A Note on Non-Composability of Layerwise Approximate Verification for Neural Inference
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918343292747776 |
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| author | Zamir, Or |
| author_facet | Zamir, Or |
| contents | 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 $δ$; 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15756 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Note on Non-Composability of Layerwise Approximate Verification for Neural Inference Zamir, Or Cryptography and Security Machine Learning 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 $δ$; 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). |
| title | A Note on Non-Composability of Layerwise Approximate Verification for Neural Inference |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2602.15756 |