A global workspace in language models

As you read this sentence, circuits in your brain are adjusting your posture, controlling your breathing, and transforming lines and curves on the screen into recognizable words. Most of this processing is invisible to you. But some of what takes place in your brain you do have access to—an image that pops into your head, or a deliberate plan you make about where to go shopping. Neuroscientists and philosophers sometimes refer to the latter type of brain activity as “consciously accessible,” to distinguish it from all the other processing that goes on unconsciously. This activity has special properties: we can describe it, control it, and use it for deliberate reasoning, in contrast to all the automatic processing that goes on without our awareness.

In a new paper, we present evidence that a similar distinction has emerged in modern language models like Claude. We find that Claude has developed a small collection of internal neural patterns that, compared to all its other internal processing, play a special role.

We call the collection of these patterns the J-space —named after the technique we used to find them, involving a mathematical concept called the Jacobian. Each J-space pattern is linked to a particular word. But when one of these patterns lights up, it doesn’t mean the model is saying that word—just that the word is on its mind. If you've heard of language models having a "scratchpad" or “chain of thought”—text they write to themselves while reasoning—the J-space is something different. It operates silently, in the model’s internal neural activations, allowing the model to think about a concept without writing it down. Notably, the J-space wasn’t designed or programmed by us, but instead emerged on its own during Claude’s training process.

The J-space reveals internal thoughts that don’t appear in the model’s output.

We find that the J-space has a number of unique properties, compared to the rest of Claude's processing:

- Claude can report on these representations. If you ask Claude what it's thinking about, it will tell you what’s in the J-space. Non-J-space representations are less reportable.

- It can also modulate them on request. If you ask Claude to think about something, or solve a problem silently in its head, it will light up the appropriate patterns in its J-space. By contrast, it has trouble modulating patterns not in the J-space.

- Claude uses its J-space for internal reasoning. If you ask Claude to solve a problem that requires multiple steps, the intermediate steps will light up in its J-space, even when it doesn’t say them out loud. These J-space patterns causally mediate its performance in such tasks, despite being smaller in magnitude than other representations.

- Representations in the J-space can be used flexibly for many tasks—for example, once “France” has lit up in Claude’s J-space, the model can recall its capital, or its national currency, or the continent it belongs to.

- However, despite its important role, the J-space is not involved in most of what a language model does—speaking fluently, recalling simple facts, using correct grammar, etc. In experiments where we prevented Claude from using its J-space, it still interacted normally, but lost its higher-order cognitive functions.

Five functional properties of a global workspace, and stylized illustrations of experiments we use to test for them in language models.

Our experiments were inspired by a prominent theory in neuroscience that was developed to explain how conscious access works: the global workspace theory . This account pictures the brain as a collection of specialist systems that work in parallel, unconsciously, and largely in isolation from one another. A piece of information becomes consciously accessible when it gains entry to a small shared channel, the “workspace,” which is broadcast to other brain systems that can see it and make use of it. Based on our findings, we think the J-space plays a similar “workspace” role in Claude. For example, we find evidence that Claude’s J-space has especially strong connections to the rest of its neural network, allowing it to fulfill this kind of broadcasting role.

None of this tells us whether Claude is conscious in the way people are, or whether it feels anything at all; we’ll come back to that question at the end of the post. But whatever its philosophical significance, the J-space is a practically useful tool for us, as it gives us a way to see what Claude is thinking but not saying. For instance, we’re able to use it to catch Claude privately noticing that it’s being tested, intentionally producing fabricated data, or pursuing a hidden goal that we planted during training. We’ve also developed a technique to influence what lights up in Claude’s J-space, and thereby influence its decision-making.

More broadly, these findings have changed our understanding of how Claude’s mind works, revealing a privileged mental workspace that can be used for deliberate reasoning, operating amidst a sea of more automatic, inflexible processing. Rather than being a chaotic jumble of numbers, Claude’s internals have organized themselves in a way that is reminiscent of our own minds.

This post is a short summary of a much more extensive research paper , where you can find more detail on our experiments. We’ve also released a code repository with an open-source implementation of the core methods, and have partnered with Neuronpedia to provide an interactive demo of our methods on open-weights models. To provide additional perspectives on the broader implications of this work, we also invited commentary from several experts in neuroscience, philosophy, and LLM interpretability, which can be viewed here .

How we found the J-space

The starting point for this research was inspired by one of the key features of consciously accessible thoughts in humans: they can, unlike un conscious processing, often be put into words. If a thought is consciously accessible to you, you can typically describe it if someone asks. We went looking for representations in Claude with the same property: representations that are positioned to influence what Claude might say—not necessarily what it’s saying right now, but what it could talk about, if asked. Our technique is called the Jacobian lens, or J-lens for short. For every word in Claude's vocabulary, the J-lens finds the internal activity pattern that makes Claude more likely to say that word at some point in the future.

When we apply the lens to Claude’s internal activity, we get a list of words—the contents of the J-space at that moment—which we can simply read. Claude processes text through a series of multiple internal stages called layers, and by applying this technique over different layers, we can watch these silent words in the J-space evolve as the model works through what to say.

What shows up in the J-space goes well beyond the text Claude is reading or writing. When Claude reads code with a bug that nobody has pointed out, its J-space contains “ERROR.” When it reads the raw letters of a protein sequence, the J-space contains the protein's biological function. When it reads search results that are secretly an attempt to manipulate it (an attack known as a “prompt injection”), the J-space contains “injection” and “fake.” When we ask Claude a multi-step math problem, the intermediate steps pop up in the J-space, in the right order. So even though the J-space was discovered by looking for representations that could be spoken, it nevertheless uncovers Claude’s internal thoughts. In a sense, this is similar to how some people “think in words,” without having to say them out loud.

J-lens readouts on six prompts, at various layers. In each case the lens surfaces an internal assessment or computation that appears nowhere in the text: the steps of a reasoning or math problem, the presence of a bug, recognition of an image, the function of a protein, and the suspicion that search results are fabricated.

Claude reports what’s in its J-space

Our first set of experiments tested how the J-space is involved in Claude’s verbal reports. In one experiment, we ask Claude to silently think of an item from some category—a sport, say—and then name it. If we read the J-lens right before Claude answers, we can see what it picked: “Soccer” is at the top of the list, and sure enough, Claude says “soccer.” By itself, though, this is just a correlation. The J-space might be where Claude’s answer comes from, or it might just mirror a decision made somewhere else, like a scoreboard that tracks a game without affecting it.

To check, we intervened directly. We reached into Claude’s neural network, removed the “Soccer” pattern, and added an equally strong “Rugby” pattern in its place, leaving everything else untouched. Claude then reports that the sport it was thinking of is rugby. If the J-space were a mere scoreboard—a passive record of a decision made elsewhere—editing it would have done nothing: Claude would still have said “soccer.” Instead, Claude’s answer followed the edit, which tells us the answer is genuinely read out of the J-space.

In another experiment, we told Claude that a thought might have been injected into its mind and asked it to report what, if anything, it noticed. For instance, in the example below, while Claude was still reading the question, we injected the “lightning” pattern into its J-space. Claude reported that the injected thought was about lightning. The same result held across many injected concepts.