For years the convenient explanation was modular: in transformers, MLP blocks memorize and attention generalizes. The paper "Where Grokking Happens" dismantles this reading with precise experiments: Transition Games, behavior-aligned exact activation games with paired non-generalizing controls. The result is a distributed utility gain, with a prospective block-0 attention bias. Selected degree-two modes account for 67 to 92 percent of the addition contrast in replacement games; the block-1 MLP mediates more of their effect than all other tested downstream paths in 12 out of 12 pairs. The sharp "MLP memorizes, attention generalizes" prediction fails: at the memory anchor the signal is -0.331 bits per example, and none of the 12 pairs goes in the predicted direction. Routing onset, global rank collapse, and a prime-invariant architecture ridge also fail.

For anyone training or adapting LLMs on local infrastructure, this is not an academic question. Grokking does not come from a module switch, but from spectral recoding of an existing distributed circuit. It means that monitoring tools built around single layers risk looking for the change in the wrong place. If the signal is carried by degree-two Fourier components and mediated by block 1, a team doing fine-tuning on proprietary data needs to track distributed activations along the pipeline, not just weights or final loss. This shifts value from modular attributions to frameworks able to record and compare intermediate representations.

The thesis is that modular explanation was a convenient heuristic, not a description of the mechanism. For interpretability vendors, the risk is offering maps that point to one block while generalization lives elsewhere. For teams operating self-hosted deployments, the concrete advantage is the opposite: if the circuit is distributed, you need finer control over execution, but also a less fragile reading of checkpoints. Local infrastructure with activation-recording capability can provide visibility that cloud limits by cost or access to training data.

The finding is not a curiosity: it redefines where to look when a local model stops memorizing and starts generalizing. And it suggests that the next generation of inspection tools will need to work on spectra and distributed circuits, not on separate modules.