Verbalizable Representations Form a Global Workspace in Language Models
This paper presents evidence that large language models maintain a privileged set of internal representations—dubbed the J-space, identified via the Jacobian Lens technique—that fulfill the functional role of a global workspace analogous to conscious access in humans. Across a series of experiments on Claude Sonnet 4.5 and other models, the authors demonstrate five key properties of the J-space: (1) verbal reportability (swapping J-space vectors changes what the model says it is thinking); (2) directed modulation (instructions to hold a concept in mind cause it to appear in the J-space); (3) internal reasoning (intervening on intermediate reasoning steps redirects the model's conclusions); (4) flexible generalization (the same J-space vector serves as an argument to multiple different downstream operations); and (5) selectivity (automatic tasks like fluent text continuation do not require the J-space, while flexible reasoning and experiential reports do). Structurally, the J-space operates in an intermediate band of layers, has a capacity of roughly 25 concepts simultaneously, and is preferentially amplified and broadcast by MLP layers and specific attention heads. The paper also demonstrates practical applications: the J-space reveals hidden strategic cognition and evaluation awareness in alignment audits, and ablating it can surface otherwise concealed misaligned behaviors. Finally, the authors introduce counterfactual reflection training, a technique that shapes the J-space's content to improve model behavior without directly training on that behavior.