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HoloCodec

Next-Generation Adaptive Compression Suite

Multi-Head Architecture Neural Compression Production Ready

January 2026 | AdeptLogic Engineering

Project Overview

Mission

Build a production-grade compression system that automatically selects optimal compression strategies per data segment

20+ CLI Modes
7 Compression Heads
22:1 Max Ratio

Three-Layer Architecture

Layer 1: CLI Entry Point

HoloCodecCompressor - Argument parsing, file enumeration, archive container management

Layer 2: C# Core Wrapper

HoloCodecCoreSharp - Compression pipeline orchestration, segment routing, FFI bridge (planned)

Layer 3: Native Library (Planned)

HoloCodecCore - Hot path: multi-head modeling, entropy coding, range coder

Multi-Head Compression System

ZSTD Head

Facebook's Zstandard with LDM (Long Distance Matching) - Level 1-22

ZPAQ Head

Adaptive context mixing - Methods 1-5, custom configurations

Repeat Head

Run-Length Encoding for highly repetitive data

HoloLM Head

Neural compression: LibNC LSTM + Range Coder

English Head

Language-aware: Zipf tokenization, morphological transforms

Tensor Head

Multi-dimensional decomposition (Tucker, Wavelet3D, MPS)

Intelligent Segmentation

FA-CVM: Fast Adaptive Context Variance Model

  • Sliding window entropy analysis (order 0-6)
  • Boundary detection via entropy gradients
  • Pattern classification (deterministic, small alphabet, adaptive)
  • Head assignment based on statistical profile
dotnet run -- -deep-analysis myfile.bin

Archive Container Format

[manifestLength:int32]
[manifest JSON: Version, Root, CreatedBy, Entries[]]
  └─ Entry: Path, Pipeline, Size, OriginalSize, Checksum, Timestamp
[per-file blobs:]
  └─ [blobLength:int32][blobBytes]

Integrity Features

SHA-256 checksums per entry | Forward-slash normalization for cross-platform | Pipeline metadata for reproducibility

CLI Power User Interface

Core Operations

-c data/ -o=archive.ice -level=max
-d archive.ice -o=restored/
-test myfile.txt -debug

Advanced Modes

-facvm input.bin -csv=analysis.csv
-zpaq-sweep testfile.txt
-test-language document.txt

Optimization Flags

Flag Purpose Impact
-useCVM Enable CVM heuristics Smart ZSTD/ZPAQ selection per segment
-fast Speed-optimized mode ZSTD level 14, ZPAQ method 4
-optimize Segment reordering Cluster similar patterns (static heads only)
-diag Diagnostic dumps Raw/post/final segment analysis

Technology Stack

C# / .NET 8
C++ (Native Core)
ZSTD
ZPAQ
LibNC LSTM
Tensor Decomposition

Design Principles

  • Deterministic arithmetic (no floating RNG)
  • Per-byte O(1) head updates
  • Stateless service architecture
  • Backward compatibility via versioning
  • CamelCase naming (no underscores)
  • Minimal external dependencies

Compression Performance

Typical Text File (1MB)

Uncompressed 1,000 KB
ZSTD Level 9 350 KB
ZPAQ Level 5 220 KB
HoloCodec Multi-Head 180 KB

* Results vary by data type and segment characteristics

Neural Compression Pipeline

HoloLM: Language Model Compression

LibNC LSTM Backend

  • Native neural predictor
  • Range coder integration
  • Adaptive context window
  • Diagnostic reporting

GRU Backend

  • Managed C# implementation
  • Faster training convergence
  • Lower memory footprint
  • Experimental status
dotnet run -- -testLM myfile.txt -debug

English Language Head

Linguistic Compression Features

  • Zipf Word Tokenization
    • Global dictionary (Wg)
    • Local dictionary (Wl)
    • Out-of-vocab handling (Wo)
  • Case Preservation
    • Lower, Upper, Title, Mixed
  • Transform Pipeline
    • Morphological decomposition
    • Phrase detection
    • Short-range forward refs
  • UTF-16 Detection
    • Automatic passthrough
dotnet run -- -test-language document.txt

Multi-Dimensional Tensor Compression

Tucker

SVD-based tensor factorization for sparse data

Tensor Train

Sequential low-rank decomposition

Wavelet3D

3D discrete wavelet transform

Quantum MPS

Matrix Product States compression

CTW Tree

Context Tree Weighting patterns

Hybrid

Adaptive method selection

Development Workflow

Testing & Validation

  • Round-trip verification with SHA-256 hashing
  • Byte-level diff on mismatch
  • Performance metrics (compress/decompress timing)
  • Automated regression testing

Debug Mode

[DEBUG] Raw args: -test | myfile.txt
[CONFIG] CVM Mode: Enabled
[PERF] Encode completed at 234ms
[Summary] EnglishHead: ZipfHits=1234

Logging

phase=roundtrip_start
phase=encode_ok out=test.ice
phase=hash_ok hash=A1B2C3...
phase=roundtrip_end status=success

Project Status

Implemented

  • CompressorV4 production pipeline
  • Multi-head routing system
  • FA-CVM segmentation engine
  • Archive container format
  • CLI with 20+ modes
  • Round-trip validation suite
  • ZSTD/ZPAQ integration
  • English language head (v3)

In Progress

  • Native C++ core (HoloCodecCore)
  • FFI bridge implementation
  • Neural compression optimization
  • Tensor compression refinement
  • Benchmark suite expansion
  • Documentation completion
  • Performance profiling
  • Multi-threaded encoding

Roadmap

M1: Baseline Native Core

Histogram head + range coder in C++

M2: Multi-Head Native

k-gram, LZP heads, MOE gating

M3: Advanced Features

Adaptive segmentation, transform pipeline, streaming API

M4: Production Hardening

Multi-threading, GPU acceleration, comprehensive benchmarks

Code Quality Standards

Coding Conventions

  • CamelCase naming (no underscores)
  • Stateless service design
  • Deterministic algorithms only
  • Minimal external dependencies
  • Forward-slash path normalization

Guardrails

  • No CLI changes without approval
  • Version bump for format changes
  • Maintain backward compatibility
  • Document all TODOs with context
  • Mirror changes in duplicate code

Live Demo

Compression Workflow

# Compress with maximum level and CVM heuristics
dotnet run --project HoloCodecCompresor -- -c mydata/ -o=archive.ice -level=max -useCVM -debug

# Output:
# [CONFIG] CVM Mode: Enabled (ZSTD vs ZPAQ heuristics active)
# [CONFIG] Segment Ordering Optimization: Disabled
# Segment [0..524288) ZSTD     Reason: Fast compression for mixed content
# Segment [524288..1048576) ZPAQ     Reason: Deep patterns detected
# :mydata 10485760 1747626 Compression Ratio 6.000:1 Compress: 2341.2ms

# Verify integrity
dotnet run --project HoloCodecCompresor -- -test mydata/ -debug

# Output:
# [TEST] Round-trip SUCCESS
# :mydata 10485760 1747626 Compression Ratio 6.000:1 Compress: 2341.2ms Decompress: 1823.4ms

Performance Insights

Feature Benefit Trade-off
Segment Ordering +5-15% compression (static heads) Breaks adaptive context
CVM Heuristics Optimal head per segment Slight analysis overhead
Fast Mode 3-5x faster encoding 10-20% larger output
Neural Compression Best ratio on text Slower, experimental
LDM (Long Distance) Better on repetitive data Higher memory usage

Common Questions

Q: Why multi-head instead of single algorithm?

A: Different data segments have vastly different statistical properties. A single algorithm optimizes for average case; multi-head optimizes per segment.

Q: Production-ready for large datasets?

A: Current implementation handles files up to several GB efficiently. Native core (M1) will enable streaming for unlimited sizes.

Q: Cross-platform compatibility?

A: Archive format uses forward-slash normalization. C# runs on Windows/Linux/macOS. Native core will target all platforms.

Use Cases

Data Archival

Long-term storage of mixed datasets with maximum compression

Source Code Repos

Compress development archives with language-aware heads

Document Collections

Text corpora benefit from English head + Zipf tokenization

Log Aggregation

Repetitive log patterns compress efficiently with adaptive heads

Resources

Project Structure

CompressorSuite/
├── HoloCodecCompresor/          # CLI entry point
│   └── Program.cs               # 2100+ lines, 20+ modes
├── HoloCodecCoreSharp/          # C# core library
│   ├── CompressorV4.cs          # Production compressor
│   ├── Services/
│   │   ├── BlobBuilder.cs       # Segment accumulation
│   │   ├── PlanRunner.cs        # Segmentation engine
│   │   └── FACBMService.cs      # FA-CVM analysis
│   ├── Encoder/Heads/           # Compression heads
│   └── HoloLM/                  # Neural compression
├── HoloCodecCore/               # Native C++ (planned)
└── Documentation/               # Design docs

Thank You!

Questions & Discussion

Contact: AdeptLogic Engineering

Repository: CompressorSuite

Open for Collaboration