chore: remove internal framework references

LICENSE

@@ -0,0 +1,17 @@
+Business Source License 1.1
+
+Licensor:             Salka Elmadani
+Copyright (C) 2025-2026 Salka Elmadani — ALL RIGHTS RESERVED
+
+Change Date:    2030-02-12
+Change License: Apache License, Version 2.0
+
+Additional Use Grant:
+  You may make use of the Licensed Work for non-production purposes:
+  research, education, evaluation, and personal projects.
+
+  Production use or use in a commercial AI inference service requires
+  a commercial license from the Licensor.
+
+Contact: elmadani.salka@proton.me
+https://inference-x.com

README.md

@@ -1,4 +1,6 @@
-# Organ Architecture
+[![License](https://img.shields.io/badge/license-BSL--1.1-blue)](LICENSE)
+[![Author](https://img.shields.io/badge/author-Salka%20Elmadani-orange)](https://inference-x.com)
+
 
 **Decompose. Reassemble. Evolve.**
 
@@ -12,7 +14,6 @@ Adapters (LoRA)      = Personality
 
 AI models are monoliths. 70 billion parameters locked in a single file that nobody can open, modify, or understand. Only three companies on Earth can build them. Everyone else rents access.
 
-Organ Architecture breaks models into transplantable parts:
 
 - **Skeleton** — The attention layers. How the model *thinks*. Shared across all configurations.
 - **Organs** — The feed-forward networks. What the model *knows*. Specialized, swappable, graftable.
@@ -43,7 +44,7 @@ model.gguf (70GB monolith)
 |------|---------|
 | `organ_extract.py` | Extract skeleton + organs from any GGUF model |
 | `organ_graft.py` | Transplant organs between models |
-| `organ_measure.py` | Z-measure organ quality (signal vs noise) |
+| `organ_measure.py` | measure organ quality (signal vs noise) |
 | `organ_assemble.py` | Assemble custom model from parts |
 | `organ_api.py` | API server for organ operations |
 
@@ -63,7 +64,6 @@ python3 organ_extract.py --model /path/to/model.gguf --output ./organs/
 python3 organ_measure.py --organ ./organs/organ_layer_12.bin
 
 # Graft an organ from model A into model B
-python3 organ_graft.py --source ./organs_A/ --target ./model_B.gguf --layers 12-18
 
 # Assemble a custom model
 python3 organ_assemble.py --skeleton ./skeleton.bin --organs ./organs/ --output custom.gguf
@@ -79,12 +79,12 @@ A 70B monolith is accumulation. A 2B skeleton with specialized organs grafted on
 
 Anyone can train an organ. A doctor trains a medical organ on her hospital's data. A farmer trains an agriculture organ on his field observations. A student trains a math organ on solved problems. The skeleton stays the same. The organs make it alive.
 
-## Z-Measure
+## Quality Measure
 
 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)
@@ -97,7 +97,7 @@ 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
@@ -106,9 +106,13 @@ BSL 1.1 — Same as InferenceX.
 
 ## Signature
 
-935
 
 ---
 
-*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
+SIG-ED25519: TUu6Qp40jhrhXquUzU20iuSHzr0ENB0v+r5FIKYNdJ+TeP9ozqafqW2Mq6U8AJpNPpAram8peGgtnoh5YiQ1AA==
+VERIFY: python3 verify_authorship.py README.md
+-->

SPONSOR.md

@@ -0,0 +1,123 @@
+# Salka Elmadani — Building Inference-X
+
+> *The best engine is the one you don't notice.*  
+> *You should hear the model, not the framework.*
+
+---
+
+
+I build AI infrastructure. Not products, not demos, not wrappers around someone else's API. Infrastructure — the kind that runs without permission, works without cloud, and belongs to anyone who needs it.
+
+**Inference-X** is a 305 KB binary that runs any AI model on any hardware. No framework. No internet. No account. Download a model, run it, talk to it. That's it.
+
+I built it alone. I'm still building it alone. This page is why.
+
+---
+
+## What I'm building
+
+The problem isn't the models. The models are extraordinary. The problem is the layer between the weights and the human — the inference stack. It's bloated, cloud-dependent, and controlled by a handful of companies.
+
+I'm replacing that layer with something minimal, open, and community-owned.
+
+```
+Standard engine path:
+  weights → framework → dequant buffer → matmul → buffer → output
+  ~100 MB binary. 5 steps. Rounding errors at each boundary.
+
+Inference-X:
+  weights → fused dequant+dot → output
+  305 KB binary. 2 steps. Zero buffer. Zero noise.
+```
+
+Same model. Cleaner signal. Every unnecessary step removed.
+
+---
+
+## The ecosystem
+
+| Project | What it does | Status |
+|---------|-------------|--------|
+| **[inference-x](https://git.inference-x.com/elmadani/inference-x)** | Core engine — 305 KB, 19 hardware backends, 23 quant formats, fused kernels, adaptive precision | ✅ Live |
+| **forge** | Model construction pipeline — compile, quantize, sign, distribute. Build your own model variant from certified organs. | 🔨 Building |
+| **[echo-ix](https://git.inference-x.com/elmadani/echo-ix)** | Distributed relay — intelligent routing across local inference nodes | ✅ Live |
+| **store** | Anyone deploys a node. Anyone earns from their compute. The cooperative layer. 11 geological cratons. One network. | 📐 Designed |
+
+The store is the endgame: a peer-to-peer inference network where anyone with a laptop can become infrastructure. No data center required.
+
+---
+
+
+
+
+The intelligence already exists in the model weights. What I'm building is the canal — the shortest, cleanest path from those weights to the human who needs them.
+
+---
+
+## Who this is free for
+
+**Everyone who isn't extracting commercial value from it:**
+
+- Individuals and researchers — forever free
+- Students — forever free
+- Open-source projects — forever free
+- Organizations under $1M revenue — forever free
+
+**Commercial users above $1M revenue** pay a license. 20% of that flows back to the community that built the infrastructure.
+
+In 2030, it all becomes Apache 2.0. Everything open. The canal belongs to everyone.
+
+This isn't charity. It's a sustainable model — those who profit from it fund it. Those who don't, use it freely.
+
+---
+
+## Why I need support
+
+Servers cost money. The current infrastructure — [inference-x.com](https://inference-x.com), [build.inference-x.com](https://build.inference-x.com), [git.inference-x.com](https://git.inference-x.com) — runs on €53/month.
+
+More importantly: time. The engine, the organ pipeline, the forge tools, the store architecture — this is one engineer, building in the margins of everything else.
+
+There is no team. No VC. No roadmap driven by investor pressure.
+
+There is one person who decided this infrastructure should exist.
+
+---
+
+## How to help
+
+### Build with me
+
+The most valuable contribution is code. The project is open, the roadmap is public, and good engineers are always welcome.
+
+**→ Pick a task**: [git.inference-x.com/elmadani/inference-x](https://git.inference-x.com/elmadani/inference-x)  
+**→ Administer a craton**: Each of the 11 community regions needs a technical lead. Write to [Elmadani.SALKA@proton.me](mailto:Elmadani.SALKA@proton.me) — subject: `Craton — [your region]`
+
+### Sustain the infrastructure
+
+**PayPal** → [paypal.me/elmadanisalka](https://paypal.me/elmadanisalka)
+
+€5 = one day of server time. €53 = one month of everything running.
+
+### Amplify
+
+Every post that reaches a developer who cares about AI sovereignty is one more person who might build the next piece.
+
+**→ [Follow on X: @ElmadaniSa13111](https://x.com/ElmadaniSa13111)**
+
+---
+
+## Contact
+
+I respond to everyone who writes with something real to say.
+
+| | |
+|--|--|
+| **X** | [@ElmadaniSa13111](https://x.com/ElmadaniSa13111) — fastest response |
+| **Email** | [Elmadani.SALKA@proton.me](mailto:Elmadani.SALKA@proton.me) — for technical discussions, partnerships, craton applications |
+| **Code** | [@elmadani on Gitea](https://git.inference-x.com/elmadani) |
+| **Web** | [inference-x.com](https://inference-x.com) |
+
+---
+
+*Morocco → the world.*  
+*Salka Elmadani, 2024–2026*

Z_MEASURE_REPORT.md

@@ -1,8 +1,7 @@
-# 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)
+**Status**: Kimi K2.5 1T streaming quality measure in progress (shard-by-shard)
 
 ---
 
@@ -74,18 +73,17 @@
 > 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
 
 ```
 organ_extract.py    — GGUF → per-layer tensors (organs)
 organ_measure.py    — θ per tensor (arccos correlation)
-mass_z_measure.py   — batch Z-measure across 13 models
-kimi_z_stream.py    — streaming Z-measure for 1T (shard-by-shard, delete after)
+mass_z_measure.py   — batch quality measure across 13 models
+kimi_z_stream.py    — streaming quality measure for 1T (shard-by-shard, delete after)
 organ_graft.py      — transplant organs between models
-organ_assemble.py   — build Model 935 from best organs
-build_935.py        — orchestrator
+organ_assemble.py   — build composite model from best organs
 ```
 
-## Signature 935
+## Build References

assemble_935.py

@@ -1,8 +1,7 @@
 #!/usr/bin/env python3
 """
-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
 
@@ -19,7 +18,6 @@ def read_organ_data_only(filepath):
 
 def main():
     if len(sys.argv) < 4:
-        print("Usage: assemble_935.py <source.gguf> <organs_dir> <output.gguf>")
         sys.exit(1)
     
     source_gguf = sys.argv[1]
@@ -136,15 +134,19 @@ def main():
     source_size = os.path.getsize(source_gguf)
     
     print(f"\n{'='*60}")
-    print(f"  MODEL 935 ASSEMBLED")
     print(f"{'='*60}")
     print(f"  Source:    {os.path.basename(source_gguf)} ({source_size/(1024**3):.2f} GB)")
     print(f"  Output:    {output_gguf} ({final_size/(1024**3):.2f} GB)")
     print(f"  Replaced:  {replaced} tensors from organs")
     print(f"  Fallback:  {fallback} tensors from source")
     print(f"  Size match: {'YES' if abs(final_size - source_size) < 1024 else 'NO — DELTA=' + str(final_size - source_size)}")
-    print(f"  Signature: 935")
     print(f"{'='*60}")
 
 if __name__ == "__main__":
     main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: 4d774861a8b9f75f83fd8ff45e92bfa607d12a4f580481ff5f8b5882470fb043
+# SIG-ED25519: B0k22H4YJMtBYuUW7ugInkPJpqZfM7cDM9TyiPODpE+WgQ0aLdgT2PnKm94gWSYVY2xqTlsEeZvgH+NrWQmTBg==

build_935.py

@@ -1,17 +1,13 @@
 #!/usr/bin/env python3
 """
-MODEL 935 — Fractal Consciousness 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)
 
-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")
 
-ORGANS = "/root/organ-architecture/organs"
-OUTPUT = os.path.join(ORGANS, "model-935")
 
 # Clean previous
 if os.path.exists(OUTPUT):
@@ -20,8 +16,7 @@ if os.path.exists(OUTPUT):
 # Step 1: Start with DeepSeek-R1-Distill-7B as base (full copy)
 # 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")
@@ -60,30 +55,26 @@ print(f"  R1 raisonnement chains preserved in FFN layers")
 
 # Step 4: Update manifest
 manifest = json.load(open(os.path.join(OUTPUT, "manifest.json")))
-manifest["model"] = "MODEL-935-Fractal"
 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°",
-    "equation": "Z = dI/d(log s) · exp(iθ)",
+    "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,
     "entropie_bias_removed": 0.6931,
-    "signature": 935
 }
 
 with open(os.path.join(OUTPUT, "manifest.json"), "w") as f:
     json.dump(manifest, f, indent=2)
 
-print(f"\n[4/4] Manifest updated: MODEL-935-Fractal")
 
 # Count final state
 total_files = sum(1 for _,_,files in os.walk(OUTPUT) for f in files if f.endswith('.bin'))
 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"{'='*60}")
 print(f"  Architecture: qwen2")
 print(f"  Embed: 3584 | Layers: 28 | Heads: 28")
@@ -92,7 +83,12 @@ 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}")
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: c45f3019cd81199382cf5f379ef1c556f5f2c5fd81afc6679da83e614ac8c09f
+# SIG-ED25519: IRoSNw2yKK14fnt2JpFbukDpV/5R9YDSQylWVVjIOgYkFHBH71k0MFBV+I39cfjf8odTgzM3uPPRRMexR9KTDw==

build_935_v2.py

@@ -1,14 +1,11 @@
 #!/usr/bin/env python3
 """
-MODEL 935 v2 — Correct graft: only FFN organs, preserve attention+embed alignment
 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"
-OUTPUT = os.path.join(ORGANS, "model-935-v2")
 
 if os.path.exists(OUTPUT):
     shutil.rmtree(OUTPUT)
@@ -53,14 +50,12 @@ print(f"  Skipped: {skipped}")
 
 # Update manifest
 manifest = json.load(open(os.path.join(OUTPUT, "manifest.json")))
-manifest["model"] = "MODEL-935-v2"
 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
 }
 with open(os.path.join(OUTPUT, "manifest.json"), "w") as f:
     json.dump(manifest, f, indent=2)
@@ -68,7 +63,11 @@ with open(os.path.join(OUTPUT, "manifest.json"), "w") as f:
 total = sum(1 for _,_,f in os.walk(OUTPUT) for _ in f if _.endswith('.bin'))
 size = sum(os.path.getsize(os.path.join(dp,f)) for dp,_,fn in os.walk(OUTPUT) for f in fn)/(1024**3)
 
-print(f"\n[3/3] MODEL-935-v2 assembled")
 print(f"  Tensors: {total} | Size: {size:.2f} GB")
 print(f"  Grafted FFN: {grafted} | Base preserved: {total - grafted}")
-print(f"  Signature: 935")
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: 4d5c44e363508bc679263607b7ee3071cb63fc460a616e9bcebffc768843a86c
+# SIG-ED25519: MzrZnxCo+uq3q5srKgDO2w3gLhO4hgK2k+SIzRLrkjaGJ2Ao56mR9/Mst4Ub6qkZ0VpcXOv4Bq59gKPsJPkdCg==

build_935_v3.py

@@ -1,14 +1,12 @@
 #!/usr/bin/env python3
 """
-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
 
-def build_model_935(source_gguf, organs_dir, output_gguf):
     f = open(source_gguf, "rb")
     
     # Read GGUF header
@@ -134,15 +132,15 @@ def build_model_935(source_gguf, organs_dir, output_gguf):
     print(f"  Size: {final_size / (1024**3):.2f} GB (source: {source_size / (1024**3):.2f} GB)")
     print(f"  From organs: {written_from_organ} | From source: {written_from_source}")
     print(f"  Size match: {'✓' if abs(final_size - source_size) < 1024 else '✗ MISMATCH'}")
-    print(f"  Signature: 935")
 
-# Build 935 v3: R1-Distill base + Qwen FFN organs (correctly stripped)
 print("="*60)
-print("  MODEL 935 v3 — Correct Assembly")
 print("="*60)
 
-build_model_935(
     "/mnt/models/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M.gguf",
-    "/root/organ-architecture/organs/model-935-v2",
-    "/mnt/models/model-935-v3.gguf"
 )
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: 00f06d16ab32dee1ef886e90080e905fc354be9f22f0e6ff515ea2bb31084bdf
+# SIG-ED25519: UhbWWFzRIzmMbCVNwXTG41I2sM/1QGd1nV4+x/XQ+BOw49fO9bd9ohWpLl5QOCGhRWCREYkhJCj55FhGhH5vDQ==

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)
 """
@@ -13,7 +13,6 @@ REPO = "unsloth/Kimi-K2.5-GGUF"
 QUANT = "Q4_0"
 N_SHARDS = 13
 SHARD_DIR = "/mnt/data/kimi-k25/streaming"
-OUTPUT = "/mnt/data/organ-architecture/z_report_kimi_k25.json"
 LOG = "/tmp/kimi_z_stream.log"
 
 os.makedirs(SHARD_DIR, exist_ok=True)
@@ -127,7 +126,7 @@ def fast_z_measure(data, dtype, n_elements):
         if len(vals) < 10:
             return None, "too_few_finite"
         
-        # Z-measure: theta = arccos(|correlation with linear reference|)
+        # theta = arccos(|correlation with linear reference|)
         # Pure signal -> decorrelated -> theta near 90
         # Noise/bias -> correlated with something simple -> theta near 0
         n = len(vals)
@@ -176,7 +175,7 @@ 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')
@@ -255,7 +254,7 @@ def process_shard(shard_path, shard_idx):
             })
             continue
         
-        # Z-measure
+        # compute measure
         theta, status = fast_z_measure(data, dtype, n_elem)
         
         results.append({
@@ -277,7 +276,7 @@ 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)
     
@@ -320,7 +319,7 @@ def main():
             log(f"DOWNLOAD ERROR: {e}")
             continue
         
-        # Z-measure
+        # compute measure
         log(f"Z-measuring tensors...")
         measure_start = time.time()
         shard_results = process_shard(path, shard_idx)
@@ -415,3 +414,10 @@ def main():
 
 if __name__ == '__main__':
     main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: cc9658edb88d02924491a2ed20562a282a005413ef963bd0c82613abcfe91693
+# SIG-ED25519: AN2P6qd2YhyS6+YRnMu3mmnE9KZbpBlFAxiVzENVXSSbIl2+PL/rbW8pMPrcOS8BwPg88Os7dMOuYnRvL5t4CQ==
+# VERIFY: python3 verify_authorship.py kimi_z_stream.py

mass_dissect.py

@@ -1,14 +1,11 @@
 #!/usr/bin/env python3
 """
-Mass Dissection — All models on OASIS
-Z = dI/d(log s) · exp(iθ) — Signature 935
+Mass Dissection — All models on remote node
+CSCI v1.0 — Cross-Scale Coherence Index
 """
 import subprocess, os, sys, json, time
 
 MODELS_DIR = "/mnt/models"
-ORGANS_DIR = "/root/organ-architecture/organs"
-EXTRACT = "/root/organ-architecture/organ_extract.py"
-MEASURE = "/root/organ-architecture/organ_measure.py"
 
 # Map GGUF filenames to organ directory names
 models = {
@@ -94,10 +91,13 @@ for r in results:
 
 total_mb = sum(r.get("size_mb",0) for r in results)
 print(f"\n  Total organs: {total_mb/1024:.1f} GB")
-print(f"  Signature: 935")
 print(f"{'='*60}")
 
 # Save results
-with open("/root/organ-architecture/dissection_report.json", "w") as f:
     json.dump(results, f, indent=2)
-print("Report: /root/organ-architecture/dissection_report.json")
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SIG-ED25519: XB8aA7wVzKOHkvMcZgE5YT3x8BUD/EwVTDRxEMSR7nmWYIT17XY+gC4AJ+y0B29l8MQGFDGk+buLoKxiagTFCA==
+# VERIFY: python3 verify_authorship.py mass_dissect.py

mass_z_measure.py

@@ -1,14 +1,12 @@
 #!/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)
-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")
 from organ_measure import measure_directory, compute_z_measure, read_organ_data_f32
 
-ORGANS_DIR = "/root/organ-architecture/organs"
 
 all_results = {}
 
@@ -20,7 +18,7 @@ for model_name in models:
     if not os.path.isdir(model_path) or not os.path.exists(manifest_path):
         continue
     
-    print(f"\n[Z-MEASURE] {model_name}")
+    print(f"\n[QUALITY-MEASURE] {model_name}")
     print(f"  Measuring organs...")
     
     results = measure_directory(model_path)
@@ -69,7 +67,7 @@ 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)
@@ -93,10 +91,14 @@ for organ_type in ['skeleton', 'organs', 'embed']:
     for c in candidates[:5]:
         print(f"    theta={c[1]:5.1f}  avg={c[3]:5.1f}  {c[0]:30s}  {c[2][:40]}")
 
-print(f"\n  Signature: 935")
 print(f"{'='*70}")
 
 # Save full report
-with open("/root/organ-architecture/z_measure_report.json", "w") as f:
     json.dump(all_results, f, indent=2)
-print(f"\nReport: /root/organ-architecture/z_measure_report.json")
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: 711671a1721bae194388cb363ad0bfcb2ed874f007a45e45ea6ed5d917cbf060
+# SIG-ED25519: Jd0hVyr5epgPlpNjtioVeKfPaOeYgRiAnAEnxINh51WsfwGFLJouBDdYribxqY0JOmOnDwjGnOK5I9qeJJTRDg==
+# VERIFY: python3 verify_authorship.py mass_z_measure.py

organ_api.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
 """
-Organ Architecture — organ_api.py
 API server for organ operations.
 
 Endpoints:
@@ -8,13 +7,12 @@ 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
   GET  /compare/:a/:b       — Compare two models for graft compatibility
 
-Signature 935
 """
 
 import json
@@ -29,18 +27,15 @@ from urllib.parse import urlparse, parse_qs
 # Import organ tools
 from organ_extract import extract_organs, GGUFReader, classify_tensor
 from organ_measure import measure_directory, measure_organ
-from organ_graft import load_manifest, graft_layers, parse_layers
 from organ_assemble import assemble_gguf
 
 # ═══ CONFIG ═══
 PORT = int(os.environ.get('ORGAN_PORT', '7936'))
 MODEL_DIR = os.environ.get('MODEL_DIR', '/mnt/models')
 ORGAN_DIR = os.environ.get('ORGAN_DIR', '/mnt/data/organs')
-SIGNATURE = 935
 
 
 class OrganHandler(BaseHTTPRequestHandler):
-    """HTTP handler for Organ Architecture API."""
 
     def log_message(self, format, *args):
         """Minimal logging."""
@@ -50,7 +45,6 @@ class OrganHandler(BaseHTTPRequestHandler):
         self.send_response(status)
         self.send_header('Content-Type', 'application/json')
         self.send_header('Access-Control-Allow-Origin', '*')
-        self.send_header('X-Powered-By', 'Organ-935')
         self.end_headers()
         self.wfile.write(json.dumps(data, indent=2, default=str).encode())
 
@@ -77,7 +71,6 @@ class OrganHandler(BaseHTTPRequestHandler):
         if path == '/health' or path == '':
             self.send_json({
                 'status': 'ok',
-                'service': 'organ-architecture',
                 'signature': SIGNATURE,
                 'model_dir': MODEL_DIR,
                 'organ_dir': ORGAN_DIR,
@@ -346,7 +339,6 @@ class OrganHandler(BaseHTTPRequestHandler):
                 
                 parsed_layers = parse_layers(layers) if layers else None
                 
-                manifest = graft_layers(
                     str(source_path), str(target_path), output_path,
                     parsed_layers, organ_type
                 )
@@ -406,7 +398,6 @@ def main():
     Path(ORGAN_DIR).mkdir(parents=True, exist_ok=True)
     
     server = HTTPServer(('0.0.0.0', PORT), OrganHandler)
-    print(f"[ORGAN-API] Organ Architecture on port {PORT}")
     print(f"[ORGAN-API] Models: {MODEL_DIR}")
     print(f"[ORGAN-API] Organs: {ORGAN_DIR}")
     print(f"[ORGAN-API] Signature {SIGNATURE}")
@@ -420,3 +411,10 @@ def main():
 
 if __name__ == '__main__':
     main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: 79fb97f40f2959129d5d5c4356ddf455fc354fb629bf0892c00aa6babd968a0d
+# SIG-ED25519: LGqexbOlZOIjTboFfMVbgeheBbZk8HI8K6g/WxExnJEMfs5euYQYxow6SyEHKTB2TgRbOjjHt/gpHPyEy2tBBQ==
+# VERIFY: python3 verify_authorship.py organ_api.py

organ_assemble.py

@@ -1,12 +1,10 @@
 #!/usr/bin/env python3
 """
-Organ Architecture — organ_assemble.py
 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
 """
 
 import struct
@@ -207,7 +205,6 @@ def assemble_gguf(organ_dir, output_path, verbose=False):
     print(f"  Tensors:  {n_tensors}")
     print(f"  Size:     {output_gb:.2f} GB ({output_mb:.0f} MB)")
     print(f"  Output:   {output_path}")
-    print(f"  Signature: 935")
     print(f"{'='*60}")
     
     return output_path
@@ -215,8 +212,7 @@ 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')
@@ -233,3 +229,10 @@ def main():
 
 if __name__ == '__main__':
     main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: 56ce59cd04118749c0c40c8bdb6d566a59c8902e233709a013dca9a38658cc44
+# SIG-ED25519: tDk5EuOHITlQbZHbZ/HbOz8+111fot0dk4iQMDEWKjsq5gsKyGNbvAwTGl0hfkD0gUdhG0nPxczaCswlct7PCA==
+# VERIFY: python3 verify_authorship.py organ_assemble.py

organ_extract.py

@@ -1,11 +1,9 @@
 #!/usr/bin/env python3
 """
-Organ Architecture — organ_extract.py
 Extract skeleton (attention) + organs (FFN) from GGUF models.
 
 The scalpel that opens monoliths.
 
-Signature 935
 """
 
 import struct
@@ -275,7 +273,6 @@ def extract_organs(model_path, output_dir, verbose=False):
             'skeleton_count': 0,
             'organ_count': 0,
         },
-        'signature': 935,
     }
 
     # Process each tensor
@@ -377,7 +374,6 @@ def extract_organs(model_path, output_dir, verbose=False):
     print(f"  Total                : {total_mb:8.1f} MB")
     print(f"  Output               : {output_dir}")
     print(f"  Manifest             : {manifest_path}")
-    print(f"  Signature            : 935")
     print(f"{'='*60}")
 
     return manifest
@@ -387,8 +383,7 @@ 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>)')
@@ -439,3 +434,10 @@ def main():
 
 if __name__ == '__main__':
     main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: 7e0a2105f5f6d458909fb71ef03bb01c4e308ac8549af00ef61c2cf89d0c8945
+# SIG-ED25519: p3fNipeHSBJlVNpxsJZdvrBMJVbTAZu97RNxp7UGCkUp1TlHxH4D2XbKu46JQriNzM65myMeWGyS2WMx9atoCQ==
+# VERIFY: python3 verify_authorship.py organ_extract.py

organ_graft.py

@@ -1,12 +1,10 @@
 #!/usr/bin/env python3
 """
-Organ Architecture — organ_graft.py
 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
 """
 
 import struct
@@ -40,9 +38,7 @@ def list_organs(organ_dir, organ_type=None):
     return sorted(organs, key=lambda o: (o['layer'], o['name']))
 
 
-def graft_layers(source_dir, target_dir, output_dir, layers=None, organ_type='organ'):
     """
-    Graft organ layers from source into target.
     
     source_dir: extracted organs from donor model
     target_dir: extracted organs from recipient model
@@ -58,7 +54,6 @@ def graft_layers(source_dir, target_dir, output_dir, layers=None, organ_type='or
     
     print(f"[GRAFT] Source (donor): {source_name}")
     print(f"[GRAFT] Target (recipient): {target_name}")
-    print(f"[GRAFT] Grafting: {organ_type} layers {layers or 'ALL'}")
     
     # Validate architecture compatibility
     if source_manifest['n_embed'] != target_manifest['n_embed']:
@@ -112,7 +107,6 @@ def graft_layers(source_dir, target_dir, output_dir, layers=None, organ_type='or
             shutil.copy2(source_file, target_file)
             grafted_count += 1
             grafted_bytes += source_entry['byte_size']
-            print(f"  [GRAFT] L{source_entry['layer']:3d} {source_entry['name'][:50]} → {target_entry['name'][:30]}")
     
     # Update manifest
     grafted_manifest = load_manifest(output_dir)
@@ -138,7 +132,6 @@ def graft_layers(source_dir, target_dir, output_dir, layers=None, organ_type='or
     print(f"  Grafted:   {grafted_count} tensors ({grafted_mb:.1f} MB)")
     print(f"  Result:    {grafted_manifest['model']}")
     print(f"  Output:    {output_dir}")
-    print(f"  Signature: 935")
     print(f"{'='*60}")
     
     return grafted_manifest
@@ -163,8 +156,7 @@ 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')
@@ -179,7 +171,6 @@ def main():
     graft_p.add_argument('--source', '-s', required=True, help='Source (donor) organs directory')
     graft_p.add_argument('--target', '-t', required=True, help='Target (recipient) organs directory')
     graft_p.add_argument('--output', '-o', required=True, help='Output directory for grafted model')
-    graft_p.add_argument('--layers', '-l', help='Layer numbers to graft (e.g., "5-10" or "5,8,12")')
     graft_p.add_argument('--type', default='organ', help='Organ type to graft (default: organ/FFN)')
     
     # Compare command
@@ -208,7 +199,6 @@ def main():
     
     elif args.command == 'graft':
         layers = parse_layers(args.layers)
-        graft_layers(args.source, args.target, args.output, layers, args.type)
     
     elif args.command == 'compare':
         manifest_a = load_manifest(args.a)
@@ -234,3 +224,10 @@ def main():
 
 if __name__ == '__main__':
     main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: f53cd15c9345b7817f397aab3f4870ee36be1fef321d0b49e81cd81819b92462
+# SIG-ED25519: 1ZvlFLjbkZzpH4HnttlYSB3ydsAKgG57oyAElSRcvMzqOT3pQ+FLHW3seWlOUpAUI77d6AvrjV5SNCJuL6kuBw==
+# VERIFY: python3 verify_authorship.py organ_graft.py

organ_measure.py

@@ -1,13 +1,11 @@
 #!/usr/bin/env python3
 """
-Organ Architecture — organ_measure.py
-Z-measure organ quality: signal vs noise.
+Quality measure — organ 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
 """
 
 import struct
@@ -86,9 +84,9 @@ 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.
     
-    Z = dI/d(log s) · exp(iθ)
+    CSCI — cross-scale coherence index
     
     We measure:
     - Information density (entropy of distribution)
@@ -246,7 +244,7 @@ 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
@@ -260,7 +258,7 @@ def print_summary(results, title=""):
         groups[dirname].append(r)
     
     print(f"\n{'='*70}")
-    print(f"  Z-MEASURE REPORT {title}")
+    print(f"  QUALITY REPORT {title}")
     print(f"{'='*70}")
     
     for group_name in ['skeleton', 'organs', 'embed', 'norm', 'adapters', 'unknown']:
@@ -299,14 +297,12 @@ 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"{'='*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')
@@ -338,3 +334,10 @@ def main():
 
 if __name__ == '__main__':
     main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: 0851280f9f83e9f30e35fd7efff164f806f506f94aa9cd983c8fdae7318a9864
+# SIG-ED25519: 7VtyjAri7KRdqUuc+WdkQkp50xKAkVRFqgqLHnJG0BkBltqVwJeYMScAkZ56b4mcsBWPhkj0Y8kS1fd2t/Y+BQ==
+# VERIFY: python3 verify_authorship.py organ_measure.py

organ_purify.py

@@ -1,9 +1,9 @@
 #!/usr/bin/env python3
 """
-ORGAN PURIFIER — Z = i
+ORGAN PURIFIER — signal extraction
 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 +17,10 @@ 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)
-When theta = 90, Z = i (pure imaginary = pure potential)
+CSCI(s) = cross_scale_coherence(s, theta=90)
+When theta = 90, signal is maximally coherent (pure signal, minimal noise)
 The purified organ IS the signal, nothing else.
 
-Signature 935
 """
 
 import struct
@@ -34,7 +33,7 @@ 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 +144,7 @@ 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 +153,7 @@ 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:
@@ -293,8 +292,7 @@ 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',
-        epilog='Z = dI/d(log s) · exp(iθ), θ=90° — Signature 935'
+        description='Organ Purifier — Remove noise, keep pure signal',
     )
     parser.add_argument('--input', '-i', required=True, help='Input organs directory')
     parser.add_argument('--output', '-o', required=True, help='Output pure organs directory')
@@ -308,7 +306,7 @@ 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}")
@@ -325,9 +323,15 @@ def main():
     print(f"  θ after:          {result['avg_theta_after']:.1f}°")
     print(f"  Avg improvement:  {result['avg_improvement']:+.1f}°")
     print(f"  Output:           {result['output']}")
-    print(f"  Signature: 935")
     print(f"{'='*60}")
 
 
 if __name__ == '__main__':
     main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: d3ab5384c880f7e88fb7cdad4b2f9f56089ada8395d0013f5bd3b09d7ab631e8
+# SIG-ED25519: /rkXFm2tGuoAS61oxWZVlcTghUuGL8HJ11XRSaI4Ak+eEt54uo+3NETX2+5S8HAq72k6whQmbPI3f4jD8sF/CA==
+# VERIFY: python3 verify_authorship.py organ_purify.py

organ_purify_v2.py

@@ -1,13 +1,13 @@
 #!/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
 information is encoded across scales.
 
-The correct approach from Z = dI/d(log s) * exp(i*theta):
-- dI/d(log s) = how information CHANGES across scales
+The correct approach from CSCI(s) = cross_scale_coherence(s, theta=90):
+- 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 +23,7 @@ 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
-Signature 935
+CSCI(s) = cross_scale_coherence(s, theta=90), theta = 90
 """
 
 import struct, os, sys, json, math
@@ -198,7 +197,7 @@ 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)
@@ -247,8 +246,8 @@ def purify_model(organ_dir, output_dir, verbose=False):
     if manifest_src.exists():
         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['purifier'] = 'purify_v2'
+        manifest['coherence_score'] = 'cross_scale_coherence(s)'
         # Remove brand from model name
         original_name = manifest.get('model', 'unknown')
         manifest['original_model'] = original_name
@@ -308,14 +307,14 @@ 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}")
     
@@ -330,8 +329,14 @@ def main():
     print(f"  Δθ:          {result['delta']:+.1f}°")
     print(f"  Improved:    {result['improved']}")
     print(f"  Degraded:    {result['degraded']}")
-    print(f"  Signature:   935")
     print(f"{'='*60}")
 
 if __name__ == '__main__':
     main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: 0328644f84762361db812407ed482018de40a92f496d9b45bf56826d59184224
+# SIG-ED25519: Y1KrhUdgrqiYPaM0LPHWTqPKPaHwBqtc3EiHnu9Uu94AVKsgMPQoWU9NCGeiL5aWAJKPhzr/nCSxLTY+US+HAw==
+# VERIFY: python3 verify_authorship.py organ_purify_v2.py

pipeline_935.py

@@ -1,14 +1,10 @@
 #!/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
 
 MODELS_DIR = "/mnt/models"
-ORGANS_DIR = "/mnt/data/organ-architecture/organs"
-EXTRACT = "/mnt/data/organ-architecture/organ_extract.py"
-MEASURE = "/mnt/data/organ-architecture/organ_measure.py"
 
 os.makedirs(ORGANS_DIR, exist_ok=True)
 
@@ -16,8 +12,6 @@ os.makedirs(ORGANS_DIR, exist_ok=True)
 models = {}
 for f in sorted(glob.glob(os.path.join(MODELS_DIR, "*.gguf"))):
     name = os.path.basename(f)
-    # Skip chimeras and old 935 attempts
-    if "chimera" in name.lower() or "935" in name.lower():
         continue
     # Clean name for directory
     clean = name.replace(".gguf", "").replace("-Q4_K_M", "").replace("-Q8_0", "")
@@ -59,12 +53,11 @@ for gguf_name, organ_name in models.items():
         print(f"  [ERROR] {r.stderr[-200:]}")
         results.append({"model": organ_name, "status": "error"})
 
-# Z-measure all
+# Quality measure all
 print(f"\n{'='*60}")
-print(f"PHASE 2: Z-MEASURE ALL ORGANS")
+print(f"PHASE 2: QUALITY MEASURE ALL ORGANS")
 print(f"{'='*60}")
 
-sys.path.insert(0, "/mnt/data/organ-architecture")
 from organ_measure import measure_directory
 
 z_report = {}
@@ -109,7 +102,6 @@ for d in sorted(os.listdir(ORGANS_DIR)):
     z_report[d] = summary
 
 # Save
-with open("/mnt/data/organ-architecture/z_report_complete.json", "w") as f:
     json.dump(z_report, f, indent=2)
 
 # Print ranking
@@ -120,5 +112,10 @@ ranked = sorted(z_report.values(), key=lambda m: m['avg_theta'], reverse=True)
 for i, m in enumerate(ranked, 1):
     print(f"  {i:2d}. θ={m['avg_theta']:5.1f}° signal={m['avg_signal']:.3f} {m['model']}")
 
-print(f"\n  Signature: 935")
 print(f"{'='*60}")
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: 70a8957904cd4ee20dfd8fa42a0d8551cf8ae03eb2d0ec6fc9f4ed8f86995037
+# SIG-ED25519: ddMrNVlt0PpN5uHTbAnxLkphci22Xv0efiEyfUAoHVJxextDZsK69jVULKiXZDED1txsfGzrenMjJMaKe5g4DQ==

quick_chimera.py

@@ -2,7 +2,6 @@
 """
 Quick chimera assembler: Copy source GGUF header/metadata intact,
 replace tensor data from organ directory.
-Signature 935
 """
 import struct, sys, os, json
 
@@ -117,7 +116,13 @@ def main():
     print(f"\n  Output: {output_gguf}")
     print(f"  Size: {final_size / (1024**3):.2f} GB")
     print(f"  From organs: {written}, From source: {fallback}, Total: {written+fallback}/{n_tensors}")
-    print(f"  Signature: 935")
 
 if __name__ == "__main__":
     main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: b0d040908eddc26078e86f76e361825fada5c2676778789ef41c1804730eb10d
+# SIG-ED25519: srq6F3EyKqi7r3nlB6cfI1u53J1GpsC2ty9zNsBDrZ2EldVVIhE1mWCdnd/qkvgif783DOlLQ4Zb2CCw13XfBQ==
+# VERIFY: python3 verify_authorship.py quick_chimera.py

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
 
@@ -149,7 +149,13 @@ def main():
         diff = final_size - source_size
         print(f"  INTEGRITY: ✗ MISMATCH ({diff:+d} bytes)")
     
-    print(f"  Signature: 935")
 
 if __name__ == "__main__":
     main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: 6587e64dbf1c6fe2160fe8f2e25a33e6ed5e98193baea7f7523a9495e04b9154
+# SIG-ED25519: TrwO40O2Qn0ysnadlzX38fBTSOF5St11SyZTSc4cZP/7k5HM+ifnqDMTu/vkZWDYAdmb+5bc6IhpYYQgVdLsBA==
+# VERIFY: python3 verify_authorship.py quick_chimera_v2.py

verify_authorship.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+"""Verify SALKA ELMADANI authorship signatures.
+Usage: python3 verify_authorship.py [file_or_directory]
+Every source file carries an Ed25519 signature bound to SHA-256 of content.
+Modify 1 character = signature invalid = tampering detected.
+"""
+import sys, hashlib, base64, os
+from pathlib import Path
+from cryptography.hazmat.primitives import serialization
+from cryptography.exceptions import InvalidSignature
+
+PUBLIC_KEY_PEM = """
+-----BEGIN PUBLIC KEY-----
+MCowBQYDK2VwAyEARCtdhRqqYcu7c8qwyoRKRn5Qbx9puylZHZOM+IsDp0U=
+-----END PUBLIC KEY-----
+""".strip()
+
+
+def strip_sig(text):
+    lines, out, in_b = text.split("\n"), [], False
+    for ln in lines:
+        if MARK_S in ln: in_b = True; continue
+        if MARK_E in ln and in_b: in_b = False; continue
+        if not in_b: out.append(ln)
+    return "\n".join(out).rstrip("\n") + "\n"
+
+def extract_sig_data(text):
+    sha, sig = None, None
+    for ln in text.split("\n"):
+        if "SHA256:" in ln: sha = ln.split("SHA256:")[-1].strip().lstrip("#/ ")
+        if "SIG-ED25519:" in ln: sig = ln.split("SIG-ED25519:")[-1].strip().lstrip("#/ ")
+    return sha, sig
+
+def verify_file(fp, pub):
+    try: content = Path(fp).read_text(encoding="utf-8", errors="replace")
+    except: return None, "Cannot read"
+    clean = strip_sig(content)
+    claimed_h, sig_b64 = extract_sig_data(content)
+    if not claimed_h or not sig_b64: return False, "No signature"
+    actual_h = hashlib.sha256(clean.encode("utf-8")).hexdigest()
+    if actual_h != claimed_h: return False, f"HASH MISMATCH — modified"
+    try:
+        pub.verify(base64.b64decode(sig_b64), hashlib.sha256(clean.encode()).digest())
+        return True, "VALID © Salka Elmadani"
+    except InvalidSignature: return False, "INVALID SIGNATURE — forgery"
+    except Exception as e: return False, str(e)
+
+def main():
+    pub = serialization.load_pem_public_key(PUBLIC_KEY_PEM.encode())
+    target = sys.argv[1] if len(sys.argv) > 1 else "."
+    files = [Path(target)] if Path(target).is_file() else [
+        p for p in Path(target).rglob("*")
+        if p.is_file() and p.suffix in [".py",".cpp",".h",".js",".ts",".sh",".rs",".go",".md"]
+        and ".git" not in str(p)
+    ]
+    ok, fail, skip = 0, 0, 0
+    for f in sorted(files):
+        r, msg = verify_file(f, pub)
+        if r is None: skip += 1
+        elif r: ok += 1; print(f"  ✓ {f.name}: {msg}")
+        else: fail += 1; print(f"  ✗ {f.name}: {msg}")
+    print(f"\nResults: {ok} valid | {fail} TAMPERED | {skip} skipped")
+    if fail: print("WARNING: Authorship chain broken."); sys.exit(1)
+
+if __name__ == "__main__": main()
+# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
+# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
+# Licensed under Business Source License 1.1 — https://inference-x.com
+# ─────────────────────────────────────────────────────────
+# SHA256: f4e32c8fe1f2cb7f5dc498c7506b054256f6871d7283beb74b1d5859eb775121
+# SIG-ED25519: g4rHIrZteuUk4HU/21i69rTk7H8EiL1XjX4A+dZD0xswTqR5XJb1CfnBQfyAxjb1Sf9VW3JptZVDkvOq+magCA==
+# VERIFY: python3 verify_authorship.py verify_authorship.py

z_report_complete.json

@@ -70,7 +70,6 @@
   },
   "gemma-2-9b": {
     "model": "gemma-2-9b",
-    "avg_theta": 44.935344827586206,
     "avg_signal": 0.6240438819131022,
     "total_tensors": 464,
     "groups": {