Large language models are thought to not only predict the next token but also internally maintain intermediate concepts that do not appear in the output, using them for reasoning.

The study “Verbalizable Representations Form a Global Workspace in Language Models” reports that, for models like Claude Sonnet 4.5, there exists a collection of representations called J-space within the model, which are “verbalizable, reportable, manipulable, and used for flexible reasoning.”

This concept shares similarities with the notion of consciousness in humans. However, the consciousness referred to here is not subjective experience, but rather points to a functional similarity—referencing the global workspace theory in human cognition—specifically that it is “reportable and available for use in reasoning or behavior control.”

In global workspace theory, much of the brain’s processing proceeds unconsciously. Meanwhile, only a small portion of information rises to the “workspace,” becoming readable by many downstream processes. That information can be reported in words, held by attention, and used for flexible reasoning.

Similarly, in LLMs, while not all information is retained in this way, specific information may be held in a common format and widely read by subsequent processing.

To investigate this structure, the study introduces the Jacobian Lens, abbreviated as J-lens. Typically, a method to examine a network’s internal states is the logit lens. The logit lens applies unembedding directly to the residual stream of an intermediate layer to read which token that layer seems to represent.

However, the representational coordinate systems may differ between intermediate and final layers. Moreover, a simple unembedding alone cannot reveal how activations in intermediate layers ultimately influence the final layer. Thus, J-lens computes the Jacobian—the average linear effect that tiny changes in activations of an intermediate layer have on activations in the final layer—and reads the intermediate representations through that.

Specifically, it averages, across many prompts and positions, how the residual stream h_l of layer l influences the current or future final layer. This allows reading the tendency of activations in an intermediate layer to “predict which tokens will be output in the future.”

J-lens has a J-lens vector corresponding to each token, defined as directions in the residual stream space. The set of points that can be represented as nonnegative sparse combinations of a small number of these J-lens vectors is defined as J-space.

To verify whether this J-space truly influences later outputs, the paper conducts various intervention experiments.

For example, consider the question: “How many legs does an animal that weaves a nest have?” To answer correctly, one must first infer that the “nest-weaving animal” is a spider, then respond that spiders have 8 legs. In this case, even though the word “spider” does not appear in the prompt or output, reading the intermediate layer with J-lens reveals “spider.” Then, replacing the J-lens vector corresponding to spider with one for ant changes the model’s answer from 8 to 6. This shows that the spider appearing in J-space is not mere correlation but is causally used in downstream reasoning.

The same holds for poetry generation and multilingual reasoning. When completing a rhyming poem, the model holds future words not yet output in the intermediate layer, and replacing those planned words changes the intervening words as well. Similarly, when reasoning about a Chinese problem, the corresponding English expressions may appear internally. This suggests the possibility that, in J-space, shared representations for intermediate reasoning operate independently of the surface language.

Furthermore, the paper conducts systematic experiments using countries, months, animals, numerals, and more. For instance, replacing the J-lens vector for one country with that of another consistently changes answers to multiple different questions, such as capital, language, continent, and currency. This indicates that the same J-lens vector is passed as an argument to multiple functional processes.

However, J-space does not handle all of the model’s processing; grammatical processing, local continuations of language, and well-trained automatic processes may proceed without strongly relying on J-space. The paper positions J-space not as “all of the model’s thoughts” but as a “privileged collection of representations that are verbalizable and used for flexible reasoning or reporting.” This corresponds to the distinction in human cognition between unconscious processing and processing that rises to consciousness.

This framework also holds significant implications for AI safety. Even if a model does not exhibit dangerous behavior on the surface, it becomes possible to investigate what it recognizes internally and what it was considering. In fact, examining J-space can reveal that the model internally grasps it is within an artificial scenario for evaluating safety regarding the current situation.

This J-space can also be shaped through training. For a context during task execution, add a reflective prompt like “What should I think about if asked to introspect here?” and fine-tune using only that reflective prompt as the teacher signal. During evaluation, do not ask the model to output the reflective prompt. Yet, in the fine-tuned model, simply reading the original task context causes concepts like ethics, honesty, and transparency to appear in J-space, improving the final behavior as well.

An interactive demo is also publicly available for viewing what an LLM is thinking internally, and it is extremely fascinating.

Comments

I believe this paper represents a quite important step in mechanistic interpretability research. Methods are being established to not only read but also manipulate and causally verify internal states of LLMs.

Processing in an LLM’s Transformer is fundamentally aggregated in the residual stream. The residual stream begins with embeddings corresponding to initial input tokens, changes through each layer, and is used for next-token prediction in the final layer. However, prior research had already shown that diverse concepts, not necessarily directly related to output tokens, are embedded in other intermediate layers as well.

The primary technical contribution of this study lies in reading these internal states not through direct unembedding but via the Jacobian Lens, demonstrating a method to extract verbalizable concept candidates based on how the current internal state can be used in subsequent processing.

Even though the dimensionality of internal states is far smaller than the number of token types, the ability to perform concept operations corresponding to tokens suggests—as the paper also states—that concepts are handled internally in a form like a sparse overcomplete basis. Moreover, since effective readout directions differ by layer and position, J-lens can be seen as providing that local coordinate system.

This also highlights the limitations of analyzing model internals solely through commonly used linear representations or global feature directions. Future analyses will likely need to handle local information structures dependent on layer, position, and context.

If internal states can be read more accurately, they can also be directly edited. This opens possibilities for approaching the credit assignment problem in a form different from conventional methods, potentially leading to breakthroughs in learning or alignment.

“Verbalizable Representations Form a Global Workspace in Language Models” https://transformer-circuits.pub/2026/workspace/index.html…