---
title: "Empirical Minimal-Realisation Compression of Deep Neural Networks via Controllability-Observability Tests — Stuff That Spins"
description: "arXiv:2607.05457v1 Announce Type: new Abstract: Deep neural networks often contain substantial hidden-state redundancy, but most compression methods operate di…"
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date: "2026-07-08T04:00:00+00:00"
modified: "2026-07-08T06:04:14.794965+00:00"
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# Empirical Minimal-Realisation Compression of Deep Neural Networks via Controllability-Observability Tests

**Source:** Unknown  
**Published:** July 8, 2026  
**Original:** https://arxiv.org/abs/2607.05457  

## On this page

- [Overview](#overview)

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## Overview

arXiv:2607.05457v1 Announce Type: new Abstract: Deep neural networks often contain substantial hidden-state redundancy, but most compression methods operate directly on weights, neurons, or quantised representations without explicitly characterising the dynamical role of internal states. This paper proposes a controllability-observability framework for empirical state-order reduction of deep neural networks. By viewing a trained network as a depth-indexed nonlinear dynamical system, we construct

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