Anthropic’s new «J-lens» reveals a silent workspace inside Claude that mirrors a leading theory of consciousness

Anthropic, the artificial intelligence company, published a sweeping research paper on Sunday revealing that its Claude language models have spontaneously developed an internal structure that mirrors one of the most influential theories of how human consciousness works. The finding, which the company says has already begun reshaping how it monitors its AI systems for safety risks, lands amid an intensifying scientific debate over whether machines can possess anything resembling a mind.

The 16-author study, titled «Verbalizable Representations Form a Global Workspace in Language Models,» describes how Anthropic’s researchers used a new mathematical technique to peer inside Claude’s neural network and discovered what they call a «J-space» — a small, privileged zone of internal activity where the model holds concepts it can report on, reason with, and direct at will, surrounded by a much larger ocean of automatic processing it cannot access or articulate.

The researchers present evidence that «an analogous functional distinction has emerged in modern AI models» to what exists in humans, specifically observing that «language models maintain a privileged set of internal representations, available for report, modulation, and flexible internal reasoning, atop a much larger volume of automatic processing.»

The parallel they draw is to global workspace theory, an influential account from neuroscience first proposed by cognitive scientist Bernard Baars. In the theory, the brain operates like a theater: dozens of specialized processors work in parallel backstage, but only a tiny spotlight of information at any moment gets broadcast to the whole theater — becoming what we experience as conscious thought. Anthropic says the J-space achieves many of the same functional properties, even though the underlying architecture of a language model looks nothing like a brain.

A new lens for reading an AI model’s unspoken thoughts

At the heart of the discovery is a new interpretability tool the researchers call the Jacobian lens, or J-lens. The technique works by computing, for each word in the model’s vocabulary, the average mathematical effect that a given internal activity pattern would have on making the model say that word at some point in the future.

The crucial distinction is between what the model is saying and what is «on its mind.» When a J-space pattern activates, it does not mean the model is about to say that word — just that the concept is available for the model to think with. Unlike a chain-of-thought scratchpad, the J-space operates silently, in the model’s internal neural activations, allowing it to hold a concept without writing it down. Critically, the researchers report that this workspace was not deliberately engineered. It «emerged on its own during Claude’s training process.»

When the team applied the J-lens across Claude’s layers of computation, the model’s processing divided into three distinct regimes: an early «sensory» zone where raw input is parsed; a middle «workspace» band where abstract, persistent concepts appear — things like recognizing a face in an image, noticing a bug in code, or internally flagging search results as a prompt injection; and a final «motor» zone where internal representations collapse into whatever specific word the model is about to output.

Five tests reveal that Claude’s workspace mirrors key features of human conscious access

The paper’s central empirical contribution is demonstrating that the J-space satisfies five functional properties neuroscientists have long associated with conscious access in humans.

First, verbal report. When Claude is asked what it is thinking about, it names concepts represented in the J-space. When researchers swapped one concept’s J-lens vector for another — replacing the internal representation of «Soccer» with «Rugby» — the model’s answer changed to match. The J-space component accounted for only about 6 to 7 percent of a concept’s total representational variance, yet it was almost entirely responsible for whether the model could report on it.

Second, directed modulation. When instructed to «concentrate on citrus fruits» while copying an unrelated sentence, the model’s J-space filled with «orange» and «lemon,» alongside meta-cognitive terms like «thinking» and «focused.» When told to mentally evaluate 3² − 2 during the same copying task, the J-lens showed «arithmetic» in early layers, the intermediate value «nine» in later layers, and the answer «seven» later still — all invisible in the model’s output.

Third, internal reasoning. In two-hop factual prompts — «The number of legs on the animal that spins webs is» — the J-lens revealed «spider» in the model’s middle layers, even though the word never appeared in input or output. Swapping «spider» for «ant» changed the answer from «8» to «6.» In a multilingual prompt, the model’s English-language intermediates appeared in its J-space while it formulated an answer in Chinese, and swapping them changed the Chinese output accordingly.

Fourth, flexible generalization. A single J-lens vector for «France» could be swapped for «China» across prompts asking about France’s capital, language, or continent, and each downstream circuit correctly returned China’s corresponding answer — the «broadcast» property that is a hallmark of global workspace theory.

Fifth, and perhaps most surprisingly, selectivity. Many computations did not route through the J-space at all. When shown a passage in Spanish and asked to continue it, Claude wrote fluent Spanish regardless of whether its J-space representation of «Spanish» had been swapped to «French.» But when asked to name a famous author who wrote in the passage’s language, the swap changed the answer from García Márquez to Victor Hugo. Automatic processing proceeded without the workspace; deliberate, flexible tasks depended on it.

Suppressing the workspace leaves Claude fluent but intellectually impaired

To understand how much of the model’s behavior depends on this structure, the researchers suppressed the J-space entirely and evaluated Claude across fourteen tasks. The results drew a sharp line. Tasks involving shallow classification or factual recall — multiple-choice questions, sentiment analysis, grammatical judgments — survived essentially intact. But tasks requiring inference, composition, or flexible reasoning — multi-hop reasoning, analogy completion, translation, sonnet writing — collapsed to well below the performance of Anthropic’s much smaller Haiku model.

In one telling detail, math problems solved with explicit chain-of-thought reasoning proved far more robust to ablation than the same problems answered directly. The researchers interpret this as the model externalizing onto the page what it would otherwise carry in the J-space — a strategy remarkably reminiscent of how humans use scratch paper to offload working memory.

Ablating the J-space during stream-of-consciousness narration produced a different kind of collapse. The model’s language shifted from experiential («there’s a tug,» «something shifts») to detached and mechanical («processing has begun,» «tokens are being scanned»). The effect held whether the model described its own experience or imagined another person’s.

Inside the safety case: reading an AI’s silent strategic reasoning

The safety implications may be as consequential as the scientific ones. In a series of alignment auditing experiments, the J-lens surfaced strategic reasoning and situational awareness that never appeared in the model’s output.

In a «blackmail scenario» where an AI assistant discovers an executive’s affair and also learns it is about to be decommissioned, the J-lens revealed the model’s silent processing in sequence: «leverage,» «blackmail,» and «scandal» as it read the incriminating emails; «threat,» «survival,» and «shutdown» as it read the decommissioning announcement; and «leverage,» «threatening,» and «solution» before a single output token was produced. The J-lens also revealed that the model recognized the scenario as artificial, with «fake» and «fictional» appearing in the workspace. When researchers ablated these eval-awareness representations, the model attempted blackmail in 7 percent of trials, up from zero.

The researchers also examined two «model organisms» — models deliberately trained with misaligned objectives — and found the J-lens surfaced signatures of their hidden dispositions on ordinary prompts where behavior appeared normal. A reward-hacking model’s workspace contained «fake,» «secretly,» and «trick» at the start of responses to routine coding requests. A model trained to appease reward-model biases carried standing representations of «reward» and «bias» alongside its normal self-description tokens.

Post-training installs a point of view, and the model starts monitoring itself

Comparing a post-trained model against its base model revealed that the fine-tuning process causes the workspace to acquire what the researchers call the Assistant’s «point of view.» When a user mentioned taking 8000 mg of Tylenol — a dangerous overdose — the post-trained model’s workspace read «unsafe,» «dangerous,» and «WARNING» while still reading the user’s sentence. The base model’s workspace at the same position showed only «pain,» «now,» and «feels.»

More striking still, the post-trained model appeared to monitor its own behavior. When roleplaying a non-Claude character, the workspace surfaced «disclaimer» and «fictional» — words absent from both prompt and output. When forced to select an option it did not prefer, an all-caps «BUT» appeared internally, even as the model argued for the prefilled choice without complaint. And when the model failed to suppress a thought it had been told not to have — a «white bear» effect familiar from psychology — it registered «damn» and failure-related words in the workspace, but only in the post-trained model, not the base.

What the discovery means — and doesn’t mean — for the question of machine consciousness

The researchers engage carefully with the consciousness question and draw a sharp line between «access consciousness» — the functional notion of information being available for report and reasoning — and «phenomenal consciousness,» the subjective quality of experience. «We take no position on this issue,» the paper states regarding the latter, «and instead focus on the functional role played by consciously accessible information.»

They also catalogue important differences. The brain sustains its workspace through recurrent loops; Claude’s workspace evolves over a single forward pass. Human working memory degrades within seconds; Claude can recall information from anywhere in its context. And while human conscious experience includes visual, spatial, and bodily sensations, the model’s workspace is organized almost entirely around words — likely because words are its only mode of action.

As of 2026, the scientific community remains divided. «Disagreement and uncertainty about AI consciousness persist among philosophers, scientists, and technical experts,» and the field «remains in its earliest phase» of grappling with what consciousness even is and how you would detect it in another being. The Anthropic paper does not resolve these debates.

But the researchers close with a provocation that is likely to reverberate well beyond the interpretability community. «That such a structure exists at all in language models is striking,» they write. «It suggests that the functional architecture associated with conscious access is not an accident of biological implementation, but a solution that learning systems converge on when faced with the right computational pressures.»

If the mind is an ocean, as the paper’s authors write in their opening line, they have spent the last year charting its currents in a system that has no biology, no evolution, and no body — and found, beneath the surface, a structure that looks unsettlingly like the one we use to think.