TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference

TOPLOC is a novel method for verifiable inference that uses a locality sensitive hashing mechanism to detect unauthorized modifications in model computations with 100% accuracy, validated in empirical evaluations. It is designed to maintain robustness across various hardware configurations and computational settings while achieving validation speeds up to 100 times faster than the original inference. By employing a polynomial encoding scheme, TOPLOC significantly reduces memory overhead, making it practical for large-scale deployment. It ensures efficient verification of large language model (LLM) inference computations, supporting an open and distributed AI system by enabling users to trust inference providers. Unlike Zero-Knowledge proofs, which are computationally expensive, TOPLOC introduces negligible overhead and integrates seamlessly with modern inference engines, presenting a viable solution for widespread adoption. Its design focuses on larger tensor values to reduce rounding errors and improve robustness, offering a foundation to verify not only LLM inference but potentially extending to model training and multi-modal pipelines.

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