LOGIC: Trustless Inference through Log-Probability Verification

LOGIC is a method designed to ensure trust in decentralized GPU networks by verifying that GPU operators are using the models they claim to be running. It leverages token-level log-probabilities and statistical testing to detect dishonest operators with high accuracy, even in permissionless environments where economic incentives might lead to the use of smaller or quantized models. LOGIC's approach focuses on verifying the statistical distribution of a model's outputs rather than recreating exact sequences, which helps in detecting decode-time spoofing attacks—a major vulnerability in many existing verification systems. The method requires no modifications to existing inference engines and integrates seamlessly with OpenAI-style APIs, making it deployable in various production environments. Through its efficient compression and verification processes, LOGIC achieves minimal overhead, making it suitable for large-scale deployment across heterogeneous GPU networks. Extensive testing on diverse prompts demonstrated LOGIC's ability to reliably differentiate between honest and dishonest operators, highlighting its potential as a robust solution for ensuring trust in decentralized AI inference networks.

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