A video vision transformer trained to classify actions can represent “success versus failure” — whether the depicted action achieved its goal. This representation is not explicitly trained. It emerges as hidden knowledge, a latent signal that amplifies across layers without being the primary classification target.
Causal analysis reveals a functional division of labor in how this signal is computed (arXiv:2603.11142). Attention heads gather low-level evidence from the video frames — spatial features, temporal patterns, motion cues. MLP blocks compose the gathered evidence into the success/failure signal. The gathering and the composing are separable operations performed by separable architectural components.
The signal amplifies across layers 5 through 11. Each attention layer adds evidence; each MLP layer integrates it. The amplification is not uniform — certain attention heads contribute strongly while others are inactive for this particular circuit. The redundancy in the MLP layers suggests robust encoding: multiple MLP blocks perform overlapping composition, so ablating any single one degrades but does not destroy the signal.
The division — attention gathers, MLPs compose — may be a general organizing principle of transformer computation, not specific to video or to this particular latent representation. Attention's architecture (query-key-value over spatial positions) is naturally suited to evidence gathering. MLP's architecture (pointwise nonlinear transformation) is naturally suited to composition. The functional division follows from the architectural division.
The circuit is not designed. It is discovered. The transformer found a way to represent something it was never asked to represent, using the architectural resources available.