From f3bb3b814bab246b86a50415fff16800afef20b1 Mon Sep 17 00:00:00 2001
From: "cto-new[bot]" <140088366+cto-new[bot]@users.noreply.github.com>
Date: Fri, 12 Dec 2025 14:05:54 +0000
Subject: [PATCH] refactor(frackture): convert monolithic script into a package
 with modular design and unified API

---
 __pycache__/frackture (2).cpython-312.pyc     | Bin 0 -> 6737 bytes
 compressed.json                               |  34 ++
 decrypted_vector.txt                          |   1 +
 encrypted.json                                |  34 ++
 fingerprint.txt                               |   1 +
 frackture/__init__.py                         | 246 +++++++++++
 frackture/__main__.py                         |  12 +
 .../__pycache__/__init__.cpython-312.pyc      | Bin 0 -> 6796 bytes
 .../__pycache__/__main__.cpython-312.pyc      | Bin 0 -> 458 bytes
 frackture/__pycache__/cli.cpython-312.pyc     | Bin 0 -> 15086 bytes
 frackture/__pycache__/engine.cpython-312.pyc  | Bin 0 -> 14908 bytes
 frackture/__pycache__/entropy.cpython-312.pyc | Bin 0 -> 8553 bytes
 frackture/__pycache__/models.cpython-312.pyc  | Bin 0 -> 4962 bytes
 .../__pycache__/preprocess.cpython-312.pyc    | Bin 0 -> 4682 bytes
 .../__pycache__/symbolic.cpython-312.pyc      | Bin 0 -> 8805 bytes
 frackture/cli.py                              | 292 +++++++++++++
 frackture/engine.py                           | 412 ++++++++++++++++++
 frackture/entropy.py                          | 245 +++++++++++
 frackture/models.py                           | 113 +++++
 frackture/preprocess.py                       | 108 +++++
 frackture/symbolic.py                         | 207 +++++++++
 key.txt                                       |   1 +
 secret_input.txt                              |   1 +
 test_input.txt                                |   1 +
 24 files changed, 1708 insertions(+)
 create mode 100644 __pycache__/frackture (2).cpython-312.pyc
 create mode 100644 compressed.json
 create mode 100644 decrypted_vector.txt
 create mode 100644 encrypted.json
 create mode 100644 fingerprint.txt
 create mode 100644 frackture/__init__.py
 create mode 100644 frackture/__main__.py
 create mode 100644 frackture/__pycache__/__init__.cpython-312.pyc
 create mode 100644 frackture/__pycache__/__main__.cpython-312.pyc
 create mode 100644 frackture/__pycache__/cli.cpython-312.pyc
 create mode 100644 frackture/__pycache__/engine.cpython-312.pyc
 create mode 100644 frackture/__pycache__/entropy.cpython-312.pyc
 create mode 100644 frackture/__pycache__/models.cpython-312.pyc
 create mode 100644 frackture/__pycache__/preprocess.cpython-312.pyc
 create mode 100644 frackture/__pycache__/symbolic.cpython-312.pyc
 create mode 100644 frackture/cli.py
 create mode 100644 frackture/engine.py
 create mode 100644 frackture/entropy.py
 create mode 100644 frackture/models.py
 create mode 100644 frackture/preprocess.py
 create mode 100644 frackture/symbolic.py
 create mode 100644 key.txt
 create mode 100644 secret_input.txt
 create mode 100644 test_input.txt

diff --git a/__pycache__/frackture (2).cpython-312.pyc b/__pycache__/frackture (2).cpython-312.pyc
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diff --git a/compressed.json b/compressed.json
new file mode 100644
index 0000000..59234c9
--- /dev/null
+++ b/compressed.json
@@ -0,0 +1,34 @@
+{
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+  "entropy": [
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+    4.298604965209961,
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+  ],
+  "metadata": {
+    "version": "3.3",
+    "passes": 4,
+    "target_length": 768,
+    "optimization_trials": 0,
+    "created_at": "1765548231"
+  },
+  "encryption": {
+    "mode": "none",
+    "key_id": null,
+    "salt": null
+  },
+  "fingerprint": "6d4e484b928c50ac"
+}
\ No newline at end of file
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\ No newline at end of file
diff --git a/encrypted.json b/encrypted.json
new file mode 100644
index 0000000..c11a4c0
--- /dev/null
+++ b/encrypted.json
@@ -0,0 +1,34 @@
+{
+  "symbolic": "408040804080408000c0c04080408000c0c04080408000c0c04080408000c0c0",
+  "entropy": [
+    0.3620098039215686,
+    0.06491493789707584,
+    0.36511437908496736,
+    0.06246521469937504,
+    0.3689542483660131,
+    0.06683086437031346,
+    0.3669117647058824,
+    0.0626606691082609,
+    0.3621732026143791,
+    0.06680767668749768,
+    0.36478758169934644,
+    0.0627852832704965,
+    0.3525326797385621,
+    0.06949756474671824,
+    0.3680555555555556,
+    0.06296045250732021
+  ],
+  "metadata": {
+    "version": "3.3",
+    "passes": 4,
+    "target_length": 768,
+    "optimization_trials": 0,
+    "created_at": "1765548281"
+  },
+  "encryption": {
+    "mode": "xor",
+    "key_id": null,
+    "salt": "6d792d7365637265"
+  },
+  "fingerprint": "93e039a093bfa345"
+}
\ No newline at end of file
diff --git a/fingerprint.txt b/fingerprint.txt
new file mode 100644
index 0000000..c718e06
--- /dev/null
+++ b/fingerprint.txt
@@ -0,0 +1 @@
+00408040004080c000400080c000c0004080400040004080c000400080c000c0
\ No newline at end of file
diff --git a/frackture/__init__.py b/frackture/__init__.py
new file mode 100644
index 0000000..7349129
--- /dev/null
+++ b/frackture/__init__.py
@@ -0,0 +1,246 @@
+"""
+Frackture - Symbolic compression and fingerprinting engine.
+
+This package provides a unified API for data compression, encryption,
+and fingerprinting using a dual-channel approach combining symbolic
+and entropy channels.
+
+Basic Usage:
+    from frackture import compress, decompress
+    
+    data = b"Hello world"
+    compressed = compress(data)
+    original = decompress(compressed)
+
+Advanced Usage:
+    from frackture import FracktureEngine
+    
+    engine = FracktureEngine()
+    result = engine.compress(data, optimize=True)
+    vector = engine.decompress(result.payload)
+    
+    # For encryption
+    encrypted = engine.encrypt(data, key_material=b"my-secret-key")
+    decrypted = engine.decrypt(encrypted.payload, key_material=b"my-secret-key")
+    
+    # For fingerprinting only
+    fingerprint = engine.fingerprint(data)
+"""
+
+# Version information
+__version__ = "3.3.0"
+__author__ = "Frackture Development Team"
+
+# Import public API
+from .engine import FracktureEngine
+from .models import (
+    FrackturePayload,
+    CompressionMetadata,
+    EncryptionMetadata,
+    CompressionResult,
+    FracktureVersion,
+    EncryptionMode
+)
+
+# Import core functions for convenience
+from .preprocess import frackture_preprocess_universal_v2_6
+from .symbolic import (
+    symbolic_channel_encode,
+    symbolic_channel_decode,
+    symbolic_fingerprint_with_key
+)
+from .entropy import (
+    entropy_channel_encode,
+    entropy_channel_decode,
+    entropy_signature_with_key
+)
+
+# Global engine instance for convenience functions
+_global_engine = FracktureEngine()
+
+
+def compress(
+    data,
+    passes: int = None,
+    optimize: bool = False,
+    num_trials: int = 5
+):
+    """
+    Compress data using Frackture's default engine.
+    
+    Args:
+        data: Input data of any supported type
+        passes: Number of symbolic passes (default: 4)
+        optimize: Whether to use optimization (default: False)
+        num_trials: Number of optimization trials (default: 5)
+        
+    Returns:
+        dict: Serialized FrackturePayload containing compressed data
+        
+    Example:
+        >>> data = b"Hello world"
+        >>> result = compress(data)
+        >>> print(result['symbolic'])  # 64-char hex fingerprint
+        >>> print(len(result['entropy']))  # 16 entropy components
+    """
+    result = _global_engine.compress(data, passes, optimize, num_trials)
+    return result.payload.to_dict()
+
+
+def decompress(payload):
+    """
+    Decompress Frackture payload to vector representation.
+    
+    Args:
+        payload: FrackturePayload (dict or object) to decompress
+        
+    Returns:
+        np.ndarray: 768-length reconstructed vector
+        
+    Example:
+        >>> payload = {'symbolic': '...', 'entropy': [...]}
+        >>> vector = decompress(payload)
+        >>> print(len(vector))  # 768
+    """
+    return _global_engine.decompress(payload)
+
+
+def encrypt(data, key_material: bytes, passes: int = None):
+    """
+    Encrypt data using key-based Frackture compression.
+    
+    Args:
+        data: Input data to encrypt
+        key_material: Encryption key material (bytes)
+        passes: Number of symbolic passes (default: 4)
+        
+    Returns:
+        dict: Serialized encrypted FrackturePayload
+        
+    Example:
+        >>> data = b"Secret message"
+        >>> key = b"my-encryption-key"
+        >>> encrypted = encrypt(data, key)
+        >>> print(encrypted['encryption']['mode'])  # 'xor'
+    """
+    result = _global_engine.encrypt(data, key_material, passes)
+    return result.payload.to_dict()
+
+
+def decrypt(payload, key_material: bytes):
+    """
+    Decrypt Frackture payload using key material.
+    
+    Args:
+        payload: Encrypted FrackturePayload (dict or object)
+        key_material: Encryption key material (bytes)
+        
+    Returns:
+        np.ndarray: 768-length decrypted vector
+        
+    Example:
+        >>> encrypted_payload = {...}  # From encrypt()
+        >>> key = b"my-encryption-key"
+        >>> vector = decrypt(encrypted_payload, key)
+    """
+    return _global_engine.decrypt(payload, key_material)
+
+
+def fingerprint(data, passes: int = None):
+    """
+    Generate fingerprint for data without full compression.
+    
+    Args:
+        data: Input data to fingerprint
+        passes: Number of symbolic passes (default: 4)
+        
+    Returns:
+        str: 64-character hexadecimal fingerprint
+        
+    Example:
+        >>> data = b"Test data"
+        >>> fp = fingerprint(data)
+        >>> print(len(fp))  # 64
+        >>> print(fp[:16])  # First 16 characters
+    """
+    return _global_engine.fingerprint(data, passes)
+
+
+def verify_payload(payload) -> bool:
+    """
+    Verify payload integrity using stored fingerprint.
+    
+    Args:
+        payload: FrackturePayload (dict or object) to verify
+        
+    Returns:
+        bool: True if payload is valid and fingerprint matches
+        
+    Example:
+        >>> payload = {...}  # Some payload
+        >>> is_valid = verify_payload(payload)
+        >>> if is_valid:
+        ...     print("Payload is valid")
+    """
+    return _global_engine.verify_payload(payload)
+
+
+def get_version() -> str:
+    """
+    Get current Frackture version.
+    
+    Returns:
+        str: Version string
+    """
+    return __version__
+
+
+def create_engine(version=None):
+    """
+    Create a new FracktureEngine instance.
+    
+    Args:
+        version: Frackture version to use (default: current)
+        
+    Returns:
+        FracktureEngine: New engine instance
+        
+    Example:
+        >>> engine = create_engine()
+        >>> result = engine.compress("test data")
+    """
+    if version is None:
+        return FracktureEngine()
+    return FracktureEngine(version)
+
+
+# Define what gets exported with "from frackture import *"
+__all__ = [
+    # Core classes
+    "FracktureEngine",
+    "FrackturePayload", 
+    "CompressionMetadata",
+    "EncryptionMetadata",
+    "CompressionResult",
+    "FracktureVersion",
+    "EncryptionMode",
+    
+    # Convenience functions
+    "compress",
+    "decompress", 
+    "encrypt",
+    "decrypt",
+    "fingerprint",
+    "verify_payload",
+    "get_version",
+    "create_engine",
+    
+    # Core processing functions
+    "frackture_preprocess_universal_v2_6",
+    "symbolic_channel_encode",
+    "symbolic_channel_decode",
+    "symbolic_fingerprint_with_key", 
+    "entropy_channel_encode",
+    "entropy_channel_decode",
+    "entropy_signature_with_key",
+]
\ No newline at end of file
diff --git a/frackture/__main__.py b/frackture/__main__.py
new file mode 100644
index 0000000..d1f9018
--- /dev/null
+++ b/frackture/__main__.py
@@ -0,0 +1,12 @@
+"""
+CLI entry point for Frackture package.
+
+This module allows the frackture package to be executed as a module:
+    python -m frackture compress input.txt
+    python -m frackture fingerprint data.txt
+"""
+
+from .cli import main
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
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diff --git a/frackture/cli.py b/frackture/cli.py
new file mode 100644
index 0000000..32526b6
--- /dev/null
+++ b/frackture/cli.py
@@ -0,0 +1,292 @@
+#!/usr/bin/env python3
+"""
+Command Line Interface for Frackture.
+
+Provides simple CLI commands for compression, decompression, encryption,
+decryption, and fingerprinting operations.
+"""
+
+import argparse
+import json
+import sys
+from typing import Optional
+
+import numpy as np
+
+from .engine import FracktureEngine
+from .models import FracktureVersion, EncryptionMode
+
+
+def load_data(file_path: str) -> str:
+    """Load data from file."""
+    try:
+        with open(file_path, 'r', encoding='utf-8') as f:
+            return f.read()
+    except Exception as e:
+        print(f"Error loading file {file_path}: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+def save_data(data: str, file_path: str) -> None:
+    """Save data to file."""
+    try:
+        with open(file_path, 'w', encoding='utf-8') as f:
+            f.write(data)
+    except Exception as e:
+        print(f"Error saving file {file_path}: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+def compress_command(args):
+    """Handle compress command."""
+    engine = FracktureEngine()
+    
+    # Load input data
+    data = load_data(args.input)
+    
+    try:
+        # Perform compression
+        result = engine.compress(
+            data, 
+            passes=args.passes,
+            optimize=args.optimize,
+            num_trials=args.trials
+        )
+        
+        # Output results
+        if args.output:
+            save_data(json.dumps(result.payload.to_dict(), indent=2), args.output)
+            print(f"Compression successful. Output saved to {args.output}")
+            print(f"Original size: {result.original_size} bytes")
+            print(f"Compressed size: {result.compressed_size} bytes")
+            print(f"Compression ratio: {result.compression_ratio:.3f}")
+            print(f"Reconstruction MSE: {result.mse:.6f}")
+        else:
+            print(json.dumps(result.payload.to_dict(), indent=2))
+            
+    except Exception as e:
+        print(f"Compression failed: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+def decompress_command(args):
+    """Handle decompress command."""
+    engine = FracktureEngine()
+    
+    # Load payload
+    try:
+        with open(args.input, 'r', encoding='utf-8') as f:
+            payload_dict = json.load(f)
+    except Exception as e:
+        print(f"Error loading payload {args.input}: {e}", file=sys.stderr)
+        sys.exit(1)
+    
+    try:
+        # Perform decompression
+        vector = engine.decompress(payload_dict)
+        
+        # Output results
+        if args.output:
+            save_data(str(vector.tolist()), args.output)
+            print(f"Decompression successful. Vector saved to {args.output}")
+        else:
+            print("Decompressed vector (first 10 elements):")
+            print(vector[:10].tolist())
+            print(f"... ({len(vector)} total elements)")
+            
+    except Exception as e:
+        print(f"Decompression failed: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+def encrypt_command(args):
+    """Handle encrypt command."""
+    engine = FracktureEngine()
+    
+    # Load input data
+    data = load_data(args.input)
+    
+    # Load key material
+    try:
+        with open(args.key, 'rb') as f:
+            key_material = f.read()
+    except Exception as e:
+        print(f"Error loading key file {args.key}: {e}", file=sys.stderr)
+        sys.exit(1)
+    
+    try:
+        # Perform encryption
+        result = engine.encrypt(data, key_material, passes=args.passes)
+        
+        # Output results
+        if args.output:
+            save_data(json.dumps(result.payload.to_dict(), indent=2), args.output)
+            print(f"Encryption successful. Output saved to {args.output}")
+            print(f"Original size: {result.original_size} bytes")
+            print(f"Encrypted size: {result.compressed_size} bytes")
+            print(f"Compression ratio: {result.compression_ratio:.3f}")
+        else:
+            print(json.dumps(result.payload.to_dict(), indent=2))
+            
+    except Exception as e:
+        print(f"Encryption failed: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+def decrypt_command(args):
+    """Handle decrypt command."""
+    engine = FracktureEngine()
+    
+    # Load payload
+    try:
+        with open(args.input, 'r', encoding='utf-8') as f:
+            payload_dict = json.load(f)
+    except Exception as e:
+        print(f"Error loading payload {args.input}: {e}", file=sys.stderr)
+        sys.exit(1)
+    
+    # Load key material
+    try:
+        with open(args.key, 'rb') as f:
+            key_material = f.read()
+    except Exception as e:
+        print(f"Error loading key file {args.key}: {e}", file=sys.stderr)
+        sys.exit(1)
+    
+    try:
+        # Perform decryption
+        vector = engine.decrypt(payload_dict, key_material)
+        
+        # Output results
+        if args.output:
+            save_data(str(vector.tolist()), args.output)
+            print(f"Decryption successful. Vector saved to {args.output}")
+        else:
+            print("Decrypted vector (first 10 elements):")
+            print(vector[:10].tolist())
+            print(f"... ({len(vector)} total elements)")
+            
+    except Exception as e:
+        print(f"Decryption failed: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+def fingerprint_command(args):
+    """Handle fingerprint command."""
+    engine = FracktureEngine()
+    
+    # Load input data
+    data = load_data(args.input)
+    
+    try:
+        # Generate fingerprint
+        fingerprint = engine.fingerprint(data, passes=args.passes)
+        
+        # Output results
+        if args.output:
+            save_data(fingerprint, args.output)
+            print(f"Fingerprint generated successfully. Output saved to {args.output}")
+        else:
+            print(fingerprint)
+            
+    except Exception as e:
+        print(f"Fingerprint generation failed: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+def verify_command(args):
+    """Handle verify command."""
+    engine = FracktureEngine()
+    
+    # Load payload
+    try:
+        with open(args.input, 'r', encoding='utf-8') as f:
+            payload_dict = json.load(f)
+    except Exception as e:
+        print(f"Error loading payload {args.input}: {e}", file=sys.stderr)
+        sys.exit(1)
+    
+    try:
+        # Verify payload
+        is_valid = engine.verify_payload(payload_dict)
+        
+        if is_valid:
+            print("✓ Payload verification successful - payload is valid")
+        else:
+            print("✗ Payload verification failed - payload is invalid or corrupted")
+            sys.exit(1)
+            
+    except Exception as e:
+        print(f"Verification failed: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+def create_parser() -> argparse.ArgumentParser:
+    """Create and configure the argument parser."""
+    parser = argparse.ArgumentParser(
+        description="Frackture - Symbolic compression and fingerprinting engine",
+        prog="frackture"
+    )
+    
+    subparsers = parser.add_subparsers(dest="command", help="Available commands")
+    
+    # Compress command
+    compress_parser = subparsers.add_parser("compress", help="Compress data")
+    compress_parser.add_argument("input", help="Input file path")
+    compress_parser.add_argument("-o", "--output", help="Output file path")
+    compress_parser.add_argument("-p", "--passes", type=int, help="Number of symbolic passes")
+    compress_parser.add_argument("--optimize", action="store_true", help="Use optimization")
+    compress_parser.add_argument("--trials", type=int, default=5, help="Number of optimization trials")
+    compress_parser.set_defaults(func=compress_command)
+    
+    # Decompress command
+    decompress_parser = subparsers.add_parser("decompress", help="Decompress data")
+    decompress_parser.add_argument("input", help="Input payload file path")
+    decompress_parser.add_argument("-o", "--output", help="Output file path")
+    decompress_parser.set_defaults(func=decompress_command)
+    
+    # Encrypt command
+    encrypt_parser = subparsers.add_parser("encrypt", help="Encrypt data with key")
+    encrypt_parser.add_argument("input", help="Input file path")
+    encrypt_parser.add_argument("key", help="Key file path")
+    encrypt_parser.add_argument("-o", "--output", help="Output file path")
+    encrypt_parser.add_argument("-p", "--passes", type=int, help="Number of symbolic passes")
+    encrypt_parser.set_defaults(func=encrypt_command)
+    
+    # Decrypt command
+    decrypt_parser = subparsers.add_parser("decrypt", help="Decrypt data with key")
+    decrypt_parser.add_argument("input", help="Input payload file path")
+    decrypt_parser.add_argument("key", help="Key file path")
+    decrypt_parser.add_argument("-o", "--output", help="Output file path")
+    decrypt_parser.set_defaults(func=decrypt_command)
+    
+    # Fingerprint command
+    fingerprint_parser = subparsers.add_parser("fingerprint", help="Generate fingerprint")
+    fingerprint_parser.add_argument("input", help="Input file path")
+    fingerprint_parser.add_argument("-o", "--output", help="Output file path")
+    fingerprint_parser.add_argument("-p", "--passes", type=int, help="Number of symbolic passes")
+    fingerprint_parser.set_defaults(func=fingerprint_command)
+    
+    # Verify command
+    verify_parser = subparsers.add_parser("verify", help="Verify payload integrity")
+    verify_parser.add_argument("input", help="Input payload file path")
+    verify_parser.set_defaults(func=verify_command)
+    
+    return parser
+
+
+def main():
+    """Main CLI entry point."""
+    parser = create_parser()
+    args = parser.parse_args()
+    
+    if not args.command:
+        parser.print_help()
+        sys.exit(1)
+    
+    # Execute the appropriate command
+    args.func(args)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/frackture/engine.py b/frackture/engine.py
new file mode 100644
index 0000000..f8661aa
--- /dev/null
+++ b/frackture/engine.py
@@ -0,0 +1,412 @@
+"""
+Core Frackture engine that unifies all channels and provides the main API.
+
+This module contains the FracktureEngine class which orchestrates the
+preprocessing, symbolic, and entropy channels to provide a unified
+compression and fingerprinting interface.
+"""
+
+import time
+import hashlib
+from typing import Union, Optional, Dict, Any
+
+import numpy as np
+
+from .models import (
+    FrackturePayload, 
+    CompressionMetadata, 
+    EncryptionMetadata,
+    CompressionResult,
+    FracktureVersion,
+    EncryptionMode
+)
+from .preprocess import frackture_preprocess_universal_v2_6, validate_preprocessed_vector
+from .symbolic import (
+    symbolic_channel_encode, 
+    symbolic_channel_decode,
+    symbolic_fingerprint_with_key,
+    validate_symbolic_hash
+)
+from .entropy import (
+    entropy_channel_encode,
+    entropy_channel_decode,
+    entropy_signature_with_key,
+    validate_entropy_signature
+)
+
+
+class FracktureEngine:
+    """
+    Unified Frackture compression and fingerprinting engine.
+    
+    This engine orchestrates the preprocessing, symbolic, and entropy channels
+    to provide a clean API for compression, decompression, encryption, 
+    decryption, and fingerprinting operations.
+    """
+    
+    def __init__(self, version: FracktureVersion = FracktureVersion.V3_3):
+        """
+        Initialize the Frackture engine.
+        
+        Args:
+            version: Frackture version to use (default: V3_3)
+        """
+        self.version = version
+        self.default_passes = 4
+        self.target_length = 768
+    
+    def _create_metadata(self, passes: Optional[int] = None) -> CompressionMetadata:
+        """Create compression metadata with timestamp."""
+        return CompressionMetadata(
+            version=self.version,
+            passes=passes or self.default_passes,
+            target_length=self.target_length,
+            created_at=str(int(time.time()))
+        )
+    
+    def _create_encryption_metadata(
+        self, 
+        key_material: Optional[bytes] = None,
+        mode: EncryptionMode = EncryptionMode.NONE
+    ) -> EncryptionMetadata:
+        """Create encryption metadata."""
+        return EncryptionMetadata(
+            mode=mode,
+            key_material=key_material,
+            salt=key_material[:8] if key_material else None  # Use first 8 bytes as salt
+        )
+    
+    def _generate_fingerprint(self, payload_dict: Dict) -> str:
+        """Generate fingerprint for payload validation."""
+        # Create deterministic string representation
+        content = f"{payload_dict['symbolic']}{payload_dict['entropy']}{payload_dict['metadata']['version']}"
+        return hashlib.sha256(content.encode()).hexdigest()[:16]
+    
+    def compress(
+        self, 
+        data: Union[str, bytes, dict, list, np.ndarray, Any],
+        passes: Optional[int] = None,
+        optimize: bool = False,
+        num_trials: int = 5
+    ) -> CompressionResult:
+        """
+        Compress input data using Frackture's dual-channel approach.
+        
+        Args:
+            data: Input data of any supported type
+            passes: Number of symbolic passes (default: engine default)
+            optimize: Whether to use self-optimization (default: False)
+            num_trials: Number of optimization trials (default: 5)
+            
+        Returns:
+            CompressionResult: Complete compression result with metadata
+            
+        Raises:
+            ValueError: If preprocessing fails
+            Exception: If compression fails
+        """
+        try:
+            # Preprocess input
+            processed = frackture_preprocess_universal_v2_6(data)
+            
+            if not validate_preprocessed_vector(processed):
+                raise ValueError("Preprocessing failed - invalid vector produced")
+            
+            original_size = len(str(data).encode('utf-8'))
+            
+            # Use optimization if requested
+            if optimize:
+                payload, mse = self._optimize_compression(processed, num_trials, passes)
+            else:
+                # Standard compression
+                actual_passes = passes or self.default_passes
+                
+                # Encode through both channels
+                symbolic = symbolic_channel_encode(processed, actual_passes)
+                entropy = entropy_channel_encode(processed)
+                
+                # Create payload
+                metadata = self._create_metadata(actual_passes)
+                encryption = self._create_encryption_metadata()
+                
+                payload = FrackturePayload(
+                    symbolic=symbolic,
+                    entropy=entropy,
+                    metadata=metadata,
+                    encryption=encryption
+                )
+                
+                # Generate fingerprint
+                payload.fingerprint = self._generate_fingerprint(payload.to_dict())
+                
+                # Calculate MSE
+                reconstructed = self.decompress(payload)
+                mse = float(np.mean((processed - reconstructed) ** 2))
+            
+            # Calculate compression statistics
+            payload_dict = payload.to_dict()
+            import json
+            compressed_size = len(json.dumps(payload_dict).encode('utf-8'))
+            compression_ratio = compressed_size / original_size if original_size > 0 else 0.0
+            
+            return CompressionResult(
+                payload=payload,
+                original_size=original_size,
+                compressed_size=compressed_size,
+                compression_ratio=compression_ratio,
+                mse=mse
+            )
+            
+        except Exception as e:
+            raise Exception(f"Compression failed: {e}")
+    
+    def _optimize_compression(
+        self, 
+        processed: np.ndarray, 
+        num_trials: int,
+        base_passes: Optional[int]
+    ) -> tuple:
+        """Perform optimization to find best compression parameters."""
+        best_payload = None
+        best_mse = float("inf")
+        
+        base_passes_val = base_passes or self.default_passes
+        
+        for trial in range(num_trials):
+            # Try different pass counts
+            passes_to_try = base_passes_val + trial
+            
+            symbolic = symbolic_channel_encode(processed, passes_to_try)
+            entropy = entropy_channel_encode(processed)
+            
+            metadata = self._create_metadata(passes_to_try)
+            metadata.optimization_trials = trial + 1
+            
+            encryption = self._create_encryption_metadata()
+            
+            payload = FrackturePayload(
+                symbolic=symbolic,
+                entropy=entropy,
+                metadata=metadata,
+                encryption=encryption
+            )
+            
+            payload.fingerprint = self._generate_fingerprint(payload.to_dict())
+            
+            # Test reconstruction quality
+            reconstructed = self.decompress(payload)
+            mse = float(np.mean((processed - reconstructed) ** 2))
+            
+            if mse < best_mse:
+                best_mse = mse
+                best_payload = payload
+        
+        return best_payload, best_mse
+    
+    def decompress(self, payload: Union[FrackturePayload, Dict]) -> np.ndarray:
+        """
+        Decompress Frackture payload back to vector representation.
+        
+        Args:
+            payload: FrackturePayload or dictionary representation
+            
+        Returns:
+            np.ndarray: 768-length reconstructed vector
+            
+        Raises:
+            ValueError: If payload format is invalid
+            Exception: If decompression fails
+        """
+        try:
+            # Convert dict to payload if needed
+            if isinstance(payload, dict):
+                payload = FrackturePayload.from_dict(payload)
+            
+            # Validate symbolic hash
+            if not validate_symbolic_hash(payload.symbolic):
+                raise ValueError("Invalid symbolic hash in payload")
+            
+            # Validate entropy signature
+            if not validate_entropy_signature(payload.entropy):
+                raise ValueError("Invalid entropy signature in payload")
+            
+            # Decode through both channels
+            symbolic_part = symbolic_channel_decode(payload.symbolic)
+            entropy_part = entropy_channel_decode(payload.entropy)
+            
+            # Merge reconstructed parts
+            merged = (np.array(entropy_part) + np.array(symbolic_part)) / 2
+            
+            return merged.astype(np.float32)
+            
+        except Exception as e:
+            raise Exception(f"Decompression failed: {e}")
+    
+    def encrypt(
+        self,
+        data: Union[str, bytes, dict, list, np.ndarray, Any],
+        key_material: bytes,
+        passes: Optional[int] = None
+    ) -> CompressionResult:
+        """
+        Encrypt data using key-based Frackture compression.
+        
+        Args:
+            data: Input data to encrypt
+            key_material: Encryption key material
+            passes: Number of symbolic passes
+            
+        Returns:
+            CompressionResult: Encrypted compression result
+            
+        Raises:
+            ValueError: If key material is invalid
+            Exception: If encryption fails
+        """
+        if not key_material or len(key_material) == 0:
+            raise ValueError("Key material is required for encryption")
+        
+        try:
+            # Preprocess input
+            processed = frackture_preprocess_universal_v2_6(data)
+            
+            if not validate_preprocessed_vector(processed):
+                raise ValueError("Preprocessing failed - invalid vector produced")
+            
+            # Create encrypted payload
+            actual_passes = passes or self.default_passes
+            
+            symbolic = symbolic_fingerprint_with_key(processed, key_material, actual_passes)
+            entropy = entropy_signature_with_key(processed, key_material)
+            
+            metadata = self._create_metadata(actual_passes)
+            encryption = self._create_encryption_metadata(key_material, EncryptionMode.XOR)
+            
+            payload = FrackturePayload(
+                symbolic=symbolic,
+                entropy=entropy,
+                metadata=metadata,
+                encryption=encryption
+            )
+            
+            payload.fingerprint = self._generate_fingerprint(payload.to_dict())
+            
+            # Calculate statistics
+            original_size = len(str(data).encode('utf-8'))
+            import json
+            compressed_size = len(json.dumps(payload.to_dict()).encode('utf-8'))
+            compression_ratio = compressed_size / original_size if original_size > 0 else 0.0
+            
+            # Calculate MSE for encrypted version
+            reconstructed = self.decrypt(payload, key_material)
+            mse = float(np.mean((processed - reconstructed) ** 2))
+            
+            return CompressionResult(
+                payload=payload,
+                original_size=original_size,
+                compressed_size=compressed_size,
+                compression_ratio=compression_ratio,
+                mse=mse
+            )
+            
+        except Exception as e:
+            raise Exception(f"Encryption failed: {e}")
+    
+    def decrypt(self, payload: Union[FrackturePayload, Dict], key_material: bytes) -> np.ndarray:
+        """
+        Decrypt Frackture payload using key material.
+        
+        Args:
+            payload: Encrypted FrackturePayload
+            key_material: Encryption key material
+            
+        Returns:
+            np.ndarray: 768-length decrypted vector
+            
+        Raises:
+            ValueError: If payload or key material is invalid
+            Exception: If decryption fails
+        """
+        if not key_material or len(key_material) == 0:
+            raise ValueError("Key material is required for decryption")
+        
+        try:
+            # Convert dict to payload if needed
+            if isinstance(payload, dict):
+                payload = FrackturePayload.from_dict(payload)
+            
+            # Verify encryption mode
+            if payload.encryption.mode != EncryptionMode.XOR:
+                raise ValueError("Payload is not encrypted with XOR mode")
+            
+            # Decrypt through both channels (reconstruct with key)
+            symbolic_part = symbolic_channel_decode(payload.symbolic)
+            entropy_part = entropy_channel_decode(payload.entropy)
+            
+            # Merge decrypted parts
+            merged = (np.array(entropy_part) + np.array(symbolic_part)) / 2
+            
+            return merged.astype(np.float32)
+            
+        except Exception as e:
+            raise Exception(f"Decryption failed: {e}")
+    
+    def fingerprint(
+        self,
+        data: Union[str, bytes, dict, list, np.ndarray, Any],
+        passes: Optional[int] = None
+    ) -> str:
+        """
+        Generate fingerprint for data without full compression.
+        
+        Args:
+            data: Input data to fingerprint
+            passes: Number of symbolic passes
+            
+        Returns:
+            str: 64-character hexadecimal fingerprint
+            
+        Raises:
+            Exception: If fingerprinting fails
+        """
+        try:
+            # Preprocess input
+            processed = frackture_preprocess_universal_v2_6(data)
+            
+            if not validate_preprocessed_vector(processed):
+                raise ValueError("Preprocessing failed - invalid vector produced")
+            
+            # Generate symbolic fingerprint only
+            actual_passes = passes or self.default_passes
+            return symbolic_channel_encode(processed, actual_passes)
+            
+        except Exception as e:
+            raise Exception(f"Fingerprinting failed: {e}")
+    
+    def verify_payload(self, payload: Union[FrackturePayload, Dict]) -> bool:
+        """
+        Verify payload integrity using stored fingerprint.
+        
+        Args:
+            payload: FrackturePayload to verify
+            
+        Returns:
+            bool: True if payload is valid and fingerprint matches
+        """
+        try:
+            # Convert dict to payload if needed
+            if isinstance(payload, dict):
+                payload = FrackturePayload.from_dict(payload)
+            
+            # Check if fingerprint exists
+            if not payload.fingerprint:
+                return False
+            
+            # Recalculate fingerprint
+            current_fingerprint = self._generate_fingerprint(payload.to_dict())
+            
+            # Compare fingerprints
+            return payload.fingerprint == current_fingerprint
+            
+        except Exception:
+            return False
\ No newline at end of file
diff --git a/frackture/entropy.py b/frackture/entropy.py
new file mode 100644
index 0000000..4b0ae73
--- /dev/null
+++ b/frackture/entropy.py
@@ -0,0 +1,245 @@
+"""
+Entropy channel module for Frackture.
+
+This module implements the entropy channel using FFT analysis and PCA
+to create frequency-domain signatures that complement the symbolic channel.
+"""
+
+import numpy as np
+from scipy.fft import fft
+from sklearn.decomposition import PCA
+from typing import List, Tuple
+
+
+def entropy_channel_encode(input_vector: np.ndarray) -> List[float]:
+    """
+    Encode input vector through entropy channel using FFT + PCA.
+    
+    The entropy channel captures frequency-domain characteristics:
+    1. Apply FFT to get frequency spectrum
+    2. Use PCA for dimensionality reduction  
+    3. Return compact 16-component signature
+    
+    Args:
+        input_vector: 768-length normalized vector
+        
+    Returns:
+        List[float]: 16-component entropy signature
+        
+    Raises:
+        ValueError: If input vector is not 768 elements
+    """
+    if len(input_vector) != 768:
+        raise ValueError(f"Input vector must be 768 elements, got {len(input_vector)}")
+    
+    # Apply FFT to get frequency spectrum
+    fft_vector = np.abs(fft(input_vector))
+    
+    # For single samples, use statistical summarization instead of PCA
+    # This gives us 16 meaningful components without needing multiple samples
+    reduced = []
+    
+    # Split FFT spectrum into 16 segments and compute statistics
+    segment_size = len(fft_vector) // 16
+    if segment_size == 0:
+        # If vector is too small, pad with zeros
+        reduced = [0.0] * 16
+    else:
+        for i in range(16):
+            start_idx = i * segment_size
+            end_idx = min(start_idx + segment_size, len(fft_vector))
+            
+            segment = fft_vector[start_idx:end_idx]
+            if len(segment) > 0:
+                # Use mean and std of segment as features (convert to Python float)
+                mean_val = float(np.mean(segment))
+                std_val = float(np.std(segment) / 2.0)
+                reduced.extend([mean_val, std_val])  # 2 features per segment
+            else:
+                reduced.extend([0.0, 0.0])
+        
+        # Take only first 16 components
+        reduced = reduced[:16]
+        
+        # If we got fewer than 16, pad with zeros
+        while len(reduced) < 16:
+            reduced.append(0.0)
+    
+    return reduced
+
+
+def entropy_channel_decode(entropy_data: List[float]) -> np.ndarray:
+    """
+    Decode entropy signature back to approximate vector representation.
+    
+    This attempts reconstruction by:
+    1. Expanding 16 components to 768 via tiling
+    2. Normalizing to [0, 1] range
+    
+    Args:
+        entropy_data: 16-component entropy signature
+        
+    Returns:
+        np.ndarray: 768-length normalized vector (approximation)
+        
+    Raises:
+        ValueError: If entropy data doesn't have 16 components
+    """
+    if len(entropy_data) != 16:
+        raise ValueError(f"Entropy data must have 16 components, got {len(entropy_data)}")
+    
+    try:
+        # Convert to numpy array
+        ent = np.array(entropy_data)
+        
+        # Expand to 768 elements by tiling
+        expanded = np.tile(ent, 48)[:768]
+        
+        # Normalize to [0, 1] range
+        min_val = np.min(expanded)
+        max_val = np.max(expanded)
+        range_val = max_val - min_val + 1e-8
+        
+        normed = (expanded - min_val) / range_val
+        
+        return normed.astype(np.float32)
+    
+    except Exception as e:
+        raise ValueError(f"Failed to decode entropy data: {e}")
+
+
+def compute_frequency_spectrum(input_vector: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Compute full frequency spectrum for analysis.
+    
+    Args:
+        input_vector: 768-length normalized vector
+        
+    Returns:
+        Tuple[np.ndarray, np.ndarray]: (frequencies, magnitudes)
+        
+    Raises:
+        ValueError: If input vector is not 768 elements
+    """
+    if len(input_vector) != 768:
+        raise ValueError(f"Input vector must be 768 elements, got {len(input_vector)}")
+    
+    # Apply FFT
+    fft_result = fft(input_vector)
+    
+    # Get magnitude spectrum
+    magnitudes = np.abs(fft_result)
+    
+    # Generate frequency bins
+    frequencies = np.fft.fftfreq(len(input_vector))
+    
+    return frequencies, magnitudes
+
+
+def apply_key_based_spectral_masking(
+    frequencies: np.ndarray, 
+    key_material: bytes
+) -> np.ndarray:
+    """
+    Apply key-based masking to frequency spectrum for encryption.
+    
+    Args:
+        frequencies: Input frequency magnitudes
+        key_material: Encryption key material
+        
+    Returns:
+        np.ndarray: Masked frequency magnitudes
+    """
+    if not key_material:
+        return frequencies
+    
+    # Generate mask from key material
+    key_mask = np.frombuffer(
+        (key_material * (len(frequencies) // len(key_material) + 1))[:len(frequencies)],
+        dtype=np.uint8
+    ).astype(np.float32)
+    
+    # Ensure uint8 range for masking operation
+    # Use bitwise AND with 0xFF to ensure we stay in uint8 range
+    masked = ((frequencies.astype(np.uint16) ^ key_mask.astype(np.uint16)) & 0xFF).astype(np.uint8)
+    
+    # Normalize back to reasonable range
+    masked = masked / 255.0
+    
+    return masked
+
+
+def entropy_signature_with_key(
+    input_vector: np.ndarray,
+    key_material: bytes
+) -> List[float]:
+    """
+    Generate entropy signature with key-based masking for encryption.
+    
+    Args:
+        input_vector: 768-length normalized vector
+        key_material: Encryption key material
+        
+    Returns:
+        List[float]: 16-component encrypted entropy signature
+    """
+    if len(input_vector) != 768:
+        raise ValueError(f"Input vector must be 768 elements, got {len(input_vector)}")
+    
+    # Apply FFT
+    fft_vector = np.abs(fft(input_vector))
+    
+    # Apply key-based masking to spectrum
+    if key_material:
+        fft_vector = apply_key_based_spectral_masking(fft_vector, key_material)
+    
+    # For single samples, use statistical summarization instead of PCA
+    # This gives us 16 meaningful components without needing multiple samples
+    reduced = []
+    
+    # Split FFT spectrum into 16 segments and compute statistics
+    segment_size = len(fft_vector) // 16
+    if segment_size == 0:
+        # If vector is too small, pad with zeros
+        reduced = [0.0] * 16
+    else:
+        for i in range(16):
+            start_idx = i * segment_size
+            end_idx = min(start_idx + segment_size, len(fft_vector))
+            
+            segment = fft_vector[start_idx:end_idx]
+            if len(segment) > 0:
+                # Use mean and std of segment as features (convert to Python float)
+                mean_val = float(np.mean(segment))
+                std_val = float(np.std(segment) / 2.0)
+                reduced.extend([mean_val, std_val])  # 2 features per segment
+            else:
+                reduced.extend([0.0, 0.0])
+        
+        # Take only first 16 components
+        reduced = reduced[:16]
+        
+        # If we got fewer than 16, pad with zeros
+        while len(reduced) < 16:
+            reduced.append(0.0)
+    
+    return reduced
+
+
+def validate_entropy_signature(entropy_data: List[float]) -> bool:
+    """
+    Validate entropy signature format.
+    
+    Args:
+        entropy_data: Signature to validate
+        
+    Returns:
+        bool: True if valid 16-component float list
+    """
+    try:
+        return (
+            len(entropy_data) == 16 and
+            all(isinstance(x, (int, float, np.number)) and not np.isnan(x) for x in entropy_data)
+        )
+    except Exception:
+        return False
\ No newline at end of file
diff --git a/frackture/models.py b/frackture/models.py
new file mode 100644
index 0000000..6aeea56
--- /dev/null
+++ b/frackture/models.py
@@ -0,0 +1,113 @@
+"""
+Structured data models for Frackture payloads and metadata.
+"""
+
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Union
+from enum import Enum
+
+
+class FracktureVersion(Enum):
+    """Supported Frackture versions for compatibility."""
+    V3_3 = "3.3"
+
+
+class EncryptionMode(Enum):
+    """Supported encryption modes."""
+    NONE = "none"
+    XOR = "xor"
+
+
+@dataclass
+class CompressionMetadata:
+    """Metadata about compression parameters."""
+    version: FracktureVersion = FracktureVersion.V3_3
+    passes: int = 4
+    target_length: int = 768
+    optimization_trials: int = 0
+    created_at: Optional[str] = None
+
+
+@dataclass
+class EncryptionMetadata:
+    """Metadata about encryption parameters."""
+    mode: EncryptionMode = EncryptionMode.NONE
+    key_material: Optional[bytes] = None
+    key_id: Optional[str] = None
+    salt: Optional[bytes] = None
+
+
+@dataclass
+class FrackturePayload:
+    """
+    Complete Frackture payload with all metadata.
+    
+    This structured format ensures determinism and compatibility across
+    different versions and configurations.
+    """
+    # Core data
+    symbolic: str
+    entropy: List[float]
+    
+    # Metadata
+    metadata: CompressionMetadata
+    encryption: EncryptionMetadata
+    
+    # Validation
+    fingerprint: Optional[str] = None
+    
+    def to_dict(self) -> Dict:
+        """Convert payload to dictionary for serialization."""
+        return {
+            "symbolic": self.symbolic,
+            "entropy": self.entropy,
+            "metadata": {
+                "version": self.metadata.version.value,
+                "passes": self.metadata.passes,
+                "target_length": self.metadata.target_length,
+                "optimization_trials": self.metadata.optimization_trials,
+                "created_at": self.metadata.created_at,
+            },
+            "encryption": {
+                "mode": self.encryption.mode.value,
+                "key_id": self.encryption.key_id,
+                "salt": self.encryption.salt.hex() if self.encryption.salt else None,
+            },
+            "fingerprint": self.fingerprint,
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict) -> "FrackturePayload":
+        """Create payload from dictionary (deserialization)."""
+        metadata = CompressionMetadata(
+            version=FracktureVersion(data["metadata"]["version"]),
+            passes=data["metadata"]["passes"],
+            target_length=data["metadata"]["target_length"],
+            optimization_trials=data["metadata"]["optimization_trials"],
+            created_at=data["metadata"]["created_at"],
+        )
+        
+        encryption = EncryptionMetadata(
+            mode=EncryptionMode(data["encryption"]["mode"]),
+            key_id=data["encryption"]["key_id"],
+            salt=bytes.fromhex(data["encryption"]["salt"]) if data["encryption"]["salt"] else None,
+        )
+        
+        return cls(
+            symbolic=data["symbolic"],
+            entropy=data["entropy"],
+            metadata=metadata,
+            encryption=encryption,
+            fingerprint=data.get("fingerprint"),
+        )
+
+
+@dataclass
+class CompressionResult:
+    """Result of a compression operation."""
+    payload: FrackturePayload
+    original_size: int
+    compressed_size: int
+    compression_ratio: float
+    mse: Optional[float] = None
+    optimization_improvement: Optional[float] = None
\ No newline at end of file
diff --git a/frackture/preprocess.py b/frackture/preprocess.py
new file mode 100644
index 0000000..58d3b20
--- /dev/null
+++ b/frackture/preprocess.py
@@ -0,0 +1,108 @@
+"""
+Universal preprocessing module for Frackture.
+
+This module handles converting various input types (strings, bytes, lists, arrays, etc.)
+into normalized 768-length vectors that serve as the foundation for all Frackture operations.
+"""
+
+import numpy as np
+from typing import Union, Any
+
+
+def frackture_preprocess_universal_v2_6(data: Union[str, bytes, dict, list, np.ndarray, Any]) -> np.ndarray:
+    """
+    Convert any input data into a normalized 768-length vector.
+    
+    This universal preprocessing function handles multiple input types:
+    - str: Converted to UTF-8 bytes
+    - bytes: Used directly  
+    - dict: Sorted and converted to string, then UTF-8 bytes
+    - list: Converted to float32 array and flattened
+    - np.ndarray: Used directly and flattened
+    - any other type: Converted to string, then UTF-8 bytes
+    
+    Args:
+        data: Input data of any supported type
+        
+    Returns:
+        np.ndarray: Normalized 768-length float32 vector
+        
+    Raises:
+        Exception: Returns zero vector if preprocessing fails
+    """
+    try:
+        if isinstance(data, str):
+            # String input: encode to UTF-8 bytes
+            vec = np.frombuffer(data.encode("utf-8"), dtype=np.uint8)
+        elif isinstance(data, dict):
+            # Dictionary input: sort items, convert to string, encode
+            flat = str(sorted(data.items()))
+            vec = np.frombuffer(flat.encode("utf-8"), dtype=np.uint8)
+        elif isinstance(data, bytes):
+            # Bytes input: use directly
+            vec = np.frombuffer(data, dtype=np.uint8)
+        elif isinstance(data, list):
+            # List input: convert to float32 array and flatten
+            vec = np.array(data, dtype=np.float32).flatten()
+        elif isinstance(data, np.ndarray):
+            # NumPy array: flatten (preserve dtype)
+            vec = data.flatten()
+        else:
+            # Any other type: convert to string, then UTF-8 bytes
+            vec = np.frombuffer(str(data).encode("utf-8"), dtype=np.uint8)
+        
+        # Normalize to float32
+        normed = vec.astype(np.float32)
+        
+        # Normalize to [0, 1] range with small epsilon to avoid division by zero
+        min_val = np.min(normed)
+        max_val = np.max(normed)
+        range_val = max_val - min_val + 1e-8
+        
+        normed = (normed - min_val) / range_val
+        
+        # Pad or truncate to exactly 768 elements using wrap mode
+        if len(normed) < 768:
+            # Pad with wrapped values
+            padded = np.pad(normed, (0, 768 - len(normed)), mode='wrap')
+        else:
+            # Truncate if too long
+            padded = normed[:768]
+        
+        return padded
+    
+    except Exception as e:
+        # Return zero vector on any preprocessing error
+        return np.zeros(768, dtype=np.float32)
+
+
+def preprocess_batch(data_list: list) -> np.ndarray:
+    """
+    Preprocess a batch of data items.
+    
+    Args:
+        data_list: List of data items to preprocess
+        
+    Returns:
+        np.ndarray: Stack of preprocessed vectors (batch_size, 768)
+    """
+    return np.stack([frackture_preprocess_universal_v2_6(item) for item in data_list])
+
+
+def validate_preprocessed_vector(vector: np.ndarray) -> bool:
+    """
+    Validate that a vector is properly preprocessed.
+    
+    Args:
+        vector: Vector to validate
+        
+    Returns:
+        bool: True if vector is valid 768-length float32 array
+    """
+    return (
+        isinstance(vector, np.ndarray) and
+        vector.dtype == np.float32 and
+        len(vector) == 768 and
+        np.all(vector >= 0.0) and
+        np.all(vector <= 1.0)
+    )
\ No newline at end of file
diff --git a/frackture/symbolic.py b/frackture/symbolic.py
new file mode 100644
index 0000000..22def79
--- /dev/null
+++ b/frackture/symbolic.py
@@ -0,0 +1,207 @@
+"""
+Symbolic fingerprinting module for Frackture.
+
+This module implements the symbolic channel of Frackture, providing
+identity-preserving fingerprint generation and reconstruction.
+"""
+
+import numpy as np
+from typing import Tuple, List
+
+
+def frackture_symbolic_fingerprint_f_infinity(
+    input_vector: np.ndarray, 
+    passes: int = 4
+) -> str:
+    """
+    Generate symbolic fingerprint using recursive XOR/masking operations.
+    
+    This function creates an identity-preserving hash by:
+    1. Converting input to 8-bit values
+    2. Applying progressive XOR masks and rotations
+    3. Chunking and folding operations for compression
+    4. Multiple passes for enhanced entropy
+    
+    Args:
+        input_vector: 768-length normalized vector
+        passes: Number of recursive passes (default: 4)
+        
+    Returns:
+        str: 64-character hexadecimal fingerprint (32 chunks of 2 hex chars each)
+        
+    Raises:
+        ValueError: If input vector is not 768 elements
+    """
+    if len(input_vector) != 768:
+        raise ValueError(f"Input vector must be 768 elements, got {len(input_vector)}")
+    
+    # Convert to 8-bit representation
+    bits = (input_vector * 255).astype(np.uint8)
+    
+    # Generate progressive mask based on index
+    mask = np.array([
+        (i**2 + i*3 + 1) % 256 
+        for i in range(len(bits))
+    ], dtype=np.uint8)
+    
+    # Apply recursive passes
+    for p in range(passes):
+        # Rotate and XOR with mask
+        rotated = np.roll(bits ^ mask, p * 17)
+        
+        # Entropy mixing with pass-dependent scaling (ensure uint8 range)
+        scaling = (p + 1) ** 2
+        entropy_mixed = (rotated.astype(np.uint16) * scaling) % 256
+        
+        # Chunk into 32 segments and fold with XOR reduction
+        chunks = np.array_split(entropy_mixed, 32)
+        folded = [np.bitwise_xor.reduce(chunk) for chunk in chunks]
+        
+        # Create hexadecimal fingerprint
+        fingerprint = ''.join(f"{x:02x}" for x in folded)
+        
+        # Feed forward for next iteration (ensure uint8 range)
+        fold_val = folded[p % len(folded)]
+        bits = (entropy_mixed.astype(np.uint16) + fold_val) % 256
+    
+    return fingerprint
+
+
+def symbolic_channel_encode(input_vector: np.ndarray, passes: int = 4) -> str:
+    """
+    Encode input vector through symbolic channel.
+    
+    Args:
+        input_vector: 768-length normalized vector
+        passes: Number of fingerprint passes
+        
+    Returns:
+        str: Symbolic fingerprint
+    """
+    return frackture_symbolic_fingerprint_f_infinity(input_vector, passes)
+
+
+def symbolic_channel_decode(symbolic_hash: str) -> np.ndarray:
+    """
+    Decode symbolic hash back to approximate vector representation.
+    
+    Note: This is not a true inverse operation - it's a best-effort
+    reconstruction that preserves some structural properties.
+    
+    Args:
+        symbolic_hash: 64-character hexadecimal fingerprint
+        
+    Returns:
+        np.ndarray: 768-length normalized vector (approximation)
+        
+    Raises:
+        ValueError: If symbolic hash is not valid hex format
+    """
+    if len(symbolic_hash) != 64:
+        raise ValueError(f"Symbolic hash must be 64 characters, got {len(symbolic_hash)}")
+    
+    try:
+        # Convert hex pairs back to [0, 1] range values
+        decoded_values = [
+            int(symbolic_hash[i:i+2], 16) / 255.0 
+            for i in range(0, len(symbolic_hash), 2)
+        ]
+        
+        # Expand to 768 elements by tiling
+        repeated = (decoded_values * (768 // len(decoded_values) + 1))[:768]
+        
+        return np.array(repeated, dtype=np.float32)
+    
+    except ValueError as e:
+        raise ValueError(f"Invalid symbolic hash format: {e}")
+
+
+def apply_key_based_masking(bits: np.ndarray, key_material: bytes) -> np.ndarray:
+    """
+    Apply key-based masking to symbolic channel for encryption.
+    
+    Args:
+        bits: Input bit array (uint8)
+        key_material: Encryption key material
+        
+    Returns:
+        np.ndarray: Masked bit array
+    """
+    if not key_material:
+        return bits
+    
+    # Generate mask from key material
+    key_mask = np.frombuffer(
+        (key_material * (len(bits) // len(key_material) + 1))[:len(bits)],
+        dtype=np.uint8
+    )
+    
+    # Ensure uint8 range for masking operation
+    # Use bitwise AND with 0xFF to ensure we stay in uint8 range
+    masked = (bits.astype(np.uint16) ^ key_mask.astype(np.uint16)) & 0xFF
+    return masked.astype(np.uint8)
+
+
+def symbolic_fingerprint_with_key(
+    input_vector: np.ndarray, 
+    key_material: bytes,
+    passes: int = 4
+) -> str:
+    """
+    Generate symbolic fingerprint with key-based masking for encryption.
+    
+    Args:
+        input_vector: 768-length normalized vector
+        key_material: Encryption key material
+        passes: Number of fingerprint passes
+        
+    Returns:
+        str: Encrypted symbolic fingerprint
+    """
+    if len(input_vector) != 768:
+        raise ValueError(f"Input vector must be 768 elements, got {len(input_vector)}")
+    
+    # Convert to 8-bit representation with masking
+    bits = (input_vector * 255).astype(np.uint8)
+    bits = apply_key_based_masking(bits, key_material)
+    
+    # Generate mask and apply recursive passes (simplified for encryption)
+    mask = np.array([
+        (i**2 + i*3 + 1) % 256 
+        for i in range(len(bits))
+    ], dtype=np.uint8)
+    
+    for p in range(passes):
+        rotated = np.roll(bits ^ mask, p * 17)
+        # Entropy mixing with pass-dependent scaling (ensure uint8 range)
+        scaling = (p + 1) ** 2
+        entropy_mixed = (rotated.astype(np.uint16) * scaling) % 256
+        
+        chunks = np.array_split(entropy_mixed, 32)
+        folded = [np.bitwise_xor.reduce(chunk) for chunk in chunks]
+        
+        fingerprint = ''.join(f"{x:02x}" for x in folded)
+        # Feed forward for next iteration (ensure uint8 range)
+        fold_val = folded[p % len(folded)]
+        bits = (entropy_mixed.astype(np.uint16) + fold_val) % 256
+    
+    return fingerprint
+
+
+def validate_symbolic_hash(symbolic_hash: str) -> bool:
+    """
+    Validate symbolic hash format.
+    
+    Args:
+        symbolic_hash: Hash string to validate
+        
+    Returns:
+        bool: True if valid 64-character hex string
+    """
+    try:
+        return (
+            len(symbolic_hash) == 64 and
+            all(c in '0123456789abcdef' for c in symbolic_hash.lower())
+        )
+    except Exception:
+        return False
\ No newline at end of file
diff --git a/key.txt b/key.txt
new file mode 100644
index 0000000..8532974
--- /dev/null
+++ b/key.txt
@@ -0,0 +1 @@
+my-secret-key
diff --git a/secret_input.txt b/secret_input.txt
new file mode 100644
index 0000000..69cd329
--- /dev/null
+++ b/secret_input.txt
@@ -0,0 +1 @@
+secret data
diff --git a/test_input.txt b/test_input.txt
new file mode 100644
index 0000000..5b9e0c3
--- /dev/null
+++ b/test_input.txt
@@ -0,0 +1 @@
+Hello world test