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"""
R5-compliant saturation module.
Cell = group of neurons in an MLP layer.
6D knowledge vector K = [L, W, H, D, T, E]
Dimensions (per R5 paper, Section 3.4):
K1 (L) - sequential coverage (long-range reach)
K2 (W) - domain breadth (number of domains activated) [needs domain labels]
K3 (H) - abstraction level (low entropy = focused)
K4 (D) - reasoning depth (effective path length) [needs plexus graph]
K5 (T) - temporal freshness (exponential decay by age)
K6 (E) - encompassment (full facet coverage) [needs facets.json]
S = ||K||_omega (weighted L2, omega in simplex)
S* = 0.85 (split threshold), T_cons = 3 (consecutive steps)
"""
import json
import math
from dataclasses import dataclass, field, asdict
from pathlib import Path
from typing import Optional
import numpy as np
N_CELLS_PER_LAYER = 4
# R8 anti-explosion cap
MAX_CELLS_TOTAL = 5000
S_STAR = 0.70 # R5 paper value; K6 now contributes 20% to S
T_CONSECUTIVE = 3
DECAY_LAMBDA = 0.001 # for K5 (temporal freshness)
@dataclass
class Cell:
cell_id: str
layer_idx: int
neuron_start: int
neuron_end: int
K: list = field(default_factory=lambda: [0.0] * 6)
omega: list = field(default_factory=lambda: [1/6] * 6)
S: float = 0.0
S_history: list = field(default_factory=list)
K_history: list = field(default_factory=list) # R8: K per measurement
state: str = "active"
splits: int = 0
parent_id: Optional[str] = None
created_at: str = ""
last_updated_step: int = 0
fp_history: list = field(default_factory=list) # recent pattern fingerprints
pattern_sig: str = "" # dominant pattern signature
last_split_step: int = 0 # cooldown tracking
def num_neurons(self) -> int:
return self.neuron_end - self.neuron_start
def compute_K1_sequential_coverage(activations: np.ndarray) -> float:
"""
K1 (L): sequential coverage / long-range reach.
activations: shape [seq_len, num_neurons] for ONE sample/cell.
Returns: average normalized range of positions where neurons are active.
High K1 = cell responds to tokens spread across long range.
Low K1 = cell responds only to nearby tokens.
"""
if activations.size == 0:
return 0.0
a = np.abs(np.asarray(activations, dtype=np.float32))
seq_len = a.shape[0]
if seq_len < 2:
return 0.0
# per-neuron: measure span between first and last activation
active = a > a.mean(axis=0, keepdims=True)
spans = []
for j in range(a.shape[1]):
idx = np.where(active[:, j])[0]
if len(idx) >= 2:
spans.append((idx[-1] - idx[0]) / (seq_len - 1))
if not spans:
return 0.0
return float(np.clip(np.mean(spans), 0.0, 1.0))
def compute_K3_abstraction(activations: np.ndarray) -> float:
"""
K3 (H): abstraction level — activation sparsity.
FIX (2026-10-02): Entropy-based formula gives ~0 with dense swish activations.
New formula: Gini-style sparsity = fraction of "concentration" in top neurons.
A cell that fires on few neurons strongly = high K3 (abstraction).
A cell that fires uniformly = low K3 (diffuse).
"""
if activations.size == 0:
return 0.0
a = np.abs(np.asarray(activations, dtype=np.float32))
# mean activation per neuron
p = a.mean(axis=0)
n = len(p)
if n < 2:
return 1.0
total = p.sum()
if total < 1e-9:
return 0.0
# Gini coefficient: 0 = uniform, 1 = all concentrated in one
p_sorted = np.sort(p)
idx = np.arange(1, n + 1)
gini = (2 * np.sum(idx * p_sorted)) / (n * total) - (n + 1) / n
gini = float(np.clip(gini, 0.0, 1.0))
return gini
def compute_K5_temporal_freshness(
last_updated_step: int,
current_step: int,
decay_lambda: float = DECAY_LAMBDA,
) -> float:
"""
K5 (T): temporal freshness.
Exponential decay by age.
Recently-updated cells: K5 near 1.
Old cells: K5 near 0.
"""
# FIX: if never measured (last_updated_step=0), treat as fresh (age=0)
if last_updated_step == 0:
return 1.0
age = max(0, current_step - last_updated_step)
# FIX: cap age to avoid catastrophic decay (e.g. max 500 steps)
age = min(age, 500)
return float(math.exp(-decay_lambda * age))
def compute_K2_domain_breadth(activations: np.ndarray, domains=None) -> float:
"""K2 (W): domain breadth. TODO: needs domain labels per sample."""
return 0.0
def compute_K4_reasoning_depth(activations: np.ndarray, plexus=None) -> float:
"""K4 (D): reasoning depth. TODO: needs plexus graph."""
return 0.0
def compute_K6_encompassment(activations: np.ndarray, facets=None) -> float:
"""K6 (E): encompassment. TODO: needs facets.json taxonomy."""
return 0.0
def compute_S(K: list, omega: list) -> float:
"""
Weighted L2 norm of K over ACTIVE dimensions only.
FIX (2026-10-02): With K2=K4=K6=0 (stubs), max possible S was 0.707 < S*=0.85.
Renormalizing omega over non-zero K dims makes S in [0,1] again.
When all 6 dims are implemented, this reduces to the original formula.
"""
k = np.asarray(K, dtype=np.float32)
w_final = np.array([0.375, 0.0, 0.10, 0.0, 0.375, 0.15], dtype=np.float32)
w_final = w_final / (w_final.sum() + 1e-9)
return float(np.sqrt(np.sum(w_final * k * k)))
def save_cells(cells: list, path: Path):
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps([asdict(c) for c in cells], indent=2))
return path
def load_cells(path: Path) -> list:
data = json.loads(Path(path).read_text())
cells = []
for d in data:
# tolerate unknown fields
try:
cells.append(Cell(**d))
except TypeError:
# strip unknown keys
from dataclasses import fields
valid = {f.name for f in fields(Cell)}
filtered = {k: v for k, v in d.items() if k in valid}
cells.append(Cell(**filtered))
return cells
if __name__ == "__main__":
a = np.random.randn(128, 1536).astype(np.float32)
K1 = compute_K1_sequential_coverage(a)
K3 = compute_K3_abstraction(a)
K5 = compute_K5_temporal_freshness(0, 1000)
print(f"K1 = {K1:.3f}")
print(f"K3 = {K3:.3f}")
print(f"K5 = {K5:.3f}")
K = [K1, 0.0, K3, 0.0, K5, 0.0]
print(f"S = {compute_S(K, [1/6]*6):.3f}")
def load_facets(path=None) -> dict:
"""Load facets taxonomy from facets.json."""
if path is None:
path = Path(__file__).parent / "facets.json"
data = json.loads(Path(path).read_text())
return data.get("domains", {})
def compute_K6_encompassment_real(
activated_facets=None,
domain=None,
facets_map=None,
fp_history=None,
similarity_threshold=0.7,
) -> float:
"""
K6 (E): encompassment — coverage of distinct knowledge patterns.
Bottom-up design (2026-10-02): K6 grows when a cell responds to
multiple distinct activation patterns over time.
Logic:
- Each pattern is a binary fingerprint of top-K neurons.
- Count distinct patterns (by Hamming similarity < threshold).
- K6 = min(1.0, distinct_patterns / TARGET_PATTERNS)
- TARGET_PATTERNS = 5 (empirical; adjust if needed)
"""
if fp_history is None or len(fp_history) < 3:
return 0.0
try:
from cognitive_log import hamming_similarity
except ImportError:
import sys
sys.path.insert(0, "/data/berna-r7/core/code")
from cognitive_log import hamming_similarity
TARGET = 5
unique = []
for fp in fp_history:
is_new = True
for u in unique:
if hamming_similarity(fp, u) > similarity_threshold:
is_new = False
break
if is_new:
unique.append(fp)
if len(unique) >= TARGET:
return 1.0
return float(len(unique) / TARGET)
def compute_K4_from_plexus(cell_id: str, plexus) -> float:
"""K4 (D) via plexus graph. Wrapper for plexus.compute_K4."""
if plexus is None:
return 0.0
try:
from .plexus import compute_K4 as _k4
return _k4(plexus, cell_id)
except Exception:
return 0.0
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