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title: "When Certificates Fail: A Unified Safety Framework for Embedded Neural Interface Models — Stuff That Spins"
description: "arXiv:2607.06630v1 Announce Type: new Abstract: Formal robustness certificates for embedded neural-interface models can pass while task accuracy collapses: at …"
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date: "2026-07-09T04:00:00+00:00"
modified: "2026-07-09T06:04:52.805506+00:00"
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# When Certificates Fail: A Unified Safety Framework for Embedded Neural Interface Models

**Source:** Unknown  
**Published:** July 9, 2026  
**Original:** https://arxiv.org/abs/2607.06630  

## On this page

- [Overview](#overview)

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## Overview

arXiv:2607.06630v1 Announce Type: new Abstract: Formal robustness certificates for embedded neural-interface models can pass while task accuracy collapses: at perturbation budget e=0.25, EEGNet classification accuracy drops by 25.7% under projected-gradient attack while the Lipschitz-style certificate remains valid for all 9 tested subjects. We argue that this gap between mathematical certification and operational safety is one instance of a broader alignment failure in neural interfaces, where 

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