TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference
Jack Min Ong, Matthew Di Ferrante, Aaron Pazdera, Ryan Garner, Sami Jaghouar, Manveer Basra, Max Ryabinin, Johannes Hagemann

TL;DR
TOPLOC introduces a hashing-based verification method for LLM inference, enabling trustless, efficient, and accurate validation of model integrity and computations across diverse hardware configurations.
Contribution
It presents a novel locality-sensitive hashing scheme for verifiable inference that significantly reduces proof size and ensures accurate detection of unauthorized modifications.
Findings
Detects unauthorized modifications with 100% accuracy
Reduces proof size by 1000x, requiring only 258 bytes per 32 tokens
Works robustly across hardware and model variations
Abstract
Large language models (LLMs) have proven to be very capable, but access to frontier models currently relies on inference providers. This introduces trust challenges: how can we be sure that the provider is using the model configuration they claim? We propose TOPLOC, a novel method for verifiable inference that addresses this problem. TOPLOC leverages a compact locality-sensitive hashing mechanism for intermediate activations, which can detect unauthorized modifications to models, prompts, or precision with 100% accuracy, achieving no false positives or negatives in our empirical evaluations. Our approach is robust across diverse hardware configurations, GPU types, and algebraic reorderings, which allows for validation speeds significantly faster than the original inference. By introducing a polynomial encoding scheme, TOPLOC minimizes the memory overhead of the generated proofs by…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsCryptography and Data Security · Cloud Data Security Solutions · Privacy-Preserving Technologies in Data
