Probing the Emergent Global Workspace of Reasoning Models on Structured Reasoning Tasks

Global workspace theory has become an influential lens on artificial intelligence. We ask whether a workspace-like channel, a limited-capacity state that later computation repeatedly reads and rewrites, arises on its own in reasoning models that were never given one, and if so, where it lives and what it carries. Recursive reasoning models, which refine a latent state rather than extend a token sequence, offer a revealing case, because the state that would have to be broadcast is explicit and re-entrant by construction. We use verifiable, multi-solution mathematical reasoning tasks, where a single intervention can separate whether a model produces a valid solution from which one it produces, and fit Jacobian lenses at corpus, per-question, and per-solution levels to a recursive latent reasoner and an autoregressive LLM. In the recursive reasoner, a subspace amounting to a fraction of a percent of the latent state is causally necessary across most of the trajectory, the recursion repairs single-step damage so that only sustained removal is fatal, and ablating branch-conditioned directions changes which solution is returned while largely sparing whether one is. The autoregressive model instead confines constraint enforcement to a narrow band of late decoder layers, where a single edit destroys validity and identity together. In both, the workspace is a functional role rather than a dedicated module, a thin channel inside the shared state; the recursive model spreads it over reasoning steps while the autoregressive model compresses it into a few adjacent layers, suggesting why recursive refinement can be more robust than autoregressive decoding. The protocol needs only an accessible internal state and a verifier of explicit constraints, and transfers to other models and tasks.