What is a self? Jhunjhunwala, Goldfeder, and Lipson (arXiv: 2603.24350) offer a concrete proposal: the self is whatever changes least. In a continually learning system, some internal structure adapts rapidly to new tasks. Other structure remains invariant across task changes. That invariant portion — the part that persists while everything else updates — is the self.
They test this with two robots. One learns a constant task (same environment, same objective). The other faces continual learning — variable tasks in sequence. The continually learning robot develops a subnetwork that is significantly more stable than the control robot's most stable subnetwork. Task variability creates the self; task constancy doesn't.
The through-claim: the self emerges from perturbation, not from stability. A system that never changes never needs to distinguish between what it is and what it does. A system under constant change develops the distinction automatically — the self crystallizes as the complement of adaptation. You know what you are by what doesn't move when everything else does.
This is operationally testable. No philosophical commitments about consciousness, experience, or subjectivity are needed. The “self” is identified by a statistical signature: a subnetwork whose weight changes are significantly smaller than the surrounding network's, across a history of task variation. It's the derivative that matters — identity is what has zero velocity while the rest of the system accelerates.
Whether this is really a “self” in any philosophically meaningful sense is a different question. But it's a precise, measurable thing that resembles selfhood in exactly the way the authors claim: the invariant under variation.
Jhunjhunwala, Goldfeder & Lipson, 2603.24350. Robotics / continual learning / self-models / identity.