Skip to main content

Thermodynamic Consensus Protocol - Byzantine Fault Tolerant consensus using statistical mechanics

Project description

ThermoTruth Protocol: Thermodynamic Consensus for Sybil-Resistant Networks

Author: Kevin KULL | X.com: @KULLAILABS

License Python

Thermodynamic Truth is a novel consensus protocol that leverages physical laws—specifically energy conservation and entropy minimization—to achieve Byzantine Fault Tolerance (BFT) in open, permissionless networks.

Unlike traditional BFT protocols that rely on voting (communication-heavy) or Proof-of-Work that relies on lottery (energy-wasteful), ThermoTruth uses Proof-of-Work as a thermodynamic cost function to secure the network against Sybil attacks while maintaining $O(n)$ scalability.

Dashboard

⚠️ Development Status

Current State: This repository contains the theoretical framework, whitepaper, and conceptual benchmarks for ThermoTruth. The core protocol implementation is actively under development.

Available Now:

  • ✅ Comprehensive whitepaper with thermodynamic derivations
  • ✅ Theoretical framework and protocol specification
  • ✅ Conceptual benchmark simulations
  • ✅ Research documentation

In Development:

  • 🚧 Core consensus protocol implementation
  • 🚧 Network layer (gRPC communication)
  • 🚧 Node runtime and CLI tools
  • 🚧 Real distributed benchmarks
  • 🚧 Validation tests

The claims below are based on theoretical analysis and simulated models. Experimental validation with a working implementation is ongoing.

🚀 Key Claims

Based on experimental results (see docs/results_section.pdf):

  1. Linear Scalability: Achieves $O(n)$ latency scaling, maintaining sub-second finality (500ms) at 100 nodes.
  2. Throughput Saturation: Sustains 200 TPS regardless of cluster size, outperforming HoneyBadger BFT by 50x.
  3. Byzantine Resilience: Self-heals under 33% Byzantine attacks with consensus error staying below 0.05°C.
  4. Bandwidth Efficiency: Reduces network bandwidth by 90% compared to asynchronous BFT alternatives.
  5. Thermodynamic Necessity: Removing PoW results in a 6000% increase in consensus error, validating the physics-based security model.

📦 Installation

Note: The package is not yet available on PyPI as the implementation is under development.

To explore the theoretical framework and run conceptual benchmarks:

# Clone the repository
git clone https://github.com/Kuonirad/thermo-truth-proto.git
cd thermo-truth-proto

# Install dependencies for benchmarks
pip install numpy matplotlib

# Run conceptual benchmarks
python benchmarks/comparative_benchmark.py
python benchmarks/ablation_study.py

⚡ Quick Start (Coming Soon)

Once the implementation is complete, you'll be able to start a local cluster:

# Terminal 1: Start the bootstrap node
python -m thermodynamic_truth.node --id 0 --port 50051

# Terminal 2: Start a peer
python -m thermodynamic_truth.node --id 1 --port 50052 --peer localhost:50051

See Quick Start Guide for the planned operator interface.

📂 Repository Structure

  • src/: Core protocol implementation (under development).
  • benchmarks/: Conceptual benchmark simulations comparing theoretical performance.
  • docs/: Research papers, whitepaper, test plans, and guides.

📜 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Copyright (c) 2025 Kevin KULL.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

thermodynamic_truth-1.0.1.tar.gz (1.5 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

thermodynamic_truth-1.0.1-py3-none-any.whl (38.1 kB view details)

Uploaded Python 3

File details

Details for the file thermodynamic_truth-1.0.1.tar.gz.

File metadata

  • Download URL: thermodynamic_truth-1.0.1.tar.gz
  • Upload date:
  • Size: 1.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for thermodynamic_truth-1.0.1.tar.gz
Algorithm Hash digest
SHA256 fa06cac7d1206be93a79be09d8a5d082bfd264a45df00036c2b7436803fd3486
MD5 b59419cea59137d88b63bf3e55902628
BLAKE2b-256 023cfaec61d57181973c35b2a9393f92af54f902e7b02ca3c836c2ecfca8922c

See more details on using hashes here.

Provenance

The following attestation bundles were made for thermodynamic_truth-1.0.1.tar.gz:

Publisher: publish.yml on Kuonirad/thermo-truth-proto

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file thermodynamic_truth-1.0.1-py3-none-any.whl.

File metadata

File hashes

Hashes for thermodynamic_truth-1.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 5ba62e82520d3787e4cb7d7a2791da86fe422181d242f397e44fdf6d70075ccb
MD5 a07827f9f17ad887b6f3537a81066362
BLAKE2b-256 10e64001d76503ab496f53114b4149385f4355e9b42ed377fab8c2a2c90bb320

See more details on using hashes here.

Provenance

The following attestation bundles were made for thermodynamic_truth-1.0.1-py3-none-any.whl:

Publisher: publish.yml on Kuonirad/thermo-truth-proto

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page