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# DAG
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# ---------------------------------------------------------------------------
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class DAG:
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"""Directed acyclic graph of checkpoints.
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Each node is a Checkpoint; edges represent geodesic transitions
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on the Fisher manifold (i.e. γ(t_i) → γ(t_{i+1}) ).
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The DAG is kept acyclic by construction: edges only point from
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older checkpoints to newer ones (timestamp ordering).
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"""
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def __init__(self) -> None:
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self.nodes: dict[str, Checkpoint] = {} # receipt_hash → Checkpoint
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self.edges: set[tuple[str, str]] = set() # (parent_hash, child_hash)
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self._topo_order: list[str] = [] # cached topological order
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self._dirty: bool = True
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# -- mutation ----------------------------------------------------------
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def add_node(self, checkpoint: Checkpoint) -> str:
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"""Add a checkpoint as a node. Returns its receipt hash.
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If the checkpoint links to a previous hash, an edge is
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automatically created (prev_hash → checkpoint.hash).
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"""
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h = checkpoint.receipt_hash()
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self.nodes[h] = checkpoint
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self._dirty = True
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if checkpoint.prev_hash is not None:
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self.add_edge(checkpoint.prev_hash, h)
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return h
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def add_edge(self, parent: str, child: str) -> None:
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"""Add a directed edge parent → child. Raises ValueError
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if the edge would create a cycle."""
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if parent not in self.nodes or child not in self.nodes:
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raise KeyError("Both endpoints must be added as nodes first")
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if parent == child:
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raise ValueError("Self-loops are forbidden")
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# Quick cycle test: if child can already reach parent, adding
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# parent → child would close a cycle.
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if self._can_reach(child, parent):
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raise ValueError(f"Edge {parent} → {child} would create a cycle")
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self.edges.add((parent, child))
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self._dirty = True
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def _can_reach(self, source: str, target: str) -> bool:
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"""DFS reachability test (used for cycle detection)."""
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stack = [source]
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seen = set()
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while stack:
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cur = stack.pop()
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if cur == target:
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return True
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if cur in seen:
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continue
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seen.add(cur)
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for p, c in self.edges:
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if p == cur:
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stack.append(c)
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return False
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# -- queries -----------------------------------------------------------
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def get_chain(self) -> list[Checkpoint]:
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"""Return the primary chain (longest path) through the DAG.
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For a single-parent chain this is simply the topological order;
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for DAGs with merges the longest path heuristic is used.
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"""
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topo = self._topological_order()
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if not topo:
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return []
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# longest path DP
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dist: dict[str, int] = {h: 1 for h in topo}
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pred: dict[str, Optional[str]] = {h: None for h in topo}
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for h in topo:
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for p, c in self.edges:
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if c == h and p in dist:
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if dist[p] + 1 > dist[h]:
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dist[h] = dist[p] + 1
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pred[h] = p
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# reconstruct
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tail = max(dist, key=lambda k: dist[k])
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chain: list[Checkpoint] = []
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cur: Optional[str] = tail
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while cur is not None:
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chain.append(self.nodes[cur])
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cur = pred[cur]
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chain.reverse()
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return chain
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def get_ancestors(self, receipt_hash: str) -> set[str]:
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"""All ancestor hashes reachable from the given node."""
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stack = [receipt_hash]
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seen: set[str] = set()
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while stack:
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cur = stack.pop()
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if cur in seen:
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continue
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seen.add(cur)
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for p, c in self.edges:
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if c == cur:
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stack.append(p)
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return seen
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def _topological_order(self) -> list[str]:
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"""Kahn's algorithm, cached."""
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if not self._dirty and self._topo_order:
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return self._topo_order
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in_degree: dict[str, int] = {h: 0 for h in self.nodes}
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adj: dict[str, list[str]] = {h: [] for h in self.nodes}
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for p, c in self.edges:
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adj[p].append(c)
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in_degree[c] = in_degree.get(c, 0) + 1
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queue = [h for h, d in in_degree.items() if d == 0]
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order: list[str] = []
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while queue:
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u = queue.pop(0)
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order.append(u)
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for v in adj[u]:
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in_degree[v] -= 1
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if in_degree[v] == 0:
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queue.append(v)
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if len(order) != len(self.nodes):
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raise RuntimeError("Cycle detected in DAG — this should never happen")
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self._topo_order = order
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self._dirty = False
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return order
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# -- comparison helpers ------------------------------------------------
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def isomorphic_to(self, other: "DAG", epsilon: float = 1e-6) -> bool:
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"""Structural isomorphism check.
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For the consensus use-case we do not need full graph isomorphism;
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it suffices to compare:
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- node count
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- edge count
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- checksum of all receipt hashes (order-independent)
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- compression ratio fingerprint of chain
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"""
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if len(self.nodes) != len(other.nodes):
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return False
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if len(self.edges) != len(other.edges):
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return False
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# Hash multiset comparison — cheap and deterministic
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self_hashes = sorted(self.nodes.keys())
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other_hashes = sorted(other.nodes.keys())
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if self_hashes != other_hashes:
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return False
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return True
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def copy(self) -> "DAG":
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"""Deep copy. Avoids infinite recursion by pre-creating the
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target DAG and passing it to Checkpoint.copy()."""
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d = DAG()
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# First pass: copy all checkpoints into the new DAG
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for cp in self.nodes.values():
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cp_copy = cp.copy(new_dag=d)
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d.nodes[cp_copy.receipt_hash()] = cp_copy
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d._dirty = True
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# Second pass: reconstruct edges from prev_hash links
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for h, cp in d.nodes.items():
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if cp.prev_hash is not None and cp.prev_hash in d.nodes:
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d.edges.add((cp.prev_hash, h))
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return d
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def __repr__(self) -> str:
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return f"DAG(nodes={len(self.nodes)}, edges={len(self.edges)})"
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# ---------------------------------------------------------------------------
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# Byzantine Consensus
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# ---------------------------------------------------------------------------
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class ByzantineConsensus:
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"""5-way Byzantine consensus with 2-fault tolerance.
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The protocol:
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1. Collect proposals from all 5 watchdogs.
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2. Build a 5×5 agreement matrix (bool) using Checkpoint.agrees_with.
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3. Find the largest fully-connected agreeing clique.
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4. If |clique| ≥ threshold (default 4): VALID consensus.
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5. Otherwise: analyse fault pattern and report.
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Fault classes detected:
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CRASH — no proposal received
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BIT_FLIP — spiral index off by small power-of-two
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DETERMINISM_FAILURE — completely different non-erroneous result
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STEALTH_FAULT — barely outside epsilon, looks almost correct
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GODEL_BOUNDARY — spiral_index ≈ 0 or pressure ≈ 1.0 (boundary)
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FAMM_CORRUPTION — pressure inconsistent with DAG depth
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Attributes:
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n_processes (int): total watchdogs (default 5)
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threshold (int): minimum clique size for VALID (default 4)
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"""
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def __init__(self, n_processes: int = 5, threshold: int = 4):
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if n_processes < threshold:
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raise ValueError("threshold cannot exceed n_processes")
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self.n_processes = n_processes
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self.threshold = threshold
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self._agreement_matrix: list[list[bool]] = []
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self._last_proposals: dict[int, Optional[Checkpoint]] = {}
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# -- proposal collection -----------------------------------------------
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def propose(self, process_id: int, checkpoint: Checkpoint) -> None:
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"""Register a checkpoint proposal from a watchdog.
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Args:
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process_id: integer 0 … n_processes-1
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checkpoint: the proposed Checkpoint
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"""
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if not (0 <= process_id < self.n_processes):
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raise ValueError(f"process_id must be in [0, {self.n_processes})")
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self._last_proposals[process_id] = checkpoint
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# -- DAG comparison ----------------------------------------------------
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def compare_dags(self, dag_i: DAG, dag_j: DAG, epsilon: float = 1e-6) -> bool:
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"""Compare two DAGs for equivalence.
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Returns True iff the DAGs are structurally isomorphic AND every
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pairwise node agrees within epsilon.
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"""
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if not dag_i.isomorphic_to(dag_j, epsilon):
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return False
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# Deep check: each corresponding node must agree
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for h in dag_i.nodes:
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if h not in dag_j.nodes:
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return False
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if not dag_i.nodes[h].agrees_with(dag_j.nodes[h], epsilon):
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return False
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return True
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# -- core algorithm: clique finding ------------------------------------
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def find_consensus_clique(
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self, proposals: dict[int, Optional[Checkpoint]], epsilon: float = 1e-6
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) -> list[int]:
|
||
"""Find the largest fully-connected clique of agreeing processes.
|
||
|
||
Uses brute-force enumeration (optimal for n≤5). Returns the
|
||
lexicographically-smallest largest clique.
|
||
|
||
Args:
|
||
proposals: mapping process_id → Checkpoint or None
|
||
epsilon: numerical tolerance for agreement
|
||
|
||
Returns:
|
||
List of process IDs forming the largest clique.
|
||
"""
|
||
active = [pid for pid, cp in proposals.items() if cp is not None]
|
||
m = len(active)
|
||
if m == 0:
|
||
return []
|
||
|
||
# Build agreement adjacency among active processes
|
||
agree: dict[int, set[int]] = {pid: set() for pid in active}
|
||
for i, pi in enumerate(active):
|
||
for j, pj in enumerate(active):
|
||
if i == j:
|
||
agree[pi].add(pi)
|
||
continue
|
||
cp_i = proposals[pi]
|
||
cp_j = proposals[pj]
|
||
if cp_i is not None and cp_j is not None and cp_i.agrees_with(cp_j, epsilon):
|
||
agree[pi].add(pj)
|
||
|
||
# Store matrix for diagnostics
|
||
self._agreement_matrix = [
|
||
[pj in agree[pi] for pj in active] for pi in active
|
||
]
|
||
|
||
# Brute-force: enumerate all subsets of active processes
|
||
best: list[int] = []
|
||
from itertools import combinations
|
||
|
||
for size in range(m, 0, -1):
|
||
found = False
|
||
for subset in combinations(sorted(active), size):
|
||
subset_set = set(subset)
|
||
if all(
|
||
subset_set <= agree[pid] # pid agrees with everyone in subset
|
||
for pid in subset
|
||
):
|
||
best = list(subset)
|
||
found = True
|
||
break
|
||
if found:
|
||
break
|
||
|
||
return best
|
||
|
||
# -- full protocol -----------------------------------------------------
|
||
|
||
def run_consensus(
|
||
self,
|
||
proposals: dict[int, Optional[Checkpoint]],
|
||
epsilon: float = 1e-6,
|
||
) -> tuple[ConsensusResult, list[int], dict[str, Any]]:
|
||
"""Execute the full 5-way consensus protocol.
|
||
|
||
Steps:
|
||
1. Collect all proposals (None = crash fault)
|
||
2. Build agreement matrix
|
||
3. Find largest consensus clique
|
||
4. Classify faults for out-of-clique processes
|
||
5. Return (result, clique, fault_report)
|
||
|
||
Args:
|
||
proposals: mapping process_id → Checkpoint or None
|
||
epsilon: numerical tolerance
|
||
|
||
Returns:
|
||
(ConsensusResult, clique_pids, fault_report_dict)
|
||
"""
|
||
t0 = time.monotonic()
|
||
self._last_proposals = dict(proposals)
|
||
|
||
# Step 1 — normalise to all expected processes
|
||
full: dict[int, Optional[Checkpoint]] = {
|
||
pid: proposals.get(pid) for pid in range(self.n_processes)
|
||
}
|
||
crash_ids = [pid for pid, cp in full.items() if cp is None]
|
||
|
||
# Step 2 & 3 — find clique
|
||
clique = self.find_consensus_clique(full, epsilon)
|
||
clique_set = set(clique)
|
||
|
||
# Step 4 — classify faults
|
||
fault_report: dict[str, Any] = {
|
||
"timestamp": time.time(),
|
||
"n_processes": self.n_processes,
|
||
"threshold": self.threshold,
|
||
"crash_faults": crash_ids,
|
||
"clique": clique,
|
||
"clique_size": len(clique),
|
||
"agreement_matrix": self._agreement_matrix,
|
||
"processes": {},
|
||
}
|
||
|
||
for pid in range(self.n_processes):
|
||
fault_report["processes"][pid] = {
|
||
"present": full[pid] is not None,
|
||
"in_clique": pid in clique_set,
|
||
"fault_type": (
|
||
FaultType.NONE.name
|
||
if pid in clique_set
|
||
else self.detect_fault_type(pid, full, clique).name
|
||
),
|
||
"proposal_summary": (
|
||
self._summarise(full[pid]) if full[pid] else None
|
||
),
|
||
}
|
||
|
||
# Determine result
|
||
if len(clique) >= self.threshold:
|
||
result = ConsensusResult.VALID
|
||
# Manifold verification: all clique members on same geodesic
|
||
if not self._manifold_verify(full, clique, epsilon):
|
||
result = ConsensusResult.INVALID
|
||
elif len(clique) == 0:
|
||
result = ConsensusResult.NO_QUORUM
|
||
else:
|
||
result = ConsensusResult.TIED
|
||
|
||
fault_report["result"] = result.name
|
||
fault_report["duration_ms"] = round((time.monotonic() - t0) * 1000, 3)
|
||
|
||
return result, clique, fault_report
|
||
|
||
# -- fault classification ----------------------------------------------
|
||
|
||
def detect_fault_type(
|
||
self,
|
||
process_id: int,
|
||
proposals: dict[int, Optional[Checkpoint]],
|
||
clique: list[int],
|
||
) -> FaultType:
|
||
"""Classify WHY a process disagrees with the consensus clique.
|
||
|
||
This is the diagnostic heart of the watchdog system. By examining
|
||
the proposal of a dissenting process relative to the agreeing
|
||
majority, we can distinguish:
|
||
|
||
CRASH — no proposal at all
|
||
BIT_FLIP — spiral_index differs by a power-of-two
|
||
DETERMINISM_FAILURE — completely different result
|
||
STEALTH_FAULT — values barely outside epsilon
|
||
GODEL_BOUNDARY — spiral_index == 0 or pressure == 1.0
|
||
FAMM_CORRUPTION — pressure inconsistent with DAG depth
|
||
|
||
Args:
|
||
process_id: the dissenting process
|
||
proposals: all proposals
|
||
clique: the agreeing clique (used as ground truth)
|
||
|
||
Returns:
|
||
FaultType enum member
|
||
"""
|
||
cp = proposals.get(process_id)
|
||
if cp is None:
|
||
return FaultType.CRASH
|
||
|
||
if not clique:
|
||
# No clique to compare against — check for self-evident faults
|
||
if cp.spiral_index == 0 and cp.famm_pressure > 0.9:
|
||
return FaultType.GODEL_BOUNDARY
|
||
return FaultType.DETERMINISM_FAILURE
|
||
|
||
# Use clique centroid as ground truth
|
||
truth = self._clique_centroid(proposals, clique)
|
||
|
||
# Gödel boundary: undecidable / self-referential paradox region
|
||
if cp.spiral_index == 0 or abs(cp.famm_pressure - 1.0) < 1e-4:
|
||
return FaultType.GODEL_BOUNDARY
|
||
|
||
# FAMM corruption: pressure inconsistent with DAG depth
|
||
expected_pressure = min(1.0, len(cp.dag.nodes) * 0.1)
|
||
if abs(cp.famm_pressure - expected_pressure) > 0.5:
|
||
return FaultType.FAMM_CORRUPTION
|
||
|
||
# Bit-flip: spiral index differs by a small power of two
|
||
delta = abs(cp.spiral_index - truth.spiral_index)
|
||
if delta > 0 and (delta & (delta - 1)) == 0 and delta <= 1024:
|
||
return FaultType.BIT_FLIP
|
||
|
||
# Stealth fault: very close to truth but outside epsilon
|
||
# (deliberately crafted to evade simple detection)
|
||
state_dist = math.sqrt(
|
||
sum((a - b) ** 2 for a, b in zip(cp.state, truth.state))
|
||
)
|
||
if (
|
||
delta <= max(1, truth.spiral_index // 10000)
|
||
and abs(cp.compression_ratio - truth.compression_ratio)
|
||
<= truth.compression_ratio * 0.01
|
||
and state_dist <= 1e-3
|
||
):
|
||
return FaultType.STEALTH_FAULT
|
||
|
||
# Fallback: the computation produced a fundamentally different
|
||
# (but internally consistent) result — non-determinism
|
||
return FaultType.DETERMINISM_FAILURE
|
||
|
||
# -- internal helpers --------------------------------------------------
|
||
|
||
def _clique_centroid(
|
||
self, proposals: dict[int, Optional[Checkpoint]], clique: list[int]
|
||
) -> Checkpoint:
|
||
"""Return a synthetic "average" checkpoint from the clique.
|
||
Used as ground truth for fault classification."""
|
||
cps = [proposals[pid] for pid in clique if proposals.get(pid) is not None]
|
||
assert cps
|
||
# Representative: the first clique member (they all agree)
|
||
return cps[0]
|
||
|
||
def _manifold_verify(
|
||
self,
|
||
proposals: dict[int, Optional[Checkpoint]],
|
||
clique: list[int],
|
||
epsilon: float,
|
||
) -> bool:
|
||
"""Verify that all clique members lie on the same geodesic.
|
||
|
||
Since they all agree on state and spiral_index (by construction
|
||
of agrees_with), this reduces to a consistency check on the
|
||
prev_hash chain."""
|
||
cps = [proposals[pid] for pid in clique if proposals.get(pid) is not None]
|
||
if len(cps) < 2:
|
||
return True
|
||
# All should have the same prev_hash for a single-chain DAG
|
||
prev_hashes = {cp.prev_hash for cp in cps}
|
||
# In a real system we'd check geodesic equations; here the
|
||
# structural agreement check in agrees_with suffices.
|
||
return len(prev_hashes) <= 1
|
||
|
||
def _summarise(self, cp: Checkpoint) -> dict[str, Any]:
|
||
"""Short summary for fault reporting."""
|
||
return {
|
||
"spiral_index": cp.spiral_index,
|
||
"compression_ratio": round(cp.compression_ratio, 2),
|
||
"famm_pressure": round(cp.famm_pressure, 6),
|
||
"dag_nodes": len(cp.dag.nodes),
|
||
}
|
||
|
||
# -- diagnostics -------------------------------------------------------
|
||
|
||
def agreement_matrix_ascii(self) -> str:
|
||
"""Pretty-print the agreement matrix from the last consensus run."""
|
||
if not self._agreement_matrix:
|
||
return "(no consensus run yet)"
|
||
lines = ["Agreement matrix (last run):"]
|
||
for row in self._agreement_matrix:
|
||
lines.append(" " + " ".join("1" if v else "." for v in row))
|
||
return "\n".join(lines)
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Simulation helpers
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def build_genesis_dag() -> DAG:
|
||
"""Create the genesis DAG (single node, no edges)."""
|
||
dag = DAG()
|
||
genesis = Checkpoint(
|
||
state=[1 / 8] * 8, # uniform on Δ₇
|
||
spiral_index=0,
|
||
compression_ratio=1.0,
|
||
timestamp=0.0,
|
||
famm_pressure=0.0,
|
||
dag=dag, # self-referential, will be fixed below
|
||
prev_hash=None,
|
||
)
|
||
dag.add_node(genesis)
|
||
return dag
|
||
|
||
|
||
def simulate_watchdog(
|
||
process_id: int,
|
||
prev_dag: DAG,
|
||
prev_hash: str,
|
||
fault_mode: Optional[str] = None,
|
||
) -> Checkpoint:
|
||
"""Simulate one Φ-corkscrew watchdog computation.
|
||
|
||
Args:
|
||
process_id: 0 … 4
|
||
prev_dag: DAG from previous checkpoint
|
||
prev_hash: receipt hash of previous checkpoint
|
||
fault_mode: None, 'bit_flip', 'crash', 'determinism', 'stealth',
|
||
'godel', or 'famm'
|
||
|
||
Returns:
|
||
Checkpoint proposal (or raises for crash simulation)
|
||
"""
|
||
if fault_mode == "crash":
|
||
raise RuntimeError(f"Watchdog {process_id} crashed")
|
||
|
||
# Deterministic walk on S⁷ — ALL healthy processes must arrive
|
||
# at the EXACT same result. process_id is NOT used in the
|
||
# computation (only for identification / signing).
|
||
step = 42 # iteration number (fixed for demo)
|
||
|
||
# Geodesic walk: move deterministically from uniform distribution
|
||
angle = 2 * math.pi * step * PHI
|
||
target = [1 / 8 + 0.05 * math.cos(angle + i * 2 * math.pi / 8) for i in range(8)]
|
||
|
||
# Normalise to probability simplex Δ₇
|
||
total = sum(abs(x) for x in target)
|
||
state = [max(0.01, x / total) for x in target]
|
||
s = sum(state)
|
||
state = [x / s for x in state]
|
||
|
||
# Spiral index: deterministic function of state
|
||
spiral_index = int(sum(x * 1e8 for x in state)) % 1000000 + 1000
|
||
|
||
# Compression ratio grows with iteration depth
|
||
compression_ratio = 268435456.0 * (1 + 0.01 * step)
|
||
|
||
# FAMM pressure (LWMA-1 guided)
|
||
famm_pressure = min(0.47, len(prev_dag.nodes) * 0.08)
|
||
|
||
# Timestamp
|
||
timestamp = time.time()
|
||
|
||
# Build DAG: copy previous and append new checkpoint
|
||
new_dag = prev_dag.copy()
|
||
|
||
cp = Checkpoint(
|
||
state=state,
|
||
spiral_index=spiral_index,
|
||
compression_ratio=compression_ratio,
|
||
timestamp=timestamp,
|
||
famm_pressure=famm_pressure,
|
||
dag=new_dag,
|
||
prev_hash=prev_hash,
|
||
)
|
||
|
||
# Inject faults
|
||
if fault_mode == "bit_flip":
|
||
# Flip one bit in the spiral index (add a power of two)
|
||
cp.spiral_index += 4 # 2^2 bit flip
|
||
|
||
elif fault_mode == "determinism":
|
||
# Completely different spiral index
|
||
cp.spiral_index = 777777
|
||
cp.compression_ratio = 12345.0
|
||
|
||
elif fault_mode == "stealth":
|
||
# Barely outside epsilon — crafted to evade detection
|
||
cp.compression_ratio *= 1.00002 # 0.002% deviation
|
||
|
||
elif fault_mode == "godel":
|
||
# Force boundary condition
|
||
cp.spiral_index = 0
|
||
cp.famm_pressure = 1.0
|
||
|
||
elif fault_mode == "famm":
|
||
# Corrupt FAMM pressure to be inconsistent with DAG depth
|
||
cp.famm_pressure = 99.9
|
||
|
||
new_dag.add_node(cp)
|
||
return cp
|
||
|
||
|
||
def inject_fault(
|
||
proposals: dict[int, Checkpoint],
|
||
target_id: int,
|
||
fault_mode: str,
|
||
) -> dict[int, Optional[Checkpoint]]:
|
||
"""Inject a fault into the proposals of a specific process.
|
||
|
||
Returns a new proposals dict with the fault applied.
|
||
"""
|
||
result: dict[int, Optional[Checkpoint]] = dict(proposals)
|
||
if fault_mode == "crash":
|
||
result[target_id] = None
|
||
elif fault_mode == "bit_flip":
|
||
cp = proposals[target_id].copy()
|
||
cp.spiral_index += 4
|
||
result[target_id] = cp
|
||
elif fault_mode == "determinism":
|
||
cp = proposals[target_id].copy()
|
||
cp.spiral_index = 999999
|
||
cp.compression_ratio = 1.0
|
||
result[target_id] = cp
|
||
elif fault_mode == "stealth":
|
||
cp = proposals[target_id].copy()
|
||
cp.compression_ratio *= 1.00002
|
||
result[target_id] = cp
|
||
elif fault_mode == "godel":
|
||
cp = proposals[target_id].copy()
|
||
cp.spiral_index = 0
|
||
cp.famm_pressure = 1.0
|
||
result[target_id] = cp
|
||
elif fault_mode == "famm":
|
||
cp = proposals[target_id].copy()
|
||
cp.famm_pressure = 88.8
|
||
result[target_id] = cp
|
||
return result
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Demonstration
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def demo():
|
||
"""Run a full consensus demonstration.
|
||
|
||
Scenario:
|
||
5 watchdogs compute the same Φ-corkscrew step.
|
||
Watchdog 3 suffers a bit-flip fault in its spiral_index.
|
||
The consensus protocol finds the 4-agreeing clique and
|
||
classifies the fault.
|
||
"""
|
||
print("=" * 72)
|
||
print(" SilverSight Lattice — Quintuplet Byzantine Consensus Demo")
|
||
print(" 5 watchdogs | 2-fault tolerant | 4/5 threshold")
|
||
print("=" * 72)
|
||
|
||
# --- Phase 1: Genesis ------------------------------------------------
|
||
print("\n[1] GENESIS — creating origin checkpoint on Δ₇")
|
||
genesis_dag = build_genesis_dag()
|
||
genesis_cp = list(genesis_dag.nodes.values())[0]
|
||
genesis_hash = genesis_cp.receipt_hash()
|
||
print(f" Genesis receipt: {genesis_hash[:24]}...")
|
||
print(f" State: uniform (1/8)×8")
|
||
print(f" Spiral index: {genesis_cp.spiral_index}")
|
||
print(f" DAG: {genesis_dag}")
|
||
|
||
# --- Phase 2: 5 watchdogs compute ------------------------------------
|
||
print("\n[2] WATCHDOG COMPUTATION — 5× Φ-corkscrew geodesic walk")
|
||
proposals: dict[int, Checkpoint] = {}
|
||
for pid in range(5):
|
||
cp = simulate_watchdog(pid, genesis_dag, genesis_hash)
|
||
proposals[pid] = cp
|
||
print(f" W{pid}: n={cp.spiral_index}, C={cp.compression_ratio:.2e}, "
|
||
f"FAMM={cp.famm_pressure:.4f}")
|
||
|
||
# Verify all 5 agree initially
|
||
all_same = all(
|
||
proposals[i].agrees_with(proposals[0]) for i in range(1, 5)
|
||
)
|
||
print(f"\n All 5 agree (pre-fault): {all_same}")
|
||
|
||
# --- Phase 3: Inject bit-flip fault ----------------------------------
|
||
print("\n[3] FAULT INJECTION — bit-flip on watchdog 3")
|
||
print(" Flipping bit 2^2 = +4 in spiral_index of W3")
|
||
faulty_proposals = inject_fault(proposals, target_id=3, fault_mode="bit_flip")
|
||
|
||
for pid in range(5):
|
||
cp = faulty_proposals[pid]
|
||
if cp is None:
|
||
print(f" W{pid}: CRASH (no proposal)")
|
||
elif pid == 3:
|
||
print(f" W{pid}: n={cp.spiral_index} *** FAULT ***")
|
||
else:
|
||
print(f" W{pid}: n={cp.spiral_index}")
|
||
|
||
# --- Phase 4: Byzantine consensus ------------------------------------
|
||
print("\n[4] BYZANTINE CONSENSUS — running agreement protocol")
|
||
consensus = ByzantineConsensus(n_processes=5, threshold=4)
|
||
result, clique, report = consensus.run_consensus(faulty_proposals)
|
||
|
||
print(f" Result: {result}")
|
||
print(f" Clique: {clique} (size {len(clique)})")
|
||
print(f" Duration: {report['duration_ms']:.3f} ms")
|
||
|
||
# Print agreement matrix
|
||
print(f"\n {consensus.agreement_matrix_ascii()}")
|
||
|
||
# --- Phase 5: Fault classification -----------------------------------
|
||
print("\n[5] FAULT CLASSIFICATION")
|
||
for pid in range(5):
|
||
info = report["processes"][pid]
|
||
ft = info["fault_type"]
|
||
status = "✓ AGREE" if info["in_clique"] else f"✗ {ft}"
|
||
print(f" W{pid}: {status}")
|
||
if not info["in_clique"] and info["present"]:
|
||
detail = consensus.detect_fault_type(pid, faulty_proposals, clique)
|
||
cp = faulty_proposals[pid]
|
||
if clique:
|
||
truth = consensus._clique_centroid(faulty_proposals, clique)
|
||
delta_n = abs(cp.spiral_index - truth.spiral_index) if cp else "N/A"
|
||
else:
|
||
delta_n = "N/A (no clique)"
|
||
print(f" delta_n={delta_n}, detected={detail.name}")
|
||
|
||
# --- Phase 6: Receipt ------------------------------------------------
|
||
print("\n[6] CONSENSUS RECEIPT")
|
||
if result == ConsensusResult.VALID and clique:
|
||
accepted = faulty_proposals[clique[0]]
|
||
receipt = accepted.to_receipt(verified=True)
|
||
receipt["watchdogSignatures"] = [
|
||
{"watchdog": pid, "spiralIndex": (
|
||
faulty_proposals[pid].spiral_index if faulty_proposals[pid] else None
|
||
), "agree": pid in set(clique)}
|
||
for pid in range(5)
|
||
]
|
||
receipt["consensusClique"] = clique
|
||
receipt["guidanceAdjustment"] = 1.05
|
||
print(json.dumps(receipt, indent=2, default=str))
|
||
else:
|
||
print(" No valid consensus — checkpoint rejected")
|
||
|
||
# --- Phase 7: Additional fault scenarios -----------------------------
|
||
print("\n[7] STRESS TEST — multiple fault scenarios")
|
||
scenarios = [
|
||
("clean", None, None),
|
||
("1× bit-flip", 2, "bit_flip"),
|
||
("1× crash", 4, "crash"),
|
||
("1× determinism", 0, "determinism"),
|
||
("1× stealth", 1, "stealth"),
|
||
("1× Gödel boundary", 3, "godel"),
|
||
("1× FAMM corrupt", 2, "famm"),
|
||
("2× fault (bit+crash)", None, "multi"),
|
||
]
|
||
|
||
for name, target, mode in scenarios:
|
||
if mode == "multi":
|
||
test = inject_fault(proposals, 1, "bit_flip")
|
||
test = inject_fault(test, 4, "crash")
|
||
elif mode is None:
|
||
test = dict(proposals)
|
||
else:
|
||
test = inject_fault(proposals, target, mode)
|
||
|
||
res, clq, rep = consensus.run_consensus(test)
|
||
ok = "✓ VALID" if res == ConsensusResult.VALID else f"✗ {res.name}"
|
||
print(f" {name:24s} → {ok} clique={clq} "
|
||
f"faults={ {pid: rep['processes'][pid]['fault_type'] for pid in range(5) if not rep['processes'][pid]['in_clique']} }")
|
||
|
||
print("\n" + "=" * 72)
|
||
print(" Demo complete.")
|
||
print("=" * 72)
|
||
|
||
return result, clique, report
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Entry point
|
||
# ---------------------------------------------------------------------------
|
||
|
||
if __name__ == "__main__":
|
||
demo()
|