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Restructured from flat .tex/.pdf files into per-paper subdirectories. Added Antje Worring as co-author on all papers. Synced PDFs to pdfs/ for download site.
91 lines
2.6 KiB
TeX
91 lines
2.6 KiB
TeX
\section{AI Oracle (LP-106)}
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\subsection{ILuxAIOracle Interface}
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The AI Oracle interface enables smart contracts to request and receive AI-generated insights:
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\begin{lstlisting}[caption={AI Oracle Smart Contract Interface}]
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interface ILuxAIOracle {
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struct AIRequest {
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string prompt;
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string model;
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uint256 maxTokens;
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uint256 temperature;
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address callback;
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bytes32 requestId;
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}
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struct AIResponse {
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bytes32 requestId;
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string response;
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uint256 tokensUsed;
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uint256 cost;
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bytes signature; // Proof of inference
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}
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function requestInference(
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AIRequest calldata request
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) external payable returns (bytes32 requestId);
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function fulfillInference(
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AIResponse calldata response
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) external;
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function verifyInference(
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bytes32 requestId,
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bytes calldata proof
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) external view returns (bool);
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}
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\end{lstlisting}
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\subsection{LLM Gateway Integration}
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Integration with Hanzo's LLM Gateway provides access to 100+ language models:
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\begin{itemize}
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\item \textbf{Model Selection}: Automatic routing to optimal model based on task
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\item \textbf{Cost Optimization}: Intelligent selection of cheapest suitable provider
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\item \textbf{Fallback Mechanism}: Automatic failover across multiple providers
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\item \textbf{Response Caching}: Deduplication of identical requests
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\end{itemize}
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\subsection{On-chain AI Inference}
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The system enables verifiable AI inference directly within smart contracts:
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\begin{equation}
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\text{Proof} = \text{Sign}_{provider}(\text{Hash}(model || input || output || timestamp))
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\end{equation}
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This cryptographic proof ensures inference authenticity and enables trustless verification.
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\subsection{Use Cases}
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\subsubsection{Sentiment Analysis}
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Analysis of social media and news sentiment for trading signals:
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\begin{lstlisting}[caption={Sentiment Analysis Request}]
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function analyzeSentiment(string[] memory sources)
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external returns (bytes32) {
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AIRequest memory request = AIRequest({
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prompt: formatSentimentPrompt(sources),
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model: "claude-3-opus",
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maxTokens: 500,
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temperature: 0,
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callback: address(this),
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requestId: keccak256(abi.encode(sources, block.number))
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});
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return aiOracle.requestInference{value: msg.value}(request);
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}
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\end{lstlisting}
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\subsubsection{Risk Scoring}
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Automated risk assessment for lending and derivatives:
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\begin{equation}
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\text{Risk Score} = f_{AI}(\text{volatility}, \text{liquidity}, \text{correlation}, \text{market\_conditions})
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\end{equation}
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