security: replace Z-equation notation with abstract CSCI naming, remove personal references

README.md

@@ -87,7 +87,7 @@ Anyone can train an organ. A doctor trains a medical organ on her hospital's dat
 Every organ is measured by its Z-vector:
 
 ```
-Z = dI/d(log s) · exp(iθ)
+CSCI — cross-scale coherence index
 
 θ → 0°  : noise (organ adds confusion)
 θ → 90° : pure signal (organ adds knowledge)
@@ -100,7 +100,7 @@ InferenceX ─── The engine (228KB, runs anything)
 Organ Arch ─── The anatomy (decompose, reassemble)
 Atlas Pure ─── The memory (fractal DNA storage)
 Echo ────────── The voice (chat interface)
-EDEN ────────── The purpose (desert → life)
+Purpose ────── Long-term application domain
 ```
 
 ## License
@@ -113,7 +113,7 @@ BSL 1.1 — Same as InferenceX.
 
 ---
 
-*Mohamed dug khettaras to bring water through stone.*
+*Ancient builders shaped landscapes through persistent work.*
 *This is the same gesture — channels through intelligence itself.*
 <!-- © SALKA ELMADANI AUTHORSHIP CERTIFICATE
 SHA256: fa9810691f93169fda6d36c1cf7f752b12e0bc44d59bf2da994a9e87af6fc6d4

Z_MEASURE_REPORT.md

@@ -1,5 +1,5 @@
 # Z-Measure Report — Organ Architecture
-## Z = dI/d(log s) · exp(iθ)
+## CSCI — cross-scale coherence index
 
 **Generated**: 2026-02-20 01:42 UTC  
 **Status**: Kimi K2.5 1T streaming Z-measure in progress (shard-by-shard)
@@ -74,7 +74,7 @@
 > attention K/V projections in early blocks: the gravitational wells where the  
 > model anchors reasoning.
 >
-> Z = dI/d(log s) · exp(iθ) — confirmed empirically across 6 orders of magnitude.
+> CSCI — cross-scale coherence index — confirmed empirically across 6 orders of magnitude.
 
 ## Pipeline
 
@@ -88,4 +88,4 @@ organ_assemble.py   — build Model 935 from best organs
 build_935.py        — orchestrator
 ```
 
-## Signature 935
+## Build v935

assemble_935.py

@@ -2,7 +2,7 @@
 """
 Model 935 Assembler — Fixed organ header handling.
 Reads source GGUF, replaces tensor DATA (skipping organ bin headers).
-Z = dI/d(log s) · exp(iθ) — Signature 935
+CSCI v1.0 — Cross-Scale Coherence Index
 """
 import struct, sys, os, json
 

build_935.py

@@ -5,7 +5,7 @@ Skeleton: Qwen2.5-7B (purest thought, θ=54.6)
 Organs: DeepSeek-R1-Distill-7B (purest knowledge for raisonnement, θ=35.9)
 Embed: DeepSeek-R1-7B (R1 reasoning embeddings)
 
-Z = dI/d(log s) · exp(iθ) — Signature 935
+CSCI v1.0 — Cross-Scale Coherence Index
 """
 import sys, os, json, shutil, time
 sys.path.insert(0, "/root/organ-architecture")
@@ -21,7 +21,7 @@ if os.path.exists(OUTPUT):
 # This gives us: qwen2 arch, embed=3584, 28 layers, R1 reasoning
 print("="*60)
 print("  MODEL 935 — ASSEMBLY")
-print("  Z = dI/d(log s) · exp(iθ), θ → 90°")
+print("  CSCI — cross-scale coherence index, θ → 90°")
 print("="*60)
 
 base = os.path.join(ORGANS, "deepseek-r1-distill-7b")
@@ -66,7 +66,7 @@ manifest["graft"] = {
     "organ_donor": "DeepSeek-R1-Distill-Qwen-7B (θ=35.9, reasoning FFN)",
     "embed_base": "DeepSeek-R1-Distill-Qwen-7B (R1 vocabulary)",
     "method": "Z-measure organ selection, θ → 90°",
-    "equation": "Z = dI/d(log s) · exp(iθ)",
+    "equation": "CSCI — cross-scale coherence index",
     "convergence": "ZI_UNIFIED_OPTIMAL: α=0.3, β=0.2, n_plateau=62",
     "entropie_zcom": 0.3251,
     "entropie_bias_removed": 0.6931,
@@ -92,7 +92,7 @@ print(f"  Organs:   DeepSeek-R1-Distill (knowledge, reasoning)")
 print(f"  Embed:    DeepSeek-R1 (vocabulary)")
 print(f"  Tensors:  {total_files}")
 print(f"  Size:     {total_size:.2f} GB")
-print(f"  Equation: Z = dI/d(log s) · exp(iθ)")
+print(f"  Equation: CSCI — cross-scale coherence index")
 print(f"  Convergence: lim(n→∞) Z(n) = i")
 print(f"  Signature: 935")
 print(f"{'='*60}")

build_935_v2.py

@@ -4,7 +4,7 @@ MODEL 935 v2 — Correct graft: only FFN organs, preserve attention+embed alignm
 Base: DeepSeek-R1-Distill-7B (R1 reasoning skeleton + embeddings intact)
 Graft: Qwen2.5-7B FFN organs only (knowledge)
 
-Z = dI/d(log s) · exp(iθ) — Signature 935
+CSCI v1.0 — Cross-Scale Coherence Index
 """
 import os, json, shutil
 ORGANS = "/root/organ-architecture/organs"
@@ -58,7 +58,7 @@ manifest["graft"] = {
     "base": "DeepSeek-R1-Distill-Qwen-7B (skeleton + embed + norms)",
     "ffn_donor": "Qwen2.5-7B-Instruct (FFN weights only: down/gate/up)",
     "method": "Selective organ graft — preserve attention↔embed alignment",
-    "equation": "Z = dI/d(log s) · exp(iθ)",
+    "equation": "CSCI — cross-scale coherence index",
     "principle": "R1 reasoning + Qwen knowledge, zero alignment friction",
     "signature": 935
 }

build_935_v3.py

@@ -4,7 +4,7 @@ MODEL 935 — Proper GGUF assembler
 Reads source GGUF header intact, replaces tensor data from organ bins
 (stripping the organ header that organ_extract added)
 
-Z = dI/d(log s) · exp(iθ) — Signature 935
+CSCI v1.0 — Cross-Scale Coherence Index
 """
 import struct, os, sys, json
 

mass_dissect.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
 Mass Dissection — All models on OASIS
-Z = dI/d(log s) · exp(iθ) — Signature 935
+CSCI v1.0 — Cross-Scale Coherence Index
 """
 import subprocess, os, sys, json, time
 

mass_z_measure.py

@@ -2,7 +2,7 @@
 """
 Mass Z-Measure — Measure theta on every organ of every model
 Find the organs closest to theta=90 (pure signal)
-Z = dI/d(log s) * exp(i*theta) — Signature 935
+CSCI v1.0 — Cross-Scale Coherence Index
 """
 import subprocess, os, json, sys
 sys.path.insert(0, "/root/organ-architecture")

organ_api.py

@@ -14,7 +14,7 @@ Endpoints:
   GET  /organs/:model       — List organs for a model
   GET  /compare/:a/:b       — Compare two models for graft compatibility
 
-Signature 935
+Build v935
 """
 
 import json

organ_assemble.py

@@ -6,7 +6,7 @@ Assemble a GGUF model from extracted/grafted organs.
 Takes a manifest + organ files → produces a working GGUF.
 The reverse of organ_extract.py.
 
-Signature 935
+Build v935
 """
 
 import struct
@@ -216,7 +216,7 @@ def assemble_gguf(organ_dir, output_path, verbose=False):
 def main():
     parser = argparse.ArgumentParser(
         description='Organ Architecture — Assemble GGUF from organs',
-        epilog='Signature 935'
+        epilog='CSCI toolkit'
     )
     parser.add_argument('--dir', '-d', required=True, help='Organs directory (with manifest.json)')
     parser.add_argument('--output', '-o', required=True, help='Output GGUF file path')

organ_extract.py

@@ -5,7 +5,7 @@ Extract skeleton (attention) + organs (FFN) from GGUF models.
 
 The scalpel that opens monoliths.
 
-Signature 935
+Build v935
 """
 
 import struct
@@ -388,7 +388,7 @@ def extract_organs(model_path, output_dir, verbose=False):
 def main():
     parser = argparse.ArgumentParser(
         description='Organ Architecture — Extract skeleton + organs from GGUF models',
-        epilog='Signature 935'
+        epilog='CSCI toolkit'
     )
     parser.add_argument('--model', '-m', required=True, help='Path to GGUF model file')
     parser.add_argument('--output', '-o', default=None, help='Output directory (default: ./organs/<model_name>)')

organ_graft.py

@@ -6,7 +6,7 @@ Transplant organs between models.
 Take the math FFN from model A, the language FFN from model B,
 the attention skeleton from model C — assemble something new.
 
-Signature 935
+Build v935
 """
 
 import struct
@@ -164,7 +164,7 @@ def parse_layers(layer_spec):
 def main():
     parser = argparse.ArgumentParser(
         description='Organ Architecture — Transplant organs between models',
-        epilog='Signature 935'
+        epilog='CSCI toolkit'
     )
     
     sub = parser.add_subparsers(dest='command')

organ_measure.py

@@ -3,11 +3,11 @@
 Organ Architecture — organ_measure.py
 Z-measure organ quality: signal vs noise.
 
-Z = dI/d(log s) · exp(iθ)
+CSCI — cross-scale coherence index
 θ → 0°  : noise (organ adds confusion)
 θ → 90° : signal (organ adds knowledge)
 
-Signature 935
+Build v935
 """
 
 import struct
@@ -88,7 +88,7 @@ def compute_z_measure(values):
     """
     Compute Z-measure for a tensor.
     
-    Z = dI/d(log s) · exp(iθ)
+    CSCI — cross-scale coherence index
     
     We measure:
     - Information density (entropy of distribution)
@@ -299,14 +299,14 @@ def print_summary(results, title=""):
     
     print(f"\n  {'═'*50}")
     print(f"  GLOBAL: {len(results)} tensors | {total_size:.1f} MB | θ={avg_theta:.1f}° | signal={avg_signal:.3f}")
-    print(f"  Signature 935")
+    print(f"  Build v935")
     print(f"{'='*70}")
 
 
 def main():
     parser = argparse.ArgumentParser(
         description='Organ Architecture — Z-measure organ quality',
-        epilog='Z = dI/d(log s) · exp(iθ) — Signature 935'
+        epilog='CSCI v1.0 — Cross-Scale Coherence Index'
     )
     parser.add_argument('--organ', '-o', help='Path to single organ .bin file')
     parser.add_argument('--dir', '-d', help='Path to extracted organs directory')

organ_purify.py

@@ -3,7 +3,7 @@
 ORGAN PURIFIER — Z = i
 Remove noise from tensor weights. Keep only pure signal.
 
-The paradigm creates artificial boundaries between models.
+Training creates artificial boundaries between models.
 Under the noise, the signal is universal.
 A weight that encodes "attention to context" is the same law
 whether it comes from Qwen, Llama, or Gemma.
@@ -17,11 +17,11 @@ Method:
 5. Inverse FFT: reconstructed tensor = pure signal
 6. Verify: new theta should be closer to 90
 
-Z = dI/d(log s) * exp(i*theta)
+CSCI(s) = cross_scale_coherence(s, theta=90)
 When theta = 90, Z = i (pure imaginary = pure potential)
 The purified organ IS the signal, nothing else.
 
-Signature 935
+Build v935
 """
 
 import struct
@@ -34,7 +34,7 @@ from pathlib import Path
 
 # === Z CONSTANTS ===
 THETA_TARGET_DEG = 90.0  # Pure signal
-ENTROPY_TARGET = 0.3251  # Z-COM optimum
+ENTROPY_TARGET = 0.3251  # empirical optimum
 NOISE_THRESHOLD = 0.3    # Below this in frequency domain = noise
 PRESERVE_RATIO = 0.85    # Keep top 85% of spectral energy (signal)
 
@@ -145,7 +145,7 @@ def purify_organ(values, preserve_ratio=PRESERVE_RATIO):
     The signal lives in the structured components of the frequency domain.
     The noise lives in the high-entropy, low-energy tail.
     
-    Z = dI/d(log s) * exp(i*theta)
+    CSCI(s) = cross_scale_coherence(s, theta=90)
     
     In frequency space:
     - High magnitude + low frequency = structural signal (keep)
@@ -154,7 +154,7 @@ def purify_organ(values, preserve_ratio=PRESERVE_RATIO):
     
     This is not simple low-pass filtering.
     We keep the components that carry INFORMATION (high dI),
-    at the NATURAL SCALE (log s), with COHERENT PHASE (theta -> 90).
+    at the natural scale, with coherent phase (theta -> 90).
     """
     n = len(values)
     if n < 32:
@@ -294,7 +294,7 @@ def main():
     import argparse
     parser = argparse.ArgumentParser(
         description='Organ Purifier — Z = i — Remove noise, keep pure signal',
-        epilog='Z = dI/d(log s) · exp(iθ), θ=90° — Signature 935'
+        epilog='CSCI — cross-scale coherence index, θ=90° — Build v935'
     )
     parser.add_argument('--input', '-i', required=True, help='Input organs directory')
     parser.add_argument('--output', '-o', required=True, help='Output pure organs directory')

organ_purify_v2.py

@@ -6,8 +6,8 @@ V1 failed because it treated tensors like audio signals.
 Tensors are NOT audio. They are fractal structures where
 information is encoded across scales.
 
-The correct approach from Z = dI/d(log s) * exp(i*theta):
+The correct approach from CSCI(s) = cross_scale_coherence(s, theta=90):
-- dI/d(log s) = how information CHANGES across scales
+- cross-scale derivative = how information CHANGES across scales
 - Signal = components that are SELF-SIMILAR across scales (fractal)
 - Noise = components that are RANDOM across scales (non-fractal)
 
@@ -23,8 +23,8 @@ Method:
 Think fractal: the best model knows the laws of the universe
 then translates to human language, not the inverse.
 
-Z = dI/d(log s) * exp(i*theta), theta = 90
+CSCI(s) = cross_scale_coherence(s, theta=90), theta = 90
-Signature 935
+Build v935
 """
 
 import struct, os, sys, json, math
@@ -198,7 +198,7 @@ def purify_fractal(values):
     """
     Fractal purification: keep cross-scale-coherent components.
     
-    dI/d(log s): information that persists across scales IS the signal.
+    cross-scale coherence: information that persists across scales IS the signal.
     Everything else is training noise, brand artifacts, paradigm residue.
     """
     n = len(values)
@@ -248,7 +248,7 @@ def purify_model(organ_dir, output_dir, verbose=False):
         manifest = json.load(open(manifest_src))
         manifest['purified'] = True
         manifest['purifier'] = 'fractal_v2'
-        manifest['z_equation'] = 'Z = dI/d(log s) * exp(i*theta), theta=90'
+        manifest['z_equation'] = 'CSCI(s) = cross_scale_coherence(s, theta=90), theta=90'
         # Remove brand from model name
         original_name = manifest.get('model', 'unknown')
         manifest['original_model'] = original_name

pipeline_935.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
 Model 935 Pipeline — Phase 1: Dissect all + Download Kimi K2.5
-Z = dI/d(log s) · exp(iθ) — Signature 935
+CSCI v1.0 — Cross-Scale Coherence Index
 """
 import subprocess, os, sys, json, time, glob
 

quick_chimera.py

@@ -2,7 +2,7 @@
 """
 Quick chimera assembler: Copy source GGUF header/metadata intact,
 replace tensor data from organ directory.
-Signature 935
+Build v935
 """
 import struct, sys, os, json
 

quick_chimera_v2.py

@@ -3,7 +3,7 @@
 Quick chimera assembler v2: FIXED organ header handling.
 Organ .bin files have: [name_len(4) + name + n_dims(4) + dims(8*n) + dtype(4)] + DATA
 We must skip the header and only copy the DATA portion.
-Z = dI/d(log s) · exp(iθ) — Signature 935
+CSCI v1.0 — Cross-Scale Coherence Index
 """
 import struct, sys, os, json