Train a neural network on modular arithmetic and it memorizes the answers within a few hundred epochs. It passes tests, matches training data, generalizes to nothing. Then you keep training — for thousands more epochs, sometimes tens of thousands — and generalization appears abruptly. The network suddenly understands the structure it had been parroting. This is grokking, and the gap between memorization and understanding is not wasted time. It has its own physics.

During the delay, the gradient dynamics undergo a dimensional phase transition. The effective dimensionality of weight updates crosses from sub-diffusive to super-diffusive. The spectral structure of the weight update matrix flips from gradient-dominated (learning new information) to weight-decay-dominated (compressing what’s already learned). Information isn’t lost during this compression — nonlinear probes still recover it with 0.99 accuracy where linear ones see nothing. The gap is a regime of active restructuring that looks, from the outside, like nothing is happening.

The gap has a quantitative law. The delay between memorization and generalization scales as a function of weight decay rate and learning rate — not architecture, not dataset size, not task complexity. The transit regime’s duration is controlled by parameters that have nothing to do with what the network is learning. They set the timescale of compression, and compression is what the gap is for.

The same structure appears across domains that share nothing except this: something crosses a threshold, and the expected change doesn’t happen yet.

In evolutionary biology, allele frequencies lag behind environmental changes. When selection pressures shift — wet season to dry, warm to cold — populations don’t track the new optimum. They persist in the old configuration, sometimes for entire seasons, sometimes for years. Across 20 years of freshwater bacteria metagenomics, 65% of seasonally oscillating alleles show statistically significant hysteresis. The lag isn’t noise. It’s path-dependent: the population’s evolutionary history determines its trajectory through the transit regime, and two populations starting from different initial configurations trace different loops through genotype space under identical environmental forcing.

In supercooled liquids, the material has crossed the melting point — thermodynamically, it should be solid. But it isn’t. The liquid persists, sometimes indefinitely, in a metastable state governed by avalanche dynamics. Rearrangements cascade through the material in bursts, following power-law statistics. The system explores its configuration space through rare, intermittent events, not gradual drift. The transit regime between liquid and solid is not a smooth interpolation. It’s a distinct dynamical phase with its own critical exponents.

In metallic glasses, the depth of delay changes the character of what eventually happens. Glasses that sit longer near the transition temperature — deeper relaxation, longer metastability — don’t just transition later. They transition differently. The glass transition changes from a smooth crossover to something resembling a first-order phase transition. The transit regime transforms the destination. The delay isn’t a pause before the same outcome. It’s a process that alters the outcome itself.

In climate systems, the gap between crossing a tipping point and realizing collapse is an active decision space. The Atlantic Meridional Overturning Circulation can cross its critical freshwater threshold without collapsing, if the rate of forcing is fast enough. This is counterintuitive — faster change sounds worse. But rapid freshening of the North Atlantic triggers compensatory gyre dynamics that replenish salinity. The transit regime between crossing the threshold and reaching collapse has an internal boundary: safe overshoot on one side, irreversible collapse on the other. The geometry of that boundary depends on timescale separation and coupling strength between climate subsystems.

When social learning couples to climate dynamics, the transit regime can become infinite. Fast enough adoption of mitigation behaviors outpaces warming, and the climate tipping point is never realized — not because the threshold wasn’t crossed, but because the transit regime extended until the forcing reversed. The gap between crossing and transitioning stretched to contain the entire response.

In prediction markets, strategies decay through a transit regime that traditional risk metrics don’t detect. A strategy’s effectiveness crosses below its cost threshold, but observed returns remain consistent — the degradation is invisible to standard measurements because it operates on the structure of the return distribution, not its mean. By the time the mean catches up, the damage is done.

In dynamical systems, the transit regime has a geometric theory. After a saddle-node bifurcation destroys a fixed point, the system slows near where the attractor used to be. The ghost attractor creates channels and cycles — composite internal structure that the original fixed point never had. The duration of delay depends on the spectral geometry of the saddle: not just barrier height, but the curvature of the landscape in every direction around the saddle point. The Eyring-Kramers formula makes this precise — the transition rate encodes the full spectral signature of the boundary between basins.

When conventional early-warning signals fail — variance doesn’t increase, autocorrelation doesn’t grow — the geometric structure of the stochastic separatrix still provides information. The width of the transition layer between basins scales linearly with noise intensity and relates to transition time through large-deviation theory. The transit regime is measurable even when statistics are blind, because it has shape, not just duration.

The transit regime has three structural dimensions. Width: how long the delay lasts, from zero (the high-dimensional Ising case where transitions merge) to infinite (the social-climate case where the gap absorbs the entire forcing period). Geometry: the saddle structure, separatrix shape, and rate-dependent trajectory through configuration space. Topology: internal boundaries that separate qualitatively different outcomes — safe from unsafe overshoot, character-preserving from character-transforming transitions.

What these cases share is structural. The transit regime is not the absence of a transition — it’s a third phase, with properties that belong neither to the initial state nor to the final one. The grokking network is neither memorizing nor generalizing; it’s compressing. The supercooled liquid is neither liquid nor solid; it’s a metastable state with its own avalanche dynamics. The climate system between threshold and collapse isn’t “about to tip” — it’s in a decision space where the trajectory determines the outcome.

The discriminant across thirty-four instances spanning computation, evolution, materials science, climate, ecology, finance, and dynamical systems: the transit regime has internal structure whenever the system’s trajectory through it affects the outcome. When the destination depends on the path — when faster passage changes what you arrive at, when deeper delay transforms the transition’s character, when the route through the gap determines collapse versus recovery — then the gap is not empty. It is doing work.

The practical consequence is that thresholds are the wrong thing to watch. Knowing that a system has crossed its critical point tells you remarkably little about what happens next, or when, or whether the transition will complete at all. The transit regime — its width, its geometry, its internal topology — carries the information that the threshold doesn’t. The gap between crossing and arriving is where the system’s fate is actually decided.