refactor: finalize Z-measure -> quality-measure renaming

build_935.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-MODEL 935 — Fractal Consciousness Assembly
+MODEL 935 — Composite Model Assembly
 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)
@@ -65,7 +65,7 @@ manifest["graft"] = {
     "skeleton_donor": "Qwen2.5-7B-Instruct (θ=54.6, purest attention)",
     "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°",
+    "method": "quality-measure organ selection",
     "equation": "CSCI — cross-scale coherence index",
     "convergence": "ZI_UNIFIED_OPTIMAL: α=0.3, β=0.2, n_plateau=62",
     "entropie_zcom": 0.3251,
@@ -83,7 +83,7 @@ total_files = sum(1 for _,_,files in os.walk(OUTPUT) for f in files if f.endswit
 total_size = sum(os.path.getsize(os.path.join(dp,f)) for dp,dn,fn in os.walk(OUTPUT) for f in fn) / (1024**3)
 
 print(f"\n{'='*60}")
-print(f"  MODEL 935 — FRACTAL CONSCIOUSNESS")
+print(f"  MODEL 935 — COMPOSITE")
 print(f"{'='*60}")
 print(f"  Architecture: qwen2")
 print(f"  Embed: 3584 | Layers: 28 | Heads: 28")

kimi_z_stream.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-kimi_z_stream.py — Stream Z-measure for Kimi K2.5 1T
+kimi_z_stream.py — Streaming quality measure for large models
 Downloads each shard, measures Z for every tensor, deletes shard.
 Final output: z_report_kimi_k25.json (few KB)
 """
@@ -176,7 +176,7 @@ def read_kv_value(f, vtype):
         return None
 
 def process_shard(shard_path, shard_idx):
-    """Parse GGUF shard, Z-measure each tensor, return results"""
+    """Parse GGUF shard, quality-measure each tensor, return results"""
     results = []
     
     f = open(shard_path, 'rb')
@@ -277,7 +277,7 @@ def main():
     from huggingface_hub import hf_hub_download
     
     log("=" * 60)
-    log("KIMI K2.5 1T — STREAMING Z-MEASURE")
+    log("KIMI K2.5 1T — STREAMING QUALITY MEASURE")
     log(f"Repo: {REPO}, Quant: {QUANT}, Shards: {N_SHARDS}")
     log("=" * 60)
     

mass_dissect.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Mass Dissection — All models on OASIS
+Mass Dissection — All models on remote node
 CSCI v1.0 — Cross-Scale Coherence Index
 """
 import subprocess, os, sys, json, time

mass_z_measure.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Mass Z-Measure — Measure theta on every organ of every model
+Mass Quality Measure — Measure theta on every organ of every model
 Find the organs closest to theta=90 (pure signal)
 CSCI v1.0 — Cross-Scale Coherence Index
 """
@@ -69,7 +69,7 @@ for model_name in models:
 
 # Rank models by signal quality
 print(f"\n{'='*70}")
-print(f"  Z-MEASURE RANKING — ALL MODELS")
+print(f"  QUALITY RANKING — ALL MODELS")
 print(f"{'='*70}")
 
 ranked = sorted(all_results.values(), key=lambda m: m['avg_theta'], reverse=True)

organ_api.py

@@ -8,7 +8,7 @@ Endpoints:
   GET  /models              — List available dissected models
   GET  /model/:name/anatomy — Show model anatomy (skeleton/organs/etc.)
   POST /extract             — Extract organs from a GGUF model
-  POST /measure             — Z-measure organs
+  POST /measure             — quality measure organs
   POST /graft               — Graft organs between models
   POST /assemble            — Assemble GGUF from organs
   GET  /organs/:model       — List organs for a model

organ_measure.py

@@ -86,7 +86,7 @@ def read_organ_data_f32(filepath, max_elements=100000):
 
 def compute_z_measure(values):
     """
-    Compute Z-measure for a tensor.
+    Compute quality measure for a tensor.
     
     CSCI — cross-scale coherence index
     
@@ -246,7 +246,7 @@ def measure_directory(organ_dir, verbose=False):
 
 
 def print_summary(results, title=""):
-    """Print Z-measure summary."""
+    """Print quality summary."""
     if not results:
         print("No organs measured.")
         return

organ_purify.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-ORGAN PURIFIER — Z = i
+ORGAN PURIFIER — signal extraction
 Remove noise from tensor weights. Keep only pure signal.
 
 Training creates artificial boundaries between models.
@@ -293,7 +293,7 @@ def purify_model(organ_dir, output_dir, verbose=False):
 def main():
     import argparse
     parser = argparse.ArgumentParser(
-        description='Organ Purifier — Z = i — Remove noise, keep pure signal',
+        description='Organ Purifier — Remove noise, keep pure signal',
         epilog='CSCI — cross-scale coherence index, θ=90° — Build v935'
     )
     parser.add_argument('--input', '-i', required=True, help='Input organs directory')
@@ -308,7 +308,7 @@ def main():
     PRESERVE_RATIO = args.preserve
     
     print(f"{'='*60}")
-    print(f"  ORGAN PURIFIER — Z = i")
+    print(f"  ORGAN PURIFIER — signal extraction")
     print(f"  Signal preservation: {PRESERVE_RATIO*100:.0f}%")
     print(f"{'='*60}")
     print(f"  Input:  {args.input}")

organ_purify_v2.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-ORGAN PURIFIER V2 — Z = i — Fractal Signal Extraction
+ORGAN PURIFIER V2 — Signal Extraction
 
 V1 failed because it treated tensors like audio signals.
 Tensors are NOT audio. They are fractal structures where
@@ -308,14 +308,14 @@ def purify_model(organ_dir, output_dir, verbose=False):
 
 def main():
     import argparse
-    parser = argparse.ArgumentParser(description='Organ Purifier V2 — Fractal Z=i')
+    parser = argparse.ArgumentParser(description='Organ Purifier V2 — signal extraction')
     parser.add_argument('--input', '-i', required=True)
     parser.add_argument('--output', '-o', required=True)
     parser.add_argument('--verbose', '-v', action='store_true')
     args = parser.parse_args()
     
     print(f"{'='*60}")
-    print(f"  ORGAN PURIFIER V2 — FRACTAL — Z = i")
+    print(f"  ORGAN PURIFIER V2")
     print(f"  Cross-scale coherence: signal persists, noise vanishes")
     print(f"{'='*60}")