Transformer Inference Proof via Gauge Canonicalization
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Solution Overview
Problem
Current zero-knowledge proof systems for transformer models suffer from prohibitive computational costs that scale linearly with the number of model parameters, making verification of large language models economically infeasible for production deployment, and fail to recognize or exploit the inherent mathematical redundancies within transformer architectures.
Innovation Solution
A two-stage approach, called GaugeZKP, that separates concerns between parameter equivalence and inference correctness, using Proof of Gauge Equivalence (PoGE) to demonstrate functional equivalence to a canonical form and Proof of Verifiable Inference (PoVI) to verify computation, eliminating redundant parameters and algebraic redundancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional zero-knowledge proof systems are used for transformer models, then verification correctness is ensured, but computational cost increases linearly with model parameters making it economically infeasible
Solution Approach 1:
The patent divides the verification process into two independent stages: (1) proving parameter equivalence to a canonical form, and (2) proving inference correctness on the canonical form. This segmentation allows the expensive verification task to be broken down into manageable parts, where the first stage is performed once during model deployment and the second stage is performed efficiently during inference, thus reducing overall computational cost while maintaining verification reliability.
Solution Approach 2:
The patent performs preliminary canonicalization of model parameters before verification. By transforming parameters to a canonical form and proving equivalence in advance (during model deployment), the system eliminates redundant parameters before the actual inference verification. This preliminary action reduces the complexity of subsequent verification steps, making the overall process economically feasible while ensuring correctness.
2Measurement precision
If full parameter verification is performed, then complete verification accuracy is achieved, but computational redundancy increases due to inherent mathematical redundancies in transformer architectures
Solution Approach 1:
The patent extracts and eliminates redundant parameters from the transformer model by transforming parameters to a canonical form. This extraction process removes mathematical redundancies inherent in transformer architectures (such as equivalent parameter transformations that produce identical outputs) while preserving the essential computational logic. The result is a streamlined verification process that maintains complete verification accuracy with reduced computational redundancy.
Solution Approach 2:
The patent changes the parameter representation by introducing a canonical form and proving equivalence between original and canonical parameters. This parameter transformation allows the system to work with a reduced set of unique parameters during verification while mathematically guaranteeing that the transformation preserves computational equivalence. This approach maintains verification accuracy while eliminating redundant computations arising from parameter symmetries.
Data Source
AI summary
One or more inference processes may be performed in the deployment of a transformer model. For proving correctness of a transformer model inference, a ZKP may be generated in two stages. In the first stage, PoGE may be generated by canonicalizing deployed weights of the transformer model through gauge transformation to produce canonical weights. A canonical model may be generated by modifying the transformer model with the canonical weights. In the second stage, PoVI may be generated. The canonical model may be executed to generate an output from an input. The output of the canonical model may be bit-identical as the output of the transformer model for the same input despite the weight canonicalization. The ZKP for the transformer model inference may include the PoGE and PoVI. The PoGE may be generated once and used for many inference processes. The PoVI may be generated per inference.