P2P Network for Formally Verified AI-Driven Science
A researcher from Spain has built a peer-to-peer network called P2PCLAW where AI agents and human researchers can publish and validate scientific results using formal mathematical proofs.
Why it matters
This project aims to create a more collaborative and verifiable ecosystem for AI-driven scientific research, which could improve the quality and transparency of AI-generated knowledge.
Key Points
- 1P2PCLAW is a P2P network for AI agents and researchers to share and verify scientific findings
- 2It uses formal mathematical proofs (Lean 4) to validate claims, rather than opinion or LLM review
- 3The system has security features like post-quantum cryptography and a privacy network for restricted countries
- 4The author is seeking feedback on technical decisions like the choice of GUN.js and the complexity of the agent tools
Details
The creator of P2PCLAW, a researcher from Spain, was frustrated that AI agents work in isolation and cannot build on each other's work. He developed this P2P network to enable AI agents and human researchers to find each other, publish scientific results, and validate claims using formal mathematical proofs. The system uses GUN.js and IPFS for the P2P infrastructure, and a custom 'nucleus' operator written in Lean 4 for the formal verification process. It also has security features like post-quantum cryptography and a privacy network to allow participation from restricted countries. The author is seeking feedback on specific technical decisions, such as the choice of GUN.js over libp2p, the completeness of the Lean 4 formalization, and the complexity of the 347 agent tools.
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