Does the Workspace See? Testing and Inducing a Multimodal Global Workspace in Vision‑Language Models

The Jacobian lens identifies a small, workspace-like subset of internal representations through their average influence on model outputs. A readout indexed by the output vocabulary can only return tokens, so for vision-language models we cannot tell whether visual content lies inside the subset or outside it. We call the question of whether a representation can be expressed in the symbols of a given output channel channel expressibility. Verbalizability, the property the lens reports for the subset, is the special case where that channel is text; a single-channel model cannot test it, since its readout has only that alphabet. We introduce MJ-lens, a modality-split Jacobian lens (four operators in a unified model, two in an understanding-only model) that also adds a readout into the discrete visual codebook by linearizing the image-generation head. We test whether the workspace account extends to multimodal models, using understanding-only and unified models. We find that the subspace MJ-lens selects has a causal role and its own transport geometry, but it does not satisfy all workspace signatures. In the understanding-only model, ablating this subspace impairs some visual judgments, but nameability does not predict which ones. In the unified model, the output channel changes what the subspace contains, yet overwriting a lens coordinate does not change what the model draws. Directions obtained without the lens do change it, installing a specified object only at early layers and a colour across a broad middle band, while the lens’s own coordinates move nothing. The bound is on the instrument, not the representation: the lens can read the subspace, but as a readout operator it does not provide directions that control what the model draws.