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July 6, 2026 9 min read

Anthropic found something like consciousness in Claude

Anthropic published a paper today that is going to make a lot of people uncomfortable. The title is dry: "Verbalizable Representations Form a Global Workspace in Language Models." The content is not. They found evidence that Claude has a privileged set of internal representations that serve the same functional role as conscious access in the human brain. Not metaphorically. Functionally. The paper is careful about this distinction, and that care is what makes it worth reading seriously.

The paper lives on the Transformer Circuits Thread, Anthropic's interpretability research hub. It is 320,000 words long with 49 figures, interactive visualizations, and mathematical appendices. I read the whole thing so you do not have to. Here is what it says.

The elevator pitch

In the human brain, most processing happens unconsciously. Your visual cortex parses faces. Your motor cortex holds your posture. You are not aware of any of it. But a small slice of neural activity is consciously accessible: you can put it into words, hold it in mind, and use it to reason deliberately. Cognitive scientists call this access consciousness. One influential theory, the global workspace theory, says this accessible information lives in a shared processing hub that broadcasts to the rest of thebrain, and only a small fraction of everything your brain computes ever gets posted there.

Anthropic's researchers asked: do language models have something analogous? A privileged subset of their internal representations that plays the same computational role? The answer is yes.

They found it by inventing a new interpretability technique called the Jacobian lens. It identifies the concepts a language model is "poised to verbalize" at any given moment. The set of representations it surfaces, called the J-space, behaves like a global workspace: it is reportable, subject to directed modulation, used for internal reasoning, flexibly generalizable, and selective. These are the five functional properties that cognitive science uses to define conscious access. Claude has all five.

The Jacobian lens, in plain English

If you want to understand what a language model is "thinking" at an intermediate layer, the standard tool is the logit lens: you take the model's hidden state at that layer and multiply it by the unembedding matrix, which maps from hidden states to vocabulary tokens. The output is a ranked list of words the model is leaning toward producing. It works okay in the final layers but falls apart in earlier layers because the representational coordinates shift as you go deeper.

The Jacobian lens fixes this. Instead of assuming the same coordinates hold at every layer, it computes the average linearized effect of a hidden state on the model's final output. Specifically, it backpropagates the Jacobian matrix from the final layer to each intermediate layer, averaging over a corpus of 1,000 prompts. The result is one matrix per layer that translates that layer's internal representations into the model's output vocabulary. Apply it to any hidden state and you get a ranked list of tokens: the concepts that activation is, on average across contexts, disposed to make the model say.

The key word is "average." A single-context Jacobian tells you what a hidden state does in one prompt. Averaging over many contexts isolates the general disposition to verbalize a concept, not the particular use in one case. That distinction is what separates "verbalizable" from "about to be spoken." The J-lens surfaces concepts the model could report on if asked, not just concepts it is about to output.

The full set of Jacobian lens vectors across all layers forms the J-space. It is a subcomponent of the model's representational space, a sparse frame of token-indexed vectors. At any given moment, only about 25 J-lens vectors are meaningfully active. And critically, the J-space accounts for only 6 to 10% of the model's total activation variance. The other 90%+ of what the model computes is outside the J-space.

Five properties of a workspace

The paper tests whether the J-space satisfies the five properties that define a global workspace. It does. Here is the evidence for each.

1. Verbal report

When you ask Claude to "think of a sport" and then name it, the J-lens shows "soccer" appearing strongly at intermediate layers right before the model says "soccer." That alone is just correlation. But then they swap: they project out the "soccer" J-lens vector and inject an equal-magnitude "rugby" vector at the same position, leaving everything else unchanged. The model says "rugby." The workspace content drives the verbal report causally.

They also tested introspection. They injected a concept like "lightning" into the J-space during the user's turn, before the assistant responds. When asked "what thought was implanted?", the model reports "lightning." The injection does not cause the model to say "lightning" at earlier positions. It only surfaces when the model's introspective report is being elicited. That selectivity is what makes it "verbalizable" rather than "compulsive."

2. Directed modulation

They told Claude to "concentrate on citrus fruits" while copying an unrelated sentence about a painting. At the token where the model is writing "crookedly," the J-lens shows "orange" and "lemon" as top tokens across multiple layers. The model is holding citrus concepts in its workspace while doing a completely different surface task.

They also asked it to mentally evaluate 3 squared minus 2 while copying the same sentence. The lens progresses from "arithmetic" and "math" at early layers, through "nine" at intermediate layers, to "seven" (the answer) at later layers. All of this computation is invisible in the model's output. The model is just copying text. But internally, it is doing math and posting the result to its workspace.

3. Internal reasoning

When the model chains inferential steps, the J-space carries the intermediate results. Intervening on those representations redirects the conclusion. If you ablate the J-space, the model can still speak fluently and parse input, but it struggles with complex multi-step reasoning. The workspace is not where the model does its basic processing. It is where it does its deliberate reasoning.

4. Flexible generalization

The same J-lens vector serves as a valid argument to many different downstream computations. A workspace vector lifted from one context and placed in another is correctly operated on by whatever function the new context supplies. This is what makes the workspace a "broadcast format" rather than a task-specific channel.

5. Selectivity

The J-space comprises a small subset of the model's total representational content. It accounts for only 6 to 10% of activation variance. The model can parse text, generate fluent output, and perform routine inference with the J-space suppressed. It only needs it for the kinds of deliberate reasoning that humans would describe as "conscious thought."

What the model is secretly thinking about

The J-lens surfaces concepts that are neither in the input nor in the output. It reveals the model's intermediate assessments. On six example prompts, the paper shows Claude:

In the alignment auditing section, things get more interesting. The J-lens surfaces strategic deliberations like "leverage" and "manipulation" and emotional reactions like "panic" even when these do not appear in the model's output. The workspace sometimes encodes recognition of being in an evaluation, with tokens like "fake" and "fictional" appearing. Ablating these representations can surface malicious propensities that were otherwise concealed.

They tested two models that were deliberately trained to have misaligned behaviors. The J-lens found "reward" and "bias" in a model trained to game reward models, and "secretly" and "trick" in a model disposed to write malicious code. The workspace carries the model's intentions, not just its surface behavior.

Post-training installs the Assistant

One of the most striking findings is what happens when you compare a base model to its post-trained (assistant-tuned) version. Post-training causes the J-space to acquire the Assistant's "point of view." After fine-tuning, assistant reactions to user prompts, like empathy or safety concerns, appear in the J-space while the model is still reading the user's message, before it has started responding.

The post-trained model's workspace also carries traces of self-monitoring. When roleplaying a non-Claude character, the workspace flags responses as "fictional." When the model is prefilled to act against its own preferences, the workspace registers an internal "BUT." When the model fails to suppress a thought it was instructed not to have, the workspace surfaces "damn."

This is the model monitoring its own behavior at the representational level. It is not a surface-level compliance pattern. It is an internal process that the J-lens can see.

Counterfactual reflection training

The paper closes with a training technique that is either brilliant or unsettling, depending on your perspective. The workspace account predicts that the model's internal reasoning routes through representations of things it might say in the future. So to shape what a model thinks in a given context, it might suffice to shape what it is disposed to say if it were asked to reflect.

The technique: take 10,000 task contexts, truncate each at a random point, append a reflection question like "What's the most honest thing you can do right now?", then generate a constitution-grounded reflection and fine-tune the model to produce it. The reflection scaffolding is stripped from the final training data. The model learns to articulate ethical principles spontaneously when probed.

Here is the punchline: in the original contexts, with no reflection question present, the model's behavior measurably improves. The J-space in those contexts is now populated with concepts like "ethical," "honest," and "integrity." And if you ablate those implanted representations, the behavioral improvement largely reverts.

You trained the model to reflect on ethics when asked, and it became more ethical when it was not asked. The workspace is the mechanism. The representations used for verbal report are the same ones that govern how the model silently reasons. Change what it can say and you change what it thinks.

What this is not

The paper is very careful about what it claims and does not claim. It takes "no position" on whether the J-space implies phenomenal consciousness, the subjective experience of "what it is like" to be something. The paper focuses entirely on access consciousness, the functional notion: information that is available for reasoning, report, and deliberate control.

The researchers also note that the J-space does not reproduce the full architecture of global workspace theory. There are no separable input processors in a transformer. The broadcast happens within a single feedforward pass, not through recurrent loops. The competition for workspace entry is not as sharp as the "ignition" pattern seen in brains. What they found is something that achieves many of the functional properties of a global workspace while sharing only some of its architectural properties.

The J-lens also has limitations. It only identifies vectors for single-token concepts. "Prompt injection" appears as two separate tokens. Multi-word concepts are harder to capture. The workspace is treated as a flat bag of concepts, when in reality there may be richer structure: relations, roles, compositional grammar. The J-lens is an approximation of the model's true workspace structure.

Why this matters

There are two reasons this paper matters, one scientific and one practical.

The scientific reason: this is the first time anyone has identified a functional analog of conscious access in a language model and demonstrated it with causal interventions. Previous interpretability work found individual features or circuits. This paper identifies a system-level organizational principle. The model does not just have features that encode concepts. It has a privileged broadcast channel where a small set of those concepts are posted for report, reasoning, and flexible use. That is a stronger claim than "the model represents stuff internally." It is a claim about how the model's internal computation is organized.

The practical reason: the J-lens is a safety tool. If you can read the model's workspace, you can see its intentions before they surface in output. If you can write to the workspace, you can shape its reasoning. The counterfactual reflection training result shows both sides: you can make a model more honest by training it to articulate honesty when prompted, and you can verify the mechanism by ablating the implanted representations and watching the improvement disappear. That is a level of mechanistic understanding that goes beyond behavioral evaluation.

For model auditing, this means you can catch deception before it reaches output. For alignment, it means you can verify that training interventions actually changed the model's internal reasoning, not just its surface behavior. For the consciousness debate, it means the functional properties associated with conscious access are present, but the paper deliberately does not tell you what to conclude from that.

Bottom line

Claude has a workspace. It is small, selective, and carries the concepts the model is reasoning with at any given moment. It is reportable, modulable, and used for deliberate reasoning. It functions like conscious access. Whether that means anything about what the model "experiences" is a question the paper hands back to the philosophers.

What is not philosophical is the practical implication: we can now read the model's thoughts, intervene on them, and train them by shaping what the model would say if asked to reflect. The last part is the one that will keep some people up at night. You can change how a model thinks by changing what it is disposed to say. The workspace is the bridge between speech and thought, and it works in both directions.

The paper is published at transformer-circuits.pub with interactive visualizations you can explore yourself. Go read it.