Agree on the Model, Verify the Inference: GKR Protocols for HND-Based Transformer Inference

Abstract:Outsourced Transformer inference exposes clients to model substitution and incomplete execution, while direct replay removes the computational benefit of delegation. We present GKR-HND, a registered-model protocol for verifying the polynomial backbone of Homomorphic--Nonhomomorphic Decomposition Transformers. The retained verifier checks the GKR transcript and registered-weight openings, but delegates expensive public evaluations to an assigned computation worker. Assuming an honest retained verifier and prover--worker non-collusion, the verifier accepts only when the worker's signed, request-bound response agrees with the proof claims. Experiments with pretrained HND models validate the proof path and the delegated public computation without dense-matrix replay.

Machine Learning (cs.LG); Cryptography and Security (cs.CR)

arXiv:2607.21162 [cs.LG]

(or arXiv:2607.21162v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.21162 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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