Jamming transition in an active exclusion process

What the math gives to ML

The paper provides a constructive nonequilibrium mechanism in which self-propulsion reversals suppress or shift a density-driven jamming transition in a hard-core exclusion process. Its transferable asset is the combination of congestion-dependent motion, a tunable reversal rate, and an observable phase boundary separating mobile and jammed regimes. A direct neural-network application is capacity-constrained mixture-of-experts routing: tokens move between available expert slots with rates that increase when the forward vacancy cluster is short, while route directions are randomly reversed to prevent persistent congestion. The key falsifiable prediction is that routing mobility and cluster statistics exhibit a sharp transition as token density and reversal rate are varied, with sufficiently rapid reversals suppressing persistent expert jams.