mlsanityData to 7 Oct 2026

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

arXiv 2607.21162 ↗First posted 23 Jul 2026cs.LG cs.CRcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 23 Jul 2026.

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14 of 41 references.

References · 14

  1. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,581
  2. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
  3. 2020Reformer: The Efficient Transformercited by 1,658
  4. 2020Rethinking Attention with Performerscited by 1,584
  5. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
  6. 2020Linformer: Self-Attention with Linear Complexitycited by 1,462
  7. 2016Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activationscited by 413
  8. 2024A Review on Edge Large Language Models: Design, Execution, and Applicationscited by 98
  9. 2024zkLLM: Zero Knowledge Proofs for Large Language Modelscited by 51
  10. 2017SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloudcited by 50
  11. 2019VeriML: Enabling Integrity Assurances and Fair Payments for Machine Learning as a Servicecited by 26
  12. 2013Time-Optimal Interactive Proofs for Circuit Evaluationcited by 14
2 more
  1. 2019DEEP-FRI: Sampling outside the box improves soundnesscited by 1
  2. 2019Flow: Separating Consensus and Compute -- Execution Verificationcited by 1

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