Data-Based Clustering and Control of Similar Biological Systems

What the math gives to ML

The paper provides a constructive mechanism for scaling control across many heterogeneous systems: represent each system by a finite-horizon input-output behavior subspace, cluster systems using the operator-norm gap between orthogonal projectors, and deploy one leader controller per cluster. The transferable asset is not merely clustering, but a control-relevant similarity metric with a geometric well-posedness interpretation: small graph-subspace gaps imply that a controller validated on a leader is less likely to become ill-posed on its followers. In neural networks, the same construction can cluster recurrent states, SSM layers, or MoE experts by their finite-horizon input-response subspaces and share an optimizer, adapter, or controller within each cluster. The key falsifiable prediction is that performance degradation and instability increase sharply when the within-cluster subspace gap crosses a measurable threshold.