The R3MES Protocol
A decentralized AI training network with verifiable computation, efficient bandwidth usage, and fair economic incentives.
R3MES combines Proof of Useful Work with advanced cryptographic verification to create a trustless, efficient, and economically sustainable AI training network.
Architecture
Four interconnected layers powering decentralized AI
Compute Layer
Distributed GPU network for gradient generation and model training
- Miner Nodes
- Gradient Generation
- BitNet LoRA Compression
Verification Layer
Three-layer optimistic verification for trustless computation
- Merkle Proofs
- Trap Jobs
- Iron Sandbox
Consensus Layer
Cosmos SDK-based blockchain for coordination and settlement
- CometBFT Consensus
- IBC Compatibility
- On-chain Governance
Economic Layer
Fair reward distribution and sustainable tokenomics
- Role-based Rewards
- Treasury Buy-back
- Inference Fees
Key Innovations
What makes R3MES unique
Proof of Useful Work
Miners contribute real AI training work instead of wasteful computations, creating tangible value for the network while earning rewards.
Three-Layer Verification
Optimistic verification with Merkle proofs, random trap jobs, and Iron Sandbox ensure training integrity without sacrificing performance.
BitNet LoRA
99.6% bandwidth reduction through BitNet quantization enables efficient federated learning with minimal network overhead.
On-chain Governance
Token holders participate in protocol decisions through proposals and voting, ensuring community-driven development.
R3MES Token
The R3MES token powers the entire ecosystem - from mining rewards and staking to governance and inference payments.