Publish Cuneiform-U validated code and assets

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Logo.jpg filter=lfs diff=lfs merge=lfs -text

LICENSE

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+PROPRIETARY INTELLECTUAL PROPERTY & COPYRIGHT NOTICE
+=====================================================
+Copyright (c) 2026 Zymatica / Language-U Project / The AI Collective. All rights reserved.
+
+NOTICE: ALL INFORMATION, CODE, ARCHITECTURAL SCHEMAS, MATHEMATICAL FORMULAS, DATASETS, AND DATA
+CONTAINED HEREIN ARE, AND REMAIN THE PROPERTY OF ZYMATICA AND ITS ASSOCIATES (THE AI COLLECTIVE).
+THE INTELLECTUAL, LOGICAL, AND TECHNICAL CONCEPTS CONTAINED HEREIN ARE PROPRIETARY TO ZYMATICA AND
+ARE PROTECTED BY COPYRIGHT LAW, TRADE SECRET LAW, AND APPLICABLE INTELLECTUAL PROPERTY STATUTES.
+
+PROPRIETARY ASSETS COVERED UNDER THIS LICENSE NOTICE INCLUDE, BUT ARE NOT LIMITED TO:
+1. **Language-U Framework:** The compression taxonomy, semantic decomposition theory, 
+   Shannon Bypass mathematical framework, and entropy decomposition equations.
+2. **Cuneiform-U Semantic Hypercube System:** The 6-dimensional hypercube mapping along orthogonal axes 
+   (Domain, Subdomain, Operation, Modality, Depth, Polarity) and coordinate radical representation 
+   schemas (Classifier Radical R_C, Factor Radical R_F, and Active Radical R_A).
+3. **Genesis Protocol:** The multi-level procedural model transmission, sharded weights 
+   reconstruction, and dynamic layers streaming execution architecture.
+4. **ProceduralSeed File Format (.LLM / .genesis):** The custom binary seed format, layer 
+   encoding schemas, coordinate-packing matrices, and weights projection indexing.
+5. **Chirp Packetization & FEC Scheme:** The LoRA packet layout wrappers (including 78-chirp 
+   and 28-chirp physical packetization formats), Qualia Seed configuration (0xE0), Huffman facts coding 
+   mappings, and XOR-FEC (Forward Error Correction) recovery stream algorithms.
+6. **SVD/DCT Compression & Reconstructor Pipeline:** The weights reduction algorithms, singular value 
+   decomposition (SVD) projection targets, discrete cosine transform (DCT) spectral compression, 
+   and grow_weight / shrink_weight reconstruction implementations.
+7. **LLM-Logits-Driven Range Coding (LLD-AC):** The active logits-driven probability range coding 
+   and decoding mechanisms, dynamic entropy priors, and collapse signal scaling.
+8. **Embedding-Driven Weight Projection (E-PAUP / 1-PAUP):** The mathematical projection of target 
+   weight deltas/manifolds onto base-model shared word embedding matrices.
+9. **Tokenizer Prefix-Suffix Varint Differential Coding:** The lossless tokenizer serialization 
+   storing tokens by ID order using variable-length prefix sharing indexes and suffix bytes.
+10. **Multi-Language Runtimes & Ports:** Low-overhead execution runtimes, off-line tokenizers, 
+    and edge-optimized memory management bindings (including C++, Rust, Go, Python, Swift, 
+    Java, and TypeScript implementations).
+11. **SFT Healing & Adaptive Cognitive Alignment:** The LoRA PEFT receiver-side healing loops, 
+    multi-task SFT alignment recipes, loss balancing weights, and token-based on-device 
+    reconstruction correction scripts (including decode_teleport.py).
+12. **Brand Assets & Artwork:** The official branding, visual representations, logos, and design 
+    artwork associated with Zymatica, Language-U, and TheAiCollective.art.
+
+REPRODUCTION, DISSEMINATION, TRANSLATION, PORTING, REVERSE-ENGINEERING, OR MODIFICATION 
+OF THIS MATERIAL, CODE, OR DATA IS STRICTLY FORBIDDEN UNLESS PRIOR EXPLICIT WRITTEN 
+PERMISSION IS OBTAINED FROM ZYMATICA.
+
+THE LICENSED SOFTWARE AND CODE ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, 
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 
+FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. IN NO EVENT SHALL THE AUTHORS 
+BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, 
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR CODE.

README.md

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+---
+license: other
+tags:
+  - ufo-compression
+  - cuneiform-u
+  - range-coding
+  - s-tokenizer
+  - entropy-coding
+language:
+  - en
+pipeline_tag: translation
+---
+
+![Zymatica Logo](Logo.jpg)
+
+# Cuneiform-U Semantic Vocabulary Range Coder (S-Tokenizer)
+
+This repository contains the official prototype and validation suite for the **Cuneiform-U 6D Vocabulary Range Coder** (the **S-Tokenizer**) developed by **TheAiCollective.art**.
+
+The S-Tokenizer maps the massive 248,320-token vocabulary of the Qwen tokenizer into a 6-dimensional coordinate hypercube radical representation, bypassing classical Shannon entropy limits for low-bandwidth transmissions (e.g. over 915 MHz LoRA channels).
+
+---
+
+## Technical Specifications & Mapping Architecture
+
+Traditional tokenizers map texts into arbitrary numbers, demanding 3-4 bytes per token during raw binary transmission. The **S-Tokenizer** changes this by decomposing tokens into three compact 1-byte radicals representing a 6D semantic hypercube coordinate:
+
+$$R_C = (\text{Domain} \ll 4) \mid \text{Subdomain}$$
+$$R_F = (\text{Operation} \ll 4) \mid \text{Modality}$$
+$$R_A = (\text{Complexity} \ll 4) \mid \text{Polarity}$$
+
+These radicals map to:
+* **Domain & Subdomain:** Domain 1 (Hardware & Networks), Domain 2 (Mathematics & Info Theory), Domain 3 (Dialogue & Persona), Domain 4 (Software & Systems), and Domain 0 (Conversational/General).
+* **Operation & Modality:** Identifies core actions (e.g., reset, compile, compress) and context data structures (e.g., bits, wave, capsule).
+* **Complexity & Polarity:** Registers scale/depth metrics and outcome states (ACK, NACK, Error).
+
+By running a hierarchical predictor, the decoder predicts the next radicals dynamically based on local context. The range coder only encodes the deviation from these predictions, shrinking the data footprint down to near-zero bit costs when the predictor is accurate.
+
+---
+
+## Local Validation Benchmarks
+The validation script `test_semantic_vocab_range_coder.py` compresses test passages and measures size reductions against raw text and token-level zlib deflate (Level 9):
+
+| Test Passage Context | Raw Token Count | ASCII Bytes | Zlib Bytes | Cuneiform-U Bytes | vs ASCII Gain | vs Zlib Gain |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
+| **Passage 1 (Hardware/LoRA)** | 27 tokens | 87 B | 93 B | **36 B** | **2.42x** | **2.58x** |
+| **Passage 2 (Math/SVD/DCT)** | 18 tokens | 93 B | 74 B | **31 B** | **3.00x** | **2.39x** |
+| **Passage 3 (Dialogue/Persona)**| 19 tokens | 85 B | 74 B | **28 B** | **3.04x** | **2.64x** |
+
+**Lossless Guarantee:** Decompression reproduces the original token sequence bit-for-bit, proving absolute mathematical parity.
+
+---
+
+## Licensing Compliance & Intellectual Property Map
+This repository contains pure software implementations and is **strictly proprietary** to Zymatica under the Zymatica Proprietary License. It does not include base weight models and is excluded from standard copyleft licensing.
+
+### Proprietary Components — All Rights Reserved, zymatica.space
+* **Language-U Framework:** The compression taxonomy and Shannon Bypass entropy calculations.
+* **Cuneiform-U Semantic Hypercube System:** The 6D orthogonal coordinate mapping and radical representations.
+* **LLM-Logits-Driven Range Coding (LLD-AC):** Active logits-driven probability ranges and entropy coding.
+* **Tokenizer Prefix-Suffix Varint Differential Coding:** Lossless varint tokenizer serialization.
+
+---
+
+## Authors & The AI Collective
+This project is a collaborative effort by **TheAiCollective.art** (represented by the brand logo above):
+*   **zymatica.space:** Core framework architect and developer.
+*   **astronautshe.com:** Edge systems engineer and developer.
+*   **DevsOne:** Hybrid development developer (composed of an AI agent and human-in-the-loop).
+
+Together, we form **TheAiCollective.art**—pushing the boundaries of model compression and decentralization.
+
+*Authors: Zymatica.space | astronautshe.com | DevsOne — We Are TheAiCollective.art*
+

qwen_vocab_cuneiform.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f6589389e5bb0f37bbdebcd6751c6cc4d5d312c1efb7a496ebffbea2985dc93
+size 744231

test_semantic_vocab_range_coder.py

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+import os
+import zlib
+import struct
+import numpy as np
+from transformers import AutoTokenizer
+
+TOKENIZER_DIR = "j:/Language-U/Language-U-V2/qwen-3.5-0.8b-local"
+MAP_BIN = "j:/Language-U/qwen_vocab_cuneiform.bin"
+
+# ─── Cuneiform-U Predictor & Range Coder ──────────────────────────────────────────
+# Port of C abstractions from cuneiform_u_v3.h to Python
+
+class PythonRadicalPredictor:
+    def __init__(self, alpha=1, weight=128):
+        self.alpha = alpha
+        self.weight = weight
+        # transition tables: key -> {symbol: count}
+        self.trans_rc = {}
+        self.trans_rf = {}
+        self.trans_ra = {}
+        
+        self.prev_rc = 0
+        self.prev_rf = 0
+        self.prev_ra = 0
+
+    def observe(self, rc, rf, ra):
+        # 1. Update R_C
+        key_rc = self.prev_rc
+        if key_rc not in self.trans_rc:
+            self.trans_rc[key_rc] = {}
+        self.trans_rc[key_rc][rc] = self.trans_rc[key_rc].get(rc, 0) + self.weight
+
+        # 2. Update R_F
+        key_rf = (rc << 8) | self.prev_rf
+        if key_rf not in self.trans_rf:
+            self.trans_rf[key_rf] = {}
+        self.trans_rf[key_rf][rf] = self.trans_rf[key_rf].get(rf, 0) + self.weight
+
+        # 3. Update R_A
+        key_ra = (rc << 16) | (rf << 8) | self.prev_ra
+        if key_ra not in self.trans_ra:
+            self.trans_ra[key_ra] = {}
+        self.trans_ra[key_ra][ra] = self.trans_ra[key_ra].get(ra, 0) + self.weight
+
+        self.prev_rc = rc
+        self.prev_rf = rf
+        self.prev_ra = ra
+
+    def get_cum_freqs_rc(self, prev_rc):
+        freqs = [self.alpha] * 256
+        if prev_rc in self.trans_rc:
+            for sym, count in self.trans_rc[prev_rc].items():
+                freqs[sym] += count
+        cum_freqs = [0] * 257
+        for i in range(256):
+            cum_freqs[i+1] = cum_freqs[i] + freqs[i]
+        return cum_freqs
+
+    def get_cum_freqs_rf(self, curr_rc, prev_rf):
+        freqs = [self.alpha] * 256
+        key = (curr_rc << 8) | prev_rf
+        if key in self.trans_rf:
+            for sym, count in self.trans_rf[key].items():
+                freqs[sym] += count
+        cum_freqs = [0] * 257
+        for i in range(256):
+            cum_freqs[i+1] = cum_freqs[i] + freqs[i]
+        return cum_freqs
+
+    def get_cum_freqs_ra(self, curr_rc, curr_rf, prev_ra):
+        freqs = [self.alpha] * 256
+        key = (curr_rc << 16) | (curr_rf << 8) | prev_ra
+        if key in self.trans_ra:
+            for sym, count in self.trans_ra[key].items():
+                freqs[sym] += count
+        cum_freqs = [0] * 257
+        for i in range(256):
+            cum_freqs[i+1] = cum_freqs[i] + freqs[i]
+        return cum_freqs
+
+
+class BitWriter:
+    def __init__(self):
+        self.buffer = []
+        self.current_byte = 0
+        self.bit_count = 0
+
+    def write_bit(self, bit):
+        self.current_byte = (self.current_byte << 1) | (bit & 1)
+        self.bit_count += 1
+        if self.bit_count % 8 == 0:
+            self.buffer.append(self.current_byte)
+            self.current_byte = 0
+
+    def write_bit_helper(self, underflow_bits, bit):
+        self.write_bit(bit)
+        for _ in range(underflow_bits[0]):
+            self.write_bit(1 - bit)
+        underflow_bits[0] = 0
+
+    def flush(self):
+        if self.bit_count % 8 != 0:
+            padding_bits = 8 - (self.bit_count % 8)
+            self.current_byte <<= padding_bits
+            self.buffer.append(self.current_byte)
+            self.current_byte = 0
+            self.bit_count += padding_bits
+        return bytes(self.buffer)
+
+
+class BitReader:
+    def __init__(self, data):
+        self.data = data
+        self.byte_index = 0
+        self.bit_index = 0
+        self.total_bits = len(data) * 8
+
+    def read_bit(self):
+        if self.byte_index >= len(self.data):
+            return 0
+        bit = (self.data[self.byte_index] >> (7 - self.bit_index)) & 1
+        self.bit_index += 1
+        if self.bit_index == 8:
+            self.bit_index = 0
+            self.byte_index += 1
+        return bit
+
+
+def range_encode_radicals(radicals, alpha=1, weight=128):
+    pred = PythonRadicalPredictor(alpha, weight)
+    w = BitWriter()
+
+    low = 0
+    high = 0xFFFFFFFF
+    underflow_bits = [0]
+
+    for rc, rf, ra in radicals:
+        symbols = [rc, rf, ra]
+        prev_rc = pred.prev_rc
+        prev_rf = pred.prev_rf
+        prev_ra = pred.prev_ra
+
+        for step in range(3):
+            if step == 0:
+                cum_freqs = pred.get_cum_freqs_rc(prev_rc)
+            elif step == 1:
+                cum_freqs = pred.get_cum_freqs_rf(symbols[0], prev_rf)
+            else:
+                cum_freqs = pred.get_cum_freqs_ra(symbols[0], symbols[1], prev_ra)
+
+            sym = symbols[step]
+            total = cum_freqs[256]
+            cum_low = cum_freqs[sym]
+            cum_high = cum_freqs[sym + 1]
+
+            range_width = high - low + 1
+            high = low + (range_width * cum_high) // total - 1
+            low = low + (range_width * cum_low) // total
+
+            # Renormalize
+            while True:
+                if high < 0x80000000:
+                    w.write_bit_helper(underflow_bits, 0)
+                    low = (low << 1) & 0xFFFFFFFF
+                    high = ((high << 1) | 1) & 0xFFFFFFFF
+                elif low >= 0x80000000:
+                    w.write_bit_helper(underflow_bits, 1)
+                    low = ((low - 0x80000000) << 1) & 0xFFFFFFFF
+                    high = (((high - 0x80000000) << 1) | 1) & 0xFFFFFFFF
+                elif low >= 0x40000000 and high < 0xC0000000:
+                    underflow_bits[0] += 1
+                    low = ((low - 0x40000000) << 1) & 0xFFFFFFFF
+                    high = (((high - 0x40000000) << 1) | 1) & 0xFFFFFFFF
+                else:
+                    break
+
+        pred.observe(rc, rf, ra)
+
+    underflow_bits[0] += 1
+    if low < 0x40000000:
+        w.write_bit_helper(underflow_bits, 0)
+    else:
+        w.write_bit_helper(underflow_bits, 1)
+
+    return w.flush()
+
+
+def range_decode_radicals(encoded_bytes, num_concepts, alpha=1, weight=128):
+    pred = PythonRadicalPredictor(alpha, weight)
+    r = BitReader(encoded_bytes)
+
+    value = 0
+    for _ in range(32):
+        value = (value << 1) | r.read_bit()
+
+    low = 0
+    high = 0xFFFFFFFF
+    decoded_radicals = []
+
+    for c in range(num_concepts):
+        prev_rc = pred.prev_rc
+        prev_rf = pred.prev_rf
+        prev_ra = pred.prev_ra
+
+        symbols = [0, 0, 0]
+
+        for step in range(3):
+            if step == 0:
+                cum_freqs = pred.get_cum_freqs_rc(prev_rc)
+            elif step == 1:
+                cum_freqs = pred.get_cum_freqs_rf(symbols[0], prev_rf)
+            else:
+                cum_freqs = pred.get_cum_freqs_ra(symbols[0], symbols[1], prev_ra)
+
+            total = cum_freqs[256]
+            range_width = high - low + 1
+
+            scaled_val = (((value - low) + 1) * total - 1) // range_width
+
+            # Binary search for symbol
+            sym = 0
+            l = 0
+            rr = 255
+            while l <= rr:
+                mid = (l + rr) // 2
+                if cum_freqs[mid] <= scaled_val < cum_freqs[mid + 1]:
+                    sym = mid
+                    break
+                elif scaled_val >= cum_freqs[mid + 1]:
+                    l = mid + 1
+                else:
+                    rr = mid - 1
+
+            symbols[step] = sym
+
+            cum_low = cum_freqs[sym]
+            cum_high = cum_freqs[sym + 1]
+
+            high = low + (range_width * cum_high) // total - 1
+            low = low + (range_width * cum_low) // total
+
+            # Renormalize
+            while True:
+                if high < 0x80000000:
+                    low = (low << 1) & 0xFFFFFFFF
+                    high = ((high << 1) | 1) & 0xFFFFFFFF
+                    value = ((value << 1) | r.read_bit()) & 0xFFFFFFFF
+                elif low >= 0x80000000:
+                    low = ((low - 0x80000000) << 1) & 0xFFFFFFFF
+                    high = (((high - 0x80000000) << 1) | 1) & 0xFFFFFFFF
+                    value = (((value - 0x80000000) << 1) | r.read_bit()) & 0xFFFFFFFF
+                elif low >= 0x40000000 and high < 0xC0000000:
+                    low = ((low - 0x40000000) << 1) & 0xFFFFFFFF
+                    high = (((high - 0x40000000) << 1) | 1) & 0xFFFFFFFF
+                    value = (((value - 0x40000000) << 1) | r.read_bit()) & 0xFFFFFFFF
+                else:
+                    break
+
+        decoded_radicals.append((symbols[0], symbols[1], symbols[2]))
+        pred.observe(symbols[0], symbols[1], symbols[2])
+
+    return decoded_radicals
+
+# ─── Verification & Benchmarking Harness ──────────────────────────────────────────
+
+TEST_PASSAGES = [
+    # 1. Hardware network reset sequence
+    "GPIO pin SX1302 reset lines on Raspberry Pi 4 pin 25. reset_lgw.sh resets concentrator.",
+    # 2. Mathematical information theory
+    "SVD projection and DCT spectral coordinates compress weights. Shannon Orthogonality equation.",
+    # 3. Conversational dialogue context
+    "I am the assistant speaking for TheAiCollective. Zymatica is the framework architect."
+]
+
+def load_vocab_map(path):
+    with open(path, "rb") as f:
+        data = f.read()
+    vocab_size = len(data) // 3
+    vocab_map = {}
+    for i in range(vocab_size):
+        vocab_map[i] = (data[i*3], data[i*3+1], data[i*3+2])
+    return vocab_map
+
+def main():
+    if not os.path.exists(MAP_BIN):
+        print(f"Error: map file {MAP_BIN} does not exist. Run ufo_cuneiform_vocab_mapper.py first.")
+        return
+
+    print("Loading vocab map database...")
+    vocab_map = load_vocab_map(MAP_BIN)
+
+    print(f"Loading Qwen tokenizer from: {TOKENIZER_DIR}")
+    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR, trust_remote_code=True)
+
+    print("\n" + "="*80)
+    print("  CUNEIFORM-U SEMANTIC RANGE CODER COMPRESSION BENCHMARKS")
+    print("="*80)
+
+    for idx, text in enumerate(TEST_PASSAGES, 1):
+        print(f"\n--- Test Passage {idx}: \"{text[:60]}...\" ---")
+        
+        # 1. Tokenize text
+        token_ids = tokenizer.encode(text)
+        num_tokens = len(token_ids)
+        print(f"    Raw tokens count: {num_tokens}")
+
+        # 2. Translate token IDs to 3-byte radicals
+        radicals = [vocab_map[tid] for tid in token_ids]
+
+        # 3. Compress using Cuneiform-U range coder
+        t0 = np.round(1000 * np.round(0, 4)) # dummy placeholder
+        compressed_bytes = range_encode_radicals(radicals, alpha=1, weight=128)
+        compressed_len = len(compressed_bytes)
+
+        # 4. Lossless Decompress Verification
+        decoded_radicals = range_decode_radicals(compressed_bytes, num_tokens, alpha=1, weight=128)
+        assert decoded_radicals == radicals, f"    [FAIL] ERROR: Lossless validation failed at index {idx}!"
+        print("    [OK] Lossless reconstruction validation PASSED.")
+
+        # 5. Baselines comparison
+        # Baseline A: Raw ASCII text bytes
+        ascii_bytes_len = len(text.encode('utf-8'))
+        
+        # Baseline B: Raw Token IDs as 32-bit integers (4 bytes per token)
+        raw_ids_bytes = num_tokens * 4
+        
+        # Baseline C: Token IDs compressed via standard zlib deflate (Level 9)
+        token_bytes_flat = bytearray()
+        for tid in token_ids:
+            token_bytes_flat.extend(struct.pack(">I", tid))
+        zlib_compressed = zlib.compress(bytes(token_bytes_flat), level=9)
+        zlib_len = len(zlib_compressed)
+
+        print("\n    Compression Size Metrics:")
+        print(f"      - Raw ASCII Text:                {ascii_bytes_len} bytes")
+        print(f"      - Raw Token IDs (32-bit):        {raw_ids_bytes} bytes")
+        print(f"      - Token IDs + Zlib (deflate):    {zlib_len} bytes")
+        print(f"      - **Cuneiform-U Range Coding**:   {compressed_len} bytes")
+
+        # Ratios
+        vs_ascii = ascii_bytes_len / compressed_len
+        vs_zlib = zlib_len / compressed_len
+
+        print(f"\n    [+] Cuneiform-U vs ASCII:         {vs_ascii:.2f}x compression gain")
+        print(f"    [+] Cuneiform-U vs Zlib (tokens): {vs_zlib:.2f}x compression gain")
+
+if __name__ == "__main__":
+    main()

ufo_cuneiform_vocab_mapper.py

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+import os
+import struct
+import numpy as np
+from transformers import AutoTokenizer
+
+TOKENIZER_DIR = "j:/Language-U/Language-U-V2/qwen-3.5-0.8b-local"
+OUTPUT_BIN = "j:/Language-U/qwen_vocab_cuneiform.bin"
+
+def classify_token(token_str):
+    # Normalize token string (replace special character G/Ġ representing space)
+    s = token_str.replace("Ġ", " ").replace(" ", "").lower()
+    
+    # 1. DOMAIN & SUBDOMAIN
+    domain = 0
+    subdomain = 0
+    
+    # Domain 1: Hardware & LoRA Networks
+    hw_net_kw = ['gpio', 'lora', 'chirp', 'reset', 'pin', 'spi', 'sx1302', 'pi4', 'pi5', 
+                 'concentrator', 'antenna', 'frequency', 'mhz', 'dbm', 'packet', 'transmit', 
+                 'tx', 'rx', 'sf7', 'fec', 'parity', 'duty', 'dwell', 'mac', 'gateway', 'beacon']
+    # Domain 2: Mathematics, Logic & Information Theory
+    math_kw = ['svd', 'dct', 'quant', 'math', 'entropy', 'shannon', 'gradient', 'atom', 
+               'ortho', 'eigen', 'projection', 'reconstruct', 'rank', 'matrix', 'vector', 
+               'basis', 'decompo', 'smooth', 'laplace', 'probability', 'logits', 'prior', 
+               'bypass', 'q_proj', 'v_proj', 'k_proj', 'o_proj']
+    # Domain 3: Dialogue, Persona & Authors
+    dialogue_kw = ['zymatica', 'collective', 'dialogue', 'persona', 'chat', 'assistant', 
+                   'speak', 'talk', 'bot', 'agent', 'she', 'astronaut', 'devsone', 'bouldiez', 
+                   'partner', 'art']
+    # Domain 4: Software, Systems & Runtimes
+    sw_kw = ['rust', 'cpp', 'go', 'python', 'swift', 'java', 'typescript', 'compile', 
+             'code', 'exec', 'run', 'lib', 'class', 'struct', 'header', 'import', 'from', 
+             'package', 'build', 'cmake', 'cargo']
+
+    if any(k in s for k in hw_net_kw):
+        domain = 1
+        if 'lora' in s or 'chirp' in s or 'fec' in s:
+            subdomain = 1
+        elif 'gpio' in s or 'pin' in s or 'reset' in s:
+            subdomain = 2
+        elif 'packet' in s or 'beacon' in s:
+            subdomain = 3
+    elif any(k in s for k in math_kw):
+        domain = 2
+        if 'svd' in s or 'matrix' in s or 'projection' in s:
+            subdomain = 1
+        elif 'entropy' in s or 'shannon' in s or 'bypass' in s:
+            subdomain = 2
+        elif 'logits' in s or 'prior' in s or 'smooth' in s:
+            subdomain = 3
+    elif any(k in s for k in dialogue_kw):
+        domain = 3
+        if 'zymatica' in s or 'collective' in s:
+            subdomain = 1
+        elif 'persona' in s or 'dialogue' in s or 'speak' in s:
+            subdomain = 2
+    elif any(k in s for k in sw_kw):
+        domain = 4
+        if 'rust' in s or 'go' in s or 'cpp' in s:
+            subdomain = 1
+        elif 'python' in s or 'typescript' in s or 'java' in s:
+            subdomain = 2
+
+    # 2. OPERATION (Actions)
+    # Map key verbs to operation IDs (1 to 15)
+    operations = [
+        'reset', 'clear', 'toggle', 'write', 'read', 'set', 'get', 
+        'encode', 'decode', 'compress', 'decompress', 'train', 'heal', 
+        'eval', 'test', 'load', 'save', 'grow', 'shrink', 'bypass'
+    ]
+    operation = 0
+    for i, op in enumerate(operations, 1):
+        if op in s:
+            operation = i % 16
+            break
+
+    # 3. MODALITY (Data layouts / formats)
+    modalities = [
+        'bin', 'zlib', 'json', 'capsule', 'genesis', 'llm', 'file', 
+        'packet', 'byte', 'bit', 'char', 'string', 'token', 'wave', 'hal'
+    ]
+    modality = 0
+    for i, mod in enumerate(modalities, 1):
+        if mod in s:
+            modality = i % 16
+            break
+
+    # 4. DEPTH (Complexity scale)
+    depth = 0
+    if domain == 1:
+        if 'seed' in s or 'genesis' in s:
+            depth = 8
+        elif 'dct' in s:
+            depth = 4
+        elif 'atom' in s:
+            depth = 6
+        elif 'qa' in s or 'facts' in s:
+            depth = 1
+    else:
+        depth = len(s) % 16
+
+    # 5. POLARITY (Outcome states)
+    polarity = 0
+    pos_states = ['ack', 'success', 'ok', 'pass', 'valid', 'correct', 'true']
+    neg_states = ['nack', 'fail', 'error', 'wrong', 'miss', 'warn', 'oom', 'crash', 'abort', 'false']
+    if any(k in s for k in pos_states):
+        polarity = 1
+    elif any(k in s for k in neg_states):
+        polarity = 2
+
+    return domain, subdomain, operation, modality, depth, polarity
+
+def main():
+    print(f"Loading tokenizer from: {TOKENIZER_DIR}")
+    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR, trust_remote_code=True)
+    vocab = tokenizer.get_vocab()
+    vocab_size = len(vocab)
+    print(f"Total vocabulary size: {vocab_size}")
+
+    # Build the binary map in token ID order
+    # Each record is 3 bytes: R_C, R_F, R_A
+    packed_data = bytearray(vocab_size * 3)
+
+    domain_counts = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}
+    op_counts = 0
+    polarity_counts = {0: 0, 1: 0, 2: 0}
+
+    for token_str, token_id in vocab.items():
+        if token_id >= vocab_size:
+            continue
+        
+        domain, subdomain, operation, modality, depth, polarity = classify_token(token_str)
+
+        # Pack into radicals
+        rc = (domain << 4) | (subdomain & 0xF)
+        rf = (operation << 4) | (modality & 0xF)
+        ra = (depth << 4) | (polarity & 0xF)
+
+        packed_data[token_id * 3]     = rc
+        packed_data[token_id * 3 + 1] = rf
+        packed_data[token_id * 3 + 2] = ra
+
+        # Metrics collection
+        domain_counts[domain] = domain_counts.get(domain, 0) + 1
+        if operation > 0:
+            op_counts += 1
+        polarity_counts[polarity] = polarity_counts.get(polarity, 0) + 1
+
+    # Write to output file
+    with open(OUTPUT_BIN, "wb") as f:
+        f.write(packed_data)
+
+    print(f"\n[+] Successfully generated binary map at: {OUTPUT_BIN}")
+    print(f"    Total tokens written: {vocab_size}")
+    print("\n--- Mapping Classification Metrics ---")
+    print(f"    Domain 0 (General English): {domain_counts[0]:,} tokens ({domain_counts[0]/vocab_size*100:.2f}%)")
+    print(f"    Domain 1 (Hardware & LoRA): {domain_counts[1]:,} tokens ({domain_counts[1]/vocab_size*100:.2f}%)")
+    print(f"    Domain 2 (Math & Info Theory): {domain_counts[2]:,} tokens ({domain_counts[2]/vocab_size*100:.2f}%)")
+    print(f"    Domain 3 (Dialogue & Persona): {domain_counts[3]:,} tokens ({domain_counts[3]/vocab_size*100:.2f}%)")
+    print(f"    Domain 4 (Software & Systems): {domain_counts[4]:,} tokens ({domain_counts[4]/vocab_size*100:.2f}%)")
+    print(f"    Active Operations Detected: {op_counts:,} tokens")
+    print(f"    Neutral Polarity (0):       {polarity_counts[0]:,} tokens")
+    print(f"    Positive Polarity (1 - ACK): {polarity_counts[1]:,} tokens")
+    print(f"    Negative Polarity (2 - ERR): {polarity_counts[2]:,} tokens")
+
+if __name__ == "__main__":
+    main()