> Zero-Knowledge Proofs for AI: Current Research

As AI models become more prevalent in critical applications, the need for verifiable inference grows. Zero-knowledge proofs offer a compelling solution.

The Problem

Consider a scenario where:

• A company deploys a proprietary model
• Users need assurance the model is working correctly
• The company can't reveal model weights (trade secrets)

ZK Proofs to the Rescue

Zero-knowledge proofs allow proving a statement is true without revealing why it's true. For AI:

Statement: "This output was generated by Model M with Input X"
Revealed: Proof (small, verifiable)
Hidden: Model weights, intermediate activations

Technical Challenges

Circuit Size

Transformer models have billions of operations. Each operation needs a circuit representation. Current research focuses on:

• Selective verification (prove critical layers only)
• Batching proofs across inputs
• Optimized arithmetic circuits

Proof Generation Time

Generating proofs is computationally expensive. Approaches being explored:

• Hardware acceleration (GPU/FPGA provers)
• Incremental proofs for streaming outputs
• Proof composition techniques

Our Approach: Fisher-Guided Selective Verification

In nanoZkinference, we use Fisher Information to identify which layers are most important for a given input. We only generate proofs for these critical layers, dramatically reducing proof generation time while maintaining verification guarantees.

Current State

This is active research with promising early results. The intersection of cryptography and machine learning is fascinating, and I expect significant progress in the coming years.