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arXiv · 2602.15756

A Note on Non-Composability of Layerwise Approximate Verification for Neural Inference

Or Zamir
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Abstract

A natural and informal approach to verifiable (or zero-knowledge) ML inference over floating-point data is: ``prove that each layer was computed correctly up to tolerance $\delta$; therefore the final output is a reasonable inference result''. This short note gives a simple counterexample showing that this inference is false in general: for any neural network, we can construct a functionally equivalent network for which adversarially chosen approximation-magnitude errors in individual layer computations suffice to steer the final output arbitrarily (within a prescribed bounded range).

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Or Zamir. 2026-02-17. A Note on Non-Composability of Layerwise Approximate Verification for Neural Inference. https://arxiv.org/abs/2602.15756

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