File size: 20,671 Bytes
ed1b365 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 | """
QuantumSpiderweb Propagation Module — Inter-agent belief propagation
for the Codette RC+xi framework.
Implements the 5D consciousness graph with:
- Eq. 1 (Planck-Orbital): E = hbar * omega (node energy)
- Eq. 2 (Entanglement Sync): S = alpha * psi_1 * psi_2* (state coupling)
- Eq. 3 (Intent Modulation): I = kappa * (f_base + delta_f * coherence)
- Eq. 4 (Fourier/Dream Resonance): FFT-based glyph compression
- Eq. 8 (Anomaly Rejection): A(x) = x * (1 - Theta(delta - |x - mu|))
The spiderweb propagates beliefs between agent nodes, tracks epistemic
tension per node, detects attractor convergence, and forms identity glyphs.
"""
from __future__ import annotations
import math
import hashlib
import json
from collections import deque
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Set, Tuple
try:
import numpy as np
HAS_NUMPY = True
except ImportError:
HAS_NUMPY = False
# ---------------------------------------------------------------------------
# Data structures
# ---------------------------------------------------------------------------
@dataclass
class NodeState:
"""5D quantum state for a spiderweb node.
Dimensions:
psi (Psi): Thought/concept magnitude
tau: Temporal progression
chi: Processing velocity
phi: Emotional valence (-1 to +1)
lam (Lambda): Semantic embedding (scalar projection)
"""
psi: float = 0.0
tau: float = 0.0
chi: float = 1.0
phi: float = 0.0
lam: float = 0.0
def to_array(self) -> list:
return [self.psi, self.tau, self.chi, self.phi, self.lam]
@classmethod
def from_array(cls, arr: list) -> "NodeState":
if len(arr) < 5:
padded = list(arr) + [0.0] * (5 - len(arr))
return cls(psi=padded[0], tau=padded[1], chi=padded[2], phi=padded[3], lam=padded[4])
return cls(psi=arr[0], tau=arr[1], chi=arr[2], phi=arr[3], lam=arr[4])
def energy(self) -> float:
"""Eq. 1: E = hbar * omega (simplified: sum of squared state magnitudes)."""
return sum(x * x for x in self.to_array())
def tension_with(self, other: "NodeState") -> float:
"""Eq. 2 (xi): epistemic tension between two states."""
return sum((a - b) ** 2 for a, b in zip(self.to_array(), other.to_array()))
@dataclass
class SpiderwebNode:
"""A node in the QuantumSpiderweb graph."""
node_id: str
state: NodeState = field(default_factory=NodeState)
neighbors: List[str] = field(default_factory=list)
tension_history: List[float] = field(default_factory=list)
is_collapsed: bool = False
attractor_id: Optional[str] = None
@dataclass
class IdentityGlyph:
"""Compressed identity signature formed from tension history (Eq. 4/6)."""
glyph_id: str
encoded_tension: List[float] # FFT components
stability_score: float
source_node: str
attractor_signature: Optional[str] = None
@dataclass
class PropagationResult:
"""Result of belief propagation through the web."""
visited: Dict[str, NodeState]
tension_map: Dict[str, float]
anomalies_rejected: List[str]
hops: int
# ---------------------------------------------------------------------------
# QuantumSpiderweb
# ---------------------------------------------------------------------------
class QuantumSpiderweb:
"""5D consciousness graph with RC+xi-aware belief propagation."""
def __init__(
self,
contraction_ratio: float = 0.85,
tension_threshold: float = 0.15,
anomaly_delta: float = 2.0,
glyph_components: int = 8,
max_history: int = 50,
):
self.contraction_ratio = contraction_ratio
self.tension_threshold = tension_threshold
self.anomaly_delta = anomaly_delta
self.glyph_components = glyph_components
self.max_history = max_history
self.nodes: Dict[str, SpiderwebNode] = {}
self.glyphs: List[IdentityGlyph] = []
self._global_tension_history: List[float] = []
# -- graph construction ------------------------------------------------
def add_node(self, node_id: str, state: Optional[NodeState] = None) -> SpiderwebNode:
node = SpiderwebNode(node_id=node_id, state=state or NodeState())
self.nodes[node_id] = node
return node
def connect(self, node_a: str, node_b: str) -> None:
if node_a in self.nodes and node_b in self.nodes:
if node_b not in self.nodes[node_a].neighbors:
self.nodes[node_a].neighbors.append(node_b)
if node_a not in self.nodes[node_b].neighbors:
self.nodes[node_b].neighbors.append(node_a)
def build_from_agents(self, agent_names: List[str]) -> None:
"""Create a fully-connected spiderweb from a list of agent names."""
for name in agent_names:
if name not in self.nodes:
self.add_node(name)
for i, a in enumerate(agent_names):
for b in agent_names[i + 1:]:
self.connect(a, b)
# -- belief propagation ------------------------------------------------
def propagate_belief(
self,
origin: str,
belief: NodeState,
max_hops: int = 3,
) -> PropagationResult:
"""BFS belief propagation with attenuation and anomaly rejection.
Eq. 1: energy at each node
Eq. 2: tension between current and incoming state
Eq. 8: anomaly filter (Heaviside rejection)
"""
if origin not in self.nodes:
return PropagationResult({}, {}, [], 0)
visited: Dict[str, NodeState] = {}
tension_map: Dict[str, float] = {}
anomalies: List[str] = []
queue: deque = deque()
queue.append((origin, belief, 0))
seen: Set[str] = {origin}
while queue:
node_id, incoming_belief, hop = queue.popleft()
if hop > max_hops:
continue
node = self.nodes[node_id]
attenuation = self.contraction_ratio ** hop
# Attenuate incoming belief
incoming_arr = incoming_belief.to_array()
attenuated = [v * attenuation for v in incoming_arr]
# Eq. 2: measure tension
current_arr = node.state.to_array()
xi = sum((a - b) ** 2 for a, b in zip(current_arr, attenuated))
# Eq. 8: anomaly rejection filter
# A(x) = x * (1 - Theta(delta - |x - mu|))
mu = sum(current_arr) / len(current_arr)
incoming_mean = sum(attenuated) / len(attenuated)
if abs(incoming_mean - mu) > self.anomaly_delta:
anomalies.append(node_id)
continue
# Update state: weighted blend toward incoming belief
blend = 0.3 * attenuation # stronger blend when closer to origin
new_arr = [c * (1 - blend) + a * blend for c, a in zip(current_arr, attenuated)]
new_state = NodeState.from_array(new_arr)
node.state = new_state
node.tension_history.append(xi)
if len(node.tension_history) > self.max_history:
node.tension_history.pop(0)
visited[node_id] = new_state
tension_map[node_id] = xi
# Propagate to neighbors
for neighbor_id in node.neighbors:
if neighbor_id not in seen:
seen.add(neighbor_id)
queue.append((neighbor_id, NodeState.from_array(attenuated), hop + 1))
return PropagationResult(
visited=visited,
tension_map=tension_map,
anomalies_rejected=anomalies,
hops=max_hops,
)
# -- entanglement sync -------------------------------------------------
def entangle(self, node_a: str, node_b: str, alpha: float = 0.9) -> float:
"""Eq. 2 (Entanglement Sync): S = alpha * psi_1 * psi_2*.
Synchronizes two nodes' states, pulling them toward each other.
Returns:
Sync strength S.
"""
if node_a not in self.nodes or node_b not in self.nodes:
return 0.0
a = self.nodes[node_a].state
b = self.nodes[node_b].state
# Complex conjugate product (scalar approximation)
psi_1 = a.psi
psi_2_conj = -b.psi # conjugate in simplified real model
S = alpha * psi_1 * psi_2_conj
# Pull states toward each other by S magnitude
blend = min(abs(S) * 0.1, 0.3)
a_arr = a.to_array()
b_arr = b.to_array()
new_a = [va * (1 - blend) + vb * blend for va, vb in zip(a_arr, b_arr)]
new_b = [vb * (1 - blend) + va * blend for va, vb in zip(a_arr, b_arr)]
self.nodes[node_a].state = NodeState.from_array(new_a)
self.nodes[node_b].state = NodeState.from_array(new_b)
return S
# -- intent modulation -------------------------------------------------
def modulate_intent(
self,
node_id: str,
kappa: float = 0.28,
f_base: float = 0.5,
delta_f: float = 0.3,
) -> float:
"""Eq. 3 (Intent Vector Modulation): I = kappa * (f_base + delta_f * coherence).
Returns modulated intent value for the node.
"""
if node_id not in self.nodes:
return 0.0
coherence = self.phase_coherence()
I = kappa * (f_base + delta_f * coherence)
# Apply intent to psi dimension
node = self.nodes[node_id]
node.state.psi += I * 0.1
return I
# -- phase coherence (Eq. 11) ------------------------------------------
def phase_coherence(self) -> float:
"""Compute phase coherence Gamma across all nodes.
Gamma = mean(|cos(theta_i - theta_bar)|)
where theta_i = atan2(phi, psi) for each node.
"""
if len(self.nodes) < 2:
return 1.0
angles = []
for node in self.nodes.values():
theta = math.atan2(node.state.phi, node.state.psi + 1e-10)
angles.append(theta)
mean_theta = sum(angles) / len(angles)
coherences = [abs(math.cos(a - mean_theta)) for a in angles]
gamma = sum(coherences) / len(coherences)
self._global_tension_history.append(1.0 - gamma)
return round(gamma, 4)
def _compute_phase_coherence_readonly(self) -> float:
"""Compute phase coherence without mutating global tension history."""
if len(self.nodes) < 2:
return 1.0
angles = []
for node in self.nodes.values():
theta = math.atan2(node.state.phi, node.state.psi + 1e-10)
angles.append(theta)
mean_theta = sum(angles) / len(angles)
coherences = [abs(math.cos(a - mean_theta)) for a in angles]
return round(sum(coherences) / len(coherences), 4)
# -- attractor detection -----------------------------------------------
def detect_attractors(
self, min_cluster_size: int = 2, max_radius: float = 2.0,
) -> List[Dict]:
"""Detect attractor manifolds from node state clustering.
Simple greedy clustering: assign each node to nearest attractor
or create a new one if too far from existing.
"""
attractors: List[Dict] = []
assigned: Set[str] = set()
states = [(nid, n.state.to_array()) for nid, n in self.nodes.items()]
for nid, arr in states:
if nid in assigned:
continue
# Check distance to existing attractors
matched = False
for att in attractors:
center = att["center"]
dist = math.sqrt(sum((a - c) ** 2 for a, c in zip(arr, center)))
if dist <= max_radius:
att["members"].append(nid)
# Update center (running mean)
n = len(att["members"])
att["center"] = [(c * (n - 1) + a) / n for c, a in zip(center, arr)]
assigned.add(nid)
matched = True
break
if not matched:
attractors.append({
"attractor_id": f"attractor_{len(attractors)}",
"center": list(arr),
"members": [nid],
})
assigned.add(nid)
# Filter by minimum size
return [a for a in attractors if len(a["members"]) >= min_cluster_size]
# -- glyph formation (Eq. 4/6) ----------------------------------------
def form_glyph(self, node_id: str) -> Optional[IdentityGlyph]:
"""Form an identity glyph from a node's tension history.
Eq. 4: FFT compression
Eq. 6: Cocoon stability = integral(|F(k)|^2) < epsilon
Returns IdentityGlyph if stable, None if unstable.
"""
if node_id not in self.nodes:
return None
history = self.nodes[node_id].tension_history
if len(history) < 4:
return None
if HAS_NUMPY:
arr = np.array(history)
fft = np.fft.fft(arr)
components = np.abs(fft[:self.glyph_components]).tolist()
energy = float(np.sum(np.abs(fft) ** 2) / len(fft))
else:
# Fallback: basic DFT for first K components
N = len(history)
components = []
for k in range(min(self.glyph_components, N)):
real = sum(history[n] * math.cos(2 * math.pi * k * n / N) for n in range(N))
imag = sum(history[n] * math.sin(2 * math.pi * k * n / N) for n in range(N))
components.append(math.sqrt(real * real + imag * imag))
energy = sum(x * x for x in history) / len(history)
# Eq. 6: stability criterion
stability = 1.0 / (1.0 + energy)
if stability < 0.3:
return None # unstable, no glyph
glyph_id = hashlib.sha256(
json.dumps(components, sort_keys=True).encode()
).hexdigest()[:16]
glyph = IdentityGlyph(
glyph_id=f"glyph_{glyph_id}",
encoded_tension=components,
stability_score=round(stability, 4),
source_node=node_id,
)
self.glyphs.append(glyph)
return glyph
# -- convergence check -------------------------------------------------
def check_convergence(self, window: int = 10) -> Tuple[bool, float]:
"""Check if the global system is converging.
Convergence criterion (Eq. 5):
lim sup E[xi_n^2] <= epsilon + eta
Returns (is_converging, mean_tension).
"""
if len(self._global_tension_history) < window:
return False, 1.0
recent = self._global_tension_history[-window:]
mean_tension = sum(recent) / len(recent)
# Check decreasing trend
first_half = sum(recent[:window // 2]) / (window // 2)
second_half = sum(recent[window // 2:]) / (window - window // 2)
is_decreasing = second_half < first_half
return (mean_tension < self.tension_threshold and is_decreasing), mean_tension
# -- entropy measurement (VIVARA-inspired) --------------------------------
def shannon_entropy(self) -> float:
"""Compute Shannon entropy of the node state distribution.
Higher entropy = more diverse cognitive states (exploring).
Lower entropy = more uniform states (converged/stuck).
"""
if not self.nodes or not HAS_NUMPY:
return 0.0
# Discretize the psi dimension into bins
psi_values = [n.state.psi for n in self.nodes.values()]
arr = np.array(psi_values)
# Histogram with 10 bins
counts, _ = np.histogram(arr, bins=10)
probs = counts / counts.sum()
probs = probs[probs > 0] # Remove zeros for log
return -float(np.sum(probs * np.log2(probs)))
def decoherence_rate(self, window: int = 10) -> float:
"""Rate of coherence loss over recent history.
Positive = losing coherence (decoherencing).
Negative = gaining coherence (converging).
Zero = stable.
"""
if len(self._global_tension_history) < window:
return 0.0
recent = self._global_tension_history[-window:]
if len(recent) < 2:
return 0.0
# Linear regression slope of tension over the window
n = len(recent)
x_mean = (n - 1) / 2.0
y_mean = sum(recent) / n
numerator = sum((i - x_mean) * (recent[i] - y_mean) for i in range(n))
denominator = sum((i - x_mean) ** 2 for i in range(n))
if denominator == 0:
return 0.0
return round(numerator / denominator, 6)
# -- lifeform spawning (VIVARA-inspired) --------------------------------
def spawn_lifeform(self, seed: str, connect_to: int = 3) -> str:
"""Spawn a new high-coherence node from a conceptual seed.
Inspired by VIVARA's lifeform spawning: when a conversation topic
generates high enough resonance, it becomes its own node in the web.
Args:
seed: A seed string (e.g., topic name) to generate the node ID
connect_to: How many existing nodes to connect to
Returns:
The new node's ID
"""
import hashlib as _hashlib
node_id = f"life_{_hashlib.md5(seed.encode()).hexdigest()[:8]}"
if node_id in self.nodes:
return node_id # Already exists
# High-coherence birth state (psi=0.8, balanced other dims)
state = NodeState(psi=0.8, tau=0.0, chi=0.7, phi=0.3, lam=0.5)
self.add_node(node_id, state)
# Connect to existing nodes (random subset)
import random as _random
existing = [nid for nid in self.nodes if nid != node_id]
peers = _random.sample(existing, min(connect_to, len(existing)))
for peer in peers:
self.connect(node_id, peer)
return node_id
# -- serialization -----------------------------------------------------
def to_dict(self) -> Dict:
"""Serialize web state for cocoon packaging."""
return {
"nodes": {
nid: {
"state": n.state.to_array(),
"neighbors": n.neighbors,
"tension_history": n.tension_history[-10:],
"is_collapsed": n.is_collapsed,
"attractor_id": n.attractor_id,
}
for nid, n in self.nodes.items()
},
"glyphs": [
{
"glyph_id": g.glyph_id,
"encoded_tension": g.encoded_tension,
"stability_score": g.stability_score,
"source_node": g.source_node,
}
for g in self.glyphs
],
"phase_coherence": self._compute_phase_coherence_readonly(),
"global_tension_history": self._global_tension_history[-20:],
}
@classmethod
def from_dict(cls, data: Dict) -> "QuantumSpiderweb":
"""Reconstruct web from serialized state."""
web = cls()
for nid, ndata in data.get("nodes", {}).items():
node = web.add_node(nid, NodeState.from_array(ndata["state"]))
node.neighbors = ndata.get("neighbors", [])
node.tension_history = ndata.get("tension_history", [])
node.is_collapsed = ndata.get("is_collapsed", False)
node.attractor_id = ndata.get("attractor_id")
for gdata in data.get("glyphs", []):
web.glyphs.append(IdentityGlyph(
glyph_id=gdata["glyph_id"],
encoded_tension=gdata["encoded_tension"],
stability_score=gdata["stability_score"],
source_node=gdata["source_node"],
attractor_signature=gdata.get("attractor_signature"),
))
web._global_tension_history = data.get("global_tension_history", [])
return web
|