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AI Services Documentation
FractalAI is building decentralized AI inference, training, and cross-chain oracle services powered by fractal compression and the golden ratio.
Roadmap — not yet in production. The AI inference, federated training, and cross-chain oracle RPC methods and contracts described below are specified and under development. They are not served by the production node today, and the FractalAIOracle contract is not yet deployed on-chain. This page documents the intended interface, not a live service.
Overview
The FractalAI network enables any blockchain to access AI capabilities through its distributed compute infrastructure. Key components:
FANE Engine
On-chain neural engine supporting Dense, Transformer, CNN, and RL architectures. Uses 4-bit PhiTensor quantization (16x compression via golden ratio quantization table).
Federated Training
FedPhiAvg aggregation distributes model training across nodes. Gradient gossip with QFC compression and phi-weighted age decay. Anti-poisoning via median absolute deviation filtering.
Cross-Chain Oracle ROADMAP
Planned: EVM chains request AI inference via the FractalAIOracle contract, with results delivered via bridge relayer and a 0.16% phi-based fee. Contract not yet deployed.
Model Marketplace
Register models on-chain with versioning and pricing. Revenue split: 61.8% creator, 38.2% compute nodes (golden ratio). Inference fees in FRAI tokens.
RPC Endpoints
Planned JSON-RPC interface for AI services (not yet served in production). Default port: 9545.
fractal_submitInference — Run AI inference
// Request
{
"jsonrpc": "2.0",
"method": "fractal_submitInference",
"params": {
"model_id": "0x2a00...0000",
"input": [1.0, 2.0, 3.0, 4.0]
},
"id": 1
}
// Response
{
"jsonrpc": "2.0",
"result": {
"output": [0.87, 0.13],
"model_type": "Classifier",
"gas_used": 125000
},
"id": 1
}fractal_getInferenceResult — Get async job result
// Request
{
"jsonrpc": "2.0",
"method": "fractal_getInferenceResult",
"params": { "job_id": "0xabc...123" },
"id": 2
}
// Response
{
"jsonrpc": "2.0",
"result": {
"status": "Completed",
"output": [0.87, 0.13],
"gas_used": 125000,
"execution_time_ms": 245
},
"id": 2
}fractal_listModels — List available models
// Request
{
"jsonrpc": "2.0",
"method": "fractal_listModels",
"params": {},
"id": 3
}
// Response
{
"jsonrpc": "2.0",
"result": {
"models": [
{
"id": "0x2a00...0000",
"type": "Classifier",
"input_shape": [4],
"output_shape": [2],
"total_params": 12500000,
"layers": 6
}
],
"total": 1
},
"id": 3
}fractal_getTrainingStatus — Federated training info
// Request
{
"jsonrpc": "2.0",
"method": "fractal_getTrainingStatus",
"params": {},
"id": 4
}
// Response
{
"jsonrpc": "2.0",
"result": {
"current_round": 42,
"total_participants": 7,
"total_rounds": 42,
"total_updates": 294,
"global_loss": 0.0234,
"aggregation_type": "FedPhiAvg"
},
"id": 4
}fractal_getNodeComputeStats — Node compute metrics
// Request
{
"jsonrpc": "2.0",
"method": "fractal_getNodeComputeStats",
"params": {},
"id": 5
}
// Response
{
"jsonrpc": "2.0",
"result": {
"total_tasks": 1284,
"open_tasks": 76,
"completed_tasks": 1196,
"fane_models": 3,
"sync_chunks_available": 128,
"sync_versions": 2
},
"id": 5
}Cross-Chain Integration (Solidity)
Planned integration: once deployed, the FractalAIOracle contract will let your smart contracts request AI inference across bridged EVM chains. The contract is not yet deployed and no chain addresses are published.
FractalAIOracle.sol — Request inference from Solidity
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;
interface IFractalAIOracle {
function requestInference(
bytes32 modelId,
bytes calldata input
) external payable returns (uint256 requestId);
function getResult(
uint256 requestId
) external view returns (bytes memory result, bool completed);
function isCompleted(
uint256 requestId
) external view returns (bool);
event InferenceCompleted(
uint256 indexed requestId,
bytes32 indexed modelId,
bytes result
);
}
// Example: AI-powered price prediction oracle
contract AIPriceOracle {
IFractalAIOracle public fractalOracle;
bytes32 public constant PRICE_MODEL = 0x2a00...;
constructor(address _oracle) {
fractalOracle = IFractalAIOracle(_oracle);
}
function requestPricePrediction(
bytes calldata priceHistory
) external payable returns (uint256) {
return fractalOracle.requestInference{value: msg.value}(
PRICE_MODEL,
priceHistory
);
}
function getPrediction(
uint256 requestId
) external view returns (bytes memory, bool) {
return fractalOracle.getResult(requestId);
}
}Fee: 0.16% of payment (phi-based). Minimum payment: 0.0001 ETH equivalent. Results delivered by bridge relayer (typically 1-3 minutes depending on chain).
Architecture
Inference Flow
User/Contract Bridge Relayer FractalAI Network
| | |
|-- requestInference() ------->| |
| (payment + modelId + input)| |
| |-- CrossChainAIJob ---->|
| | |-- ComputeProtocol
| | | (scheduler assigns)
| | |-- FANE Engine
| | | (runs inference)
| |<-- result -------------|
|<-- fulfillInference() -------| |
| | |
|-- getResult() ------------->| |
|<-- (output, completed) -----| |Federated Training Flow
Coordinator Node A Node B Node C
| | | |
|-- start_round() -------->| | |
|-- start_round() ---------|------------------>| |
|-- start_round() ---------|-------------------|------------------>|
| | | |
| |-- local training |-- local training |-- local training
| | (FANE engine) | (FANE engine) | (FANE engine)
| | | |
|<-- compressed gradient ---| | |
|<-- compressed gradient ---|-------------------| |
|<-- compressed gradient ---|-------------------|-------------------|
| | | |
|-- FedPhiAvg aggregate | | |
| (phi-weighted) | | |
| | | |
|-- updated weights ------->| | |
|-- updated weights ---------|------------------>| |
|-- updated weights ---------|-------------------|------------------>|Key Constants (Golden Ratio)
PHI
1.618033988749895
Revenue Split (Creator)
61.8% (1/PHI)
Bridge Fee
0.16% (phi-based)
Fee Burn Rate
61.8% of fees burned
PhiTensor Compression
16x (4-bit quantization)
Activation Function
phi-sigmoid: 1/(1+e^(-x/PHI))
Model Registration
Register models on the FractalAI Model Registry for discovery and monetization.
Register a model via RPC (conceptual)
// Models are registered through on-chain transactions
// or via the admin dashboard at /admin/ai
// Architecture types supported:
// - Transformer { num_heads, num_layers, d_model }
// - CNN { channels, kernel_sizes }
// - MLP { hidden_dims }
// - ReinforcementLearning { state_dim, action_dim }
// - Custom(name)
// Revenue split (automatic):
// - 61.8% to model creator (golden ratio)
// - 38.2% to compute nodes
// - 0.16% protocol fee (61.8% of which is burned)
// Pricing: Set by creator in FRAI tokens per inference call
// Version management: Increment via update_version()
// Accuracy tracking: Updated via training verification proofs