On Activation-Anchored Asymptotics: Finite-Size Structure and the Origin of Growth

Early-stage data in many counting problems across mathematics and physics is routinely dismissed as irregular finite-size noise, while asymptotic behavior is modeled relative to a natural origin. This paper argues that such irregularity often reflects a structurally meaningful boundary: an activation point at which the effective configuration space of the system first becomes nontrivial.

We introduce activation-anchored asymptotics, a framework in which asymptotic expansions are formulated relative to this activation boundary rather than the natural origin. Using prime knot enumeration as a primary example, we show that anchoring local growth rates at the activation crossing number yields substantially improved stability and genuine out-of-sample predictive power, extending and generalizing the predictive results established in Finite-Size Activation in Prime Knot Enumeration: A Local-Growth Perspective (Kirk, 2026).

The same structural mechanism is demonstrated independently in genus-filtered rooted maps, where activation boundaries are enforced exactly by topology, and in protein folding datasets, where activation occurs probabilistically rather than through strict combinatorial constraints. Across these domains, treating activation boundaries as structural anchors reorganizes finite-size corrections, improves convergence, and yields a unified framework for modeling growth across
discrete and physical systems.

An empirical heuristic for identifying activation boundaries in data-driven settings is provided in an appendix to support reproducible application of the framework.