SOURCE-LINKED INTELLIGENCE

Reciprocity Separates Gradient Flow from Rotation in Conservative Physical Learning

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Physical learning lets a trainable material or network use its own physical response to carry error signals, reducing the need for a separately programmed backward computation. We ask what determines whether such a system follows conventional gradient descent or evolves along a genuinely different learning trajectory. Our canonical model is a directed layered transport network in which every node redistributes a fixed amount of flow, so learning preserves positivity and total mass. In this model, conservation constrains only the allowable learning directions. Within the matched response class

Read original source ↗ Open in workspace

recordType

paper

region

Global