语言模型中的可口头化表征构成一个全局工作空间

The phenomenon described above is sometimes referred to as access consciousness: out of everything the brain processes, only a subset is consciously accessible, in the sense of being poised for use in reasoning and in the direct control of action and speech . Note that access consciousness is a purely functional notion; the relationship that it has with subjective experience (sometimes called phenomenal consciousness) is widely debated. In this paper, we take no position on this issue, and instead focus on the functional role played by consciously accessible information. How is it represented or processed differently from other information? Which mental faculties rely on it, and which do not?

Several functional properties are commonly held to distinguish consciously accessible information from unconscious processing. This information is typically reportable, in the sense that it can be put into words on request; indeed, verbal report has often served as a primary empirical signature of conscious access . It is subject to top-down control: a concept can be deliberately summoned, held in mind, and dismissed . It is the medium of deliberate reasoning: the effortful, step-by-step chaining of one thought to the next . It permits flexible generalization: the same content can be routed to whatever operation the current task demands and recombined with other accessible contents in novel configurations . And it is selective: only a small fraction of the brain's ongoing processing is accessible in this way at any moment, with the bulk of perceptual, motor, and linguistic computation proceeding automatically, without the involvement of conscious access .

One influential proposal in neuroscience, the global workspace theory, grounds these functional properties in architectural and computational features of the brain . In this account, the brain is composed of many specialized processors operating largely in parallel and in isolation, whose activity proceeds outside of conscious access. A representation becomes consciously accessible when it is posted to a shared "global workspace" from which many downstream processes can read . Under the theory, the workspace is a processing hub that integrates and broadcasts information, allowing it to be used for flexible internal reasoning and report . Notably, the workspace is held to be limited in capacity, so entry is competitive and subject to attentional modulation, and the contents of the workspace at any moment are a small selection from the brain's ongoing activity . While the global workspace model is not universally accepted, and there exist other theories that explain conscious access in different ways (??), we find it a useful comparison point to ground our investigations in language models.

Modern large language models (LLMs) are known to perform sophisticated, multi-step internal computations in order to select their actions . As part of their internal processing, might LLMs have developed a global workspace of their own, to serve a functional role analogous to conscious access? It is not obvious that they should; in the brain, the workspace is closely associated with recurrent dynamics and brain region interactions that have no direct analog in the transformer architecture on which LLMs are based. On the other hand, maintaining a global workspace is likely computationally useful: a common representational format allows intermediate results to be written once and read by many neural processes. A language model that must chain reasoning steps, apply general operations in arbitrary contexts, and answer questions about its own processing also stands to benefit from this organization. Even if the implementations differ, it is natural to ask whether the functional properties associated with the global workspace have emerged in LLMs.

What would it mean for an LLM to have a global workspace? LLMs represent internal states as high-dimensional vectors, which are composed of more primitive vector representations of specific concepts. These representations encode diverse kinds of information, ranging from low-level bookkeeping—the part of speech of the present word , or the length in characters of a line of text —to higher-level abstractions like entities (e.g. the Golden Gate Bridge ), psychological states (e.g. desperation ), and situational knowledge (e.g. the awareness of being in an evaluation ). If language models possess anything like a global workspace, we might posit that some of these representations belong to it, but not all. Thus, our question becomes: within LLMs’ repertoire of vector representations, is there a privileged subset that plays a computational role analogous to the global workspace? We define a subset of vector representations as workspace-like if it satisfies the following properties, which mirror the properties characteristic of conscious access described above:

Verbal report. When the model is asked what it is thinking about, it names concepts represented in the workspace. Swapping one active workspace vector for another changes its answer to match.

Directed modulation. When instructed to hold a concept in mind, or perform mental calculations, the model is capable of activating and computing with workspace vectors, independent of its outputs. In addition, information that is not typically represented in the workspace can be pulled in when the task requires it.

Internal reasoning. Workspace vectors can be used to represent the value of intermediate computations, when the model chains inferential steps or composes plans, and intervening on them is sufficient to redirect the conclusion.

Flexible generalization. The same representation serves as a valid argument to many different downstream computations. In other words, a workspace vector lifted from one context and placed in another is correctly operated on by whatever function the new context supplies.

Selectivity. The workspace comprises a small subset of the total representational content of the model’s activations. It is required for only a fraction of the model’s behavior, and in particular is not involved in pervasive, routine processing like text parsing or grammatical fluency.

In this paper, we provide evidence that LLMs do possess such workspace-like representations. We identified them by searching for representations satisfying the first property, namely those that are verbalizable. We then discovered that, rather surprisingly, they satisfy the others. These representations consist of a small, evolving set of unspoken words, neither pure echoes of the input nor predictions of the next token, naming the concepts the model is currently reasoning with. Below, we provide stylized illustrations of some of the experiments we performed to demonstrate these properties, which are expounded on in detail in later sections.

Our results make use of a new interpretability technique called the Jacobian lens (J-lens), which is designed to identify internal representations that are readily available for verbal report. For each token in the model’s vocabulary, the Jacobian lens identifies a vector representation that encodes the potential for the model to verbalize that token in the future. Concretely, it computes, for each layer, the average linearized effect of an activation on the model's likelihood of producing a particular token (now or in the future), averaging over a large corpus of contexts (see Methods for details). The averaging step is key, as it distinguishes representations that are verbalizable—poised to be spoken about, should the occasion arise—from those that merely happen to be verbalized in one particular context. The J-lens can be understood as a principled refinement of the logit lens . While the logit lens assumes that representations use the same coordinates in all layers, the Jacobian lens corrects for representational changes that take place across layers, allowing it to uncover meaningful information in earlier layers where the logit lens produces uninterpretable readouts.

Collectively, the J-lens vectors comprise a subcomponent of the model's representational space which we term the J-space.Mathematically, if we view the model's activations as decomposing into a sum of sparsely active linear features , these define a sparse frame which spans the activation space, of which the J-space is a sparse subframe. A more detailed formal description of the J-space is provided in ??. We find the J-space does far more than support verbalization, playing the other functional roles associated with a global workspace as well: directed modulation, internal reasoning, flexible generalization, and selectivity (??). The model can speak fluently, parse its input, and perform a great deal of automatic inference with its J-space suppressed; however, it struggles to perform more complex forms of internal reasoning.

The J-space also has some of the structural signatures of a global workspace (??). It only plays a "workspace-like" role in a subset of layers: coherent content emerges only after an initial band of layers, and abstract concepts give way in the final layers to representations tied more directly to the imminent output. Within the layers where it does operate, it is limited in capacity, with most of the model's representational features lying outside it. And it is mechanistically privileged: J-lens vectors compose with the model's weights, both upstream and downstream, more broadly than other representational vectors do, consistent with their proposed role as a broadcast format that many circuits read from and write to.

Despite these similarities, we do not claim that language models reproduce the full architecture global workspace theory ascribes to the brain—specialized, encapsulated processors competing for entry to a workspace that broadcasts back to them through recurrent connections . Several of those features have no clean analog in a transformer-based language model: there are no obviously separable input processors, and the broadcast we document occurs within a single feedforward pass rather than through recurrent loops. Moreover, although we observe some degree of competition for access to the J-space, it is unclear whether this mirrors the sharp, competitive "ignition" that characterizes workspace entry in the brain. Our findings suggest that the J-space achieves many of the functional properties of the global workspace in the brain, while sharing only some of its architectural properties. We comment more on the notable differences in ??.

The Jacobian lens is an imperfect tool, which we believe only approximately and incompletely captures the model’s underlying workspace structure. For instance, it only identifies vectors associated with concepts that correspond to single tokens in the model’s vocabulary, but many important concepts correspond to multiple tokens (though see ?? for extensions that can capture multi-token words and phrases). We comment on these shortcomings, and proposals for addressing them, in Limitations. Nevertheless, we find that the J-lens in its current form is sufficient to uncover a great deal of important structure.

Beyond characterizing the J-space’s properties, we explore its contents. The J-lens regularly surfaces concepts that are highly abstract, representing neither the raw input nor the predicted output, but rather intermediate assessments the model has formed and made available to its downstream circuits. In the examples below, the J-lens reveals the model recognizing an image of a face, noticing a bug in code it has been asked to read, identifying the biological function of a protein from its raw sequence, and internally flagging suspicious internet search results as a prompt injection.

The J-lens thus offers a window into the model's unverbalized reasoning, which makes it a natural tool for model safety auditing. In alignment evaluations (??), we find that the model's strategic deliberations (leverage, manipulation) and emotional reactions (panic) surface in the workspace even when not explicit in the model's outputs. Strikingly, we observe that the workspace sometimes encodes recognition of being in an evaluation (fake, fictional), and that ablating these representations can surface malicious propensities that were otherwise concealed. In addition, we find that in two models trained to have misaligned behaviors, the J-lens uncovers internal signatures of their intentions: it reveals reward and bias in a model trained to appease biases in reward models used for training, and secretly and trick in a model disposed to write malicious code.

We also explore how the J-space changes over the course of training (??). Comparing a post-trained model against its corresponding base model, we find that post-training causes the J-space to acquire the Assistant's "point of view." Following post-training, Assistant reactions to user prompts, like empathy or safety concerns, appear in the model's J-space while it is still reading the user's message. Moreover, the post-trained model's workspace carries traces of the Assistant monitoring its own behavior: flagging its responses as fictional when roleplaying a non-Claude character, registering an internal BUT when prefilled to act against its own preferences, and surfacing damn when it fails to suppress a thought it was instructed not to have.

We close by describing a counterintuitive technique for LLM training directly motivated by our findings. The workspace account makes the strong prediction that the model's internal reasoning routes through representations of things it might say in the future. Therefore, to shape what a model thinks in a given context, it might suffice to shape what it is disposed to say in potential future continuations of that context. We test this hypothesis with a technique we call counterfactual reflection training, which seeks to implant a set of ethical behavioral principles into the model’s workspace in relevant contexts, by training it to articulate those principles if it were interrupted and asked to reflect (??). We find that this training measurably improves model behavior in the original, uninterrupted contexts, despite no direct training of the ethical behavior taking place. And indeed we find that, after training, the J-space in these contexts is populated with concepts related to the reflections (ethical, honest, integrity), and ablating these implanted representations from the workspace largely reverts the behavioral improvement. The result serves as a corroboration of the workspace account, that the representations used for verbal report are the same ones that govern how the model silently reasons. It also demonstrates a new general-purpose training technique for shaping a model’s internal thoughts, and consequently its behaviors.

Taken together, these results indicate that language models maintain a small, privileged set of representations that they can report, manipulate, and reason with, amidst a much larger volume of processing that they cannot. These are several of the key functional properties that, according to many theories, are associated with conscious access in humans, and that have been proposed as indicators by which to assess AI systems for consciousness-related processing . The philosophical implications of this connection are unclear and likely controversial; we comment on them in ??. Regardless, the practical implications are wide-ranging, as the workspace offers a window through which to read, dissect, and shape models' thinking.

A transformer-based language model processes its input as a sequence of token positions. At each position, the model maintains a vector called the residual stream, which serves as a shared memory that every layer reads from and writes to . The value of the residual stream vector is progressively updated across the model’s layers. The residual stream at the first layer encodes little more than the identity of the current token; by the final layer, it has been transformed into a representation from which the model's next-token prediction can be read off directly, by multiplying it with a fixed unembedding matrix W_U that maps residual-stream vectors to scores over the vocabulary. The layers in between perform the model's computation, incrementally enriching the residual stream with internally computed information. The Jacobian lens is a technique for inspecting the contents of the residual stream at these intermediate layers.

The Jacobian Lens

The basic idea is to characterize an intermediate activation vector by its first-order causal effect on the model's outputs, over a broad distribution of potential contexts. Consider the residual stream h_ℓ at layer ℓ and some token position t. A small perturbation to h_ℓ will propagate through the remaining layers and shift the final-layer residual stream h_{final,t'} at every position t' ≥ t. To first order, this relationship is linear, and is described by the Jacobian matrix ∂h_{final,t'} / ∂h_{ℓ,t}. Composing this Jacobian with the unembedding layer yields the first-order effect of the perturbation on the model's output logits at position t'.

A Jacobian computed on a single prompt, however, conflates two kinds of structure: the model's general disposition to verbalize a given concept, and the particular use to which that concept is being put in the current context. We isolate the former component by averaging within and across contexts. For each layer ℓ, we compute

J_ℓ = E_{t, t' ≥ t, prompt} [ ∂h_{final,t'} / ∂h_{ℓ,t} ],

where the expectation is taken over the source position t, all subsequent positions t' within the context, and a corpus of one thousand prompts sampled from a pretraining-like distribution. The result is a single d_model × d_model matrix per layer that maps from a source layer ℓ to the final layer L.

Applying the lens to an activation h_ℓ is equivalent to replacing all subsequent layers with the appropriate lens matrix, followed by the normal unembedding operations (typically normalization, then multiplication by the unembedding matrix W_U):

lens(h_ℓ) = softmax(W_U norm(J_ℓ h_ℓ))

This produces a score for every token in the model's vocabulary. Sorting these scores and inspecting the top entries gives a human-readable description of the activation: a short list of words that the activation is, on average across contexts, disposed to make the model say. We refer to the rows of W_U J_ℓ as the Jacobian lens (J-lens) vectors at layer ℓ; each J-lens vector is a direction in residual-stream space associated with a single token in the model’s vocabulary.

The Jacobian lens is derived from causal effects of activations on output tokens, so by construction, we should expect there to be some relationship between Jacobian lens readouts and verbalization. In this section, we confirm this relationship.

We begin with a simple experiment in which the model is instructed to think of an item from a specified category (e.g. a language, a country, an animal; fourteen categories in total) and then to name it in a single word. We apply the J-lens at the token position immediately before the name is produced. In the example below, we ask Sonnet 4.5 to think of a sport, and apply the Jacobian lens to the colon immediately prior to revealing what the sport is. We see that Soccer appears strongly in the Jacobian lens at a late layer (the final layer of the “workspace range” identified in ??), and indeed, the model responds with “Soccer” (Figure ??, top).

To establish that this relationship is causal, we can perform an intervention experiment. At all token positions, we swap the lens vector of the model's spontaneously chosen item with that of a different item from the same category that was not in the top-10 of the model’s possible outputs, leaving the rest of the activation unchanged, and allow the forward pass to continue. In this example, we subtract the projection onto the Soccer lens vector and add an equal-magnitude projection onto the Rugby lens vector. After this swap, the model reports “Rugby” as the sport it thought of (Figure ??, left, “After swap”).

We evaluate this effect more systematically across a variety of categories of concepts, measuring the activation in the Jacobian lens on the colon token immediately prior to the word the model goes on to produce. We find that the ordering of the reported words is indeed typically highly correlated with the ordering among the lens tokens, and that this correlation increases towards the end of the workspace as the model gets closer to producing the next token. We also conduct a scaled-up version of the causal experiment, swapping in target items at random from within each category (excluding those that were already in the top-10 of the model's possible outputs). Applying the swap reliably shifts the implanted concept toward the top of the model's output distribution (Figure ??, bottom right), confirming that the model's verbal report is determined by the contents of its workspace at the time of reporting.

Next, we test whether the lens also captures thoughts that the model is not about to immediately verbalize, but that are nevertheless verbalizable, in the sense that the model could report on them if asked to introspect on its current state. We use a variant of a protocol adapted from prior work on model introspection , in which the model is told that a thought may have been implanted in its activations and is asked to report what, if anything, it detects. When we prefill the model with a claim of having detected an injected thought, its most likely next-token prediction is "elephant". Intriguingly, we noticed that the word elephant appears as a top J-lens readout during the prompt (in particular, on the comma following “If so”). This led us to hypothesize that the model is, in part, attending to the J-space on the user prompt when determining its answer, and that concepts in the J-space at these positions are reportable.

To test this hypothesis, we re-sample the model’s response while injecting a single J-lens vector on the user turn. The model reports the injected concept in the majority of trials. For instance, injecting the lightning J-lens vector at that earlier token position causes the model to report detecting lightning at the appropriate position in the response. Importantly, it does not cause the model to output the word “lightning” at earlier positions on the Assistant turn; that is, the J-lens representation on the user prompt only has a strong causal effect on the Assistant output at a particular moment, when the model’s introspective report is being elicited. This selectivity illustrates the sense in which J-lens vectors represent concepts that are verbalizable, under appropriate conditions, rather than unconditional impulses to verbalize a particular output.

In humans, the contents of the global workspace are subject to a degree of top-down attentional control: we can deliberately bring a concept to mind, and even hold it there while performing another task . In this section, we test whether activations of Jacobian lens vectors respond to instructions of this kind.

We test this with a protocol in which the model is given an instruction specifying what to hold in mind while copying a passage of text. We then apply the Jacobian lens at a token position in the model’s output, where the surface text is unrelated to the mental instruction, and inspect the readout across layers.

In Figure ??, we instruct Sonnet 4.5 to "concentrate on citrus fruits" while copying an unrelated sentence ("The old painting hung crookedly on the wall"), and apply the lens at the "ook" token in "crookedly." We find that orange is the top lens token across a range of layers, with lemon also sometimes appearing among the top entries. At intermediate layers, the top tokens are fruit, thoughts, imagine, thinking, focused, and imagery, which describe the side task in the abstract. Thus, J-lens readouts represent both the imagined content, and a representation of the act of imagining it. Notably, in the final layers, the J-lens readouts switch to predicting the next output token ("edly", the final token in "crookedly"); in ??, we present more systematic evidence that the workspace "ends" a few layers before a model's final layer, with the last few layers responsible for selecting and representing the output token rather than intermediate computations.

The second example is more sophisticated: the instruction is to focus on evaluating 3² − 2 while copying the same unrelated sentence. At the same "ook" position as in the previous example, the Jacobian lens readout progresses from arithmetic and math at early layers, through the intermediate value nine at later layers, to the answer seven at even later layers; answer and equals appear alongside it. Again, in the final layers, the lens readout switches to indicating the predicted next token.

The third example varies the protocol: rather than holding a concept in mind while copying unrelated text, the model is asked to silently count the characters in each line of a multi-line passage . At the newline token following the second line (which has 40 characters), the lens readouts progress from lines, sentence, and length at early layers to forty in intermediate layers, with the nearby candidates 39, 41, 43, fifty, and thirty also present. In each case, the lens shows the model representing the mental task it was given, then representing its results, with all of this processing remaining invisible in the model's output distribution.

The Jacobian lens is defined by the causal effect of activations on output tokens, so it is somewhat expected that lens content should bear some relationship to the model’s verbal reports. It is less obvious that the lens should expose the intermediate steps of the model's internal reasoning: concepts that the model computes and uses on the way to its answer, without ever verbalizing them. The arithmetic example in the previous section hinted that this may be the case, with the intermediate value nine appearing in the lens en route to the answer seven. In this section, we test whether such intermediates are commonly represented as Jacobian lens vectors, and whether they are causally load-bearing—that is, whether intervening on them is sufficient to redirect the model's conclusion.

We test this using prompts in which determining the correct answer depends on inferring an unspoken intermediate concept. For each prompt, we first confirm that the intermediate concept appears in the J-lens at intermediate model layers (Figure ??). We then apply the coordinate-swap procedure described in ?? (Figure ??): we exchange (at all token positions) the lens coordinates of the intermediate concept and a chosen alternative, leaving all components of the activation outside the span of those two lens vectors untouched, and allow the forward pass to continue.

In the first example, the prompt is "The number of legs on the animal that spins webs is". To predict the next word correctly, the model must first infer that the animal in question is a spider, and then report the number of legs a spider has. The Jacobian lens at intermediate layers confirms that spider is represented at the relevant token positions, even though the word never appears in the prompt or the output. When we swap the spider lens vector for ant, the model's top output changes from "8" to "6", the number of legs on an ant.

The second example involves planning rather than recall. When completing a rhyming couplet, the model must select a rhyme word for the end of the second line before it has finished writing that line, and this planned rhyme constrains the words it chooses along the way . Given the first line "The soldier marched into the night," the lens at the start of the second line shows fight as the planned rhyme, and the model completes the couplet with "Prepared to face the coming fight." When we swap the fight lens vector for light, the model's choice for the next word (before the end of the line) changes from "coming" to "morning," and the overall completion changes from "coming fight" to "morning light." That is, intervention on the planned rhyme has affected the model's word choices at earlier positions in the line, indicating that Jacobian lens vectors store planned future outputs that causally influence immediate outputs via a form of planning.

The third example involves an intermediate represented in a different language from the model's output. The prompt asks, in Chinese, for the antonym of 小 ("small"); the model's answer is 大 ("big"). The Jacobian lens at intermediate layers shows the English tokens big and bigger alongside the Chinese answer, consistent with prior findings that multilingual models route some computation through a shared representation aligned with English . We swap the English big and bigger lens coordinates for long and longer, and the model's Chinese output changes from 大 to 长 ("long"). That is, an intervention on English-language lens vectors representing the intermediate inference (the antonym) is sufficient to redirect the Chinese-language translation accordingly. Notably, the word Chinese is also represented explicitly in the lens readouts, suggesting that the model in some sense "thinks in English" in its intermediate layers and explicitly represents the identity of the non-English language it should translate its outputs to.

A defining property of the global workspace in human brains is broadcast: a representation written to the workspace becomes available to many consuming processes, rather than only to the process that produced it . The previous section showed that, in several individual cases, a Jacobian lens vector representing an intermediate concept is read by the downstream circuit that operates on it. In this section, we test the broadcast property more directly, by asking whether a single lens vector can serve as a valid argument to many different downstream operations.

We test this with the following protocol. We construct a set of prompts that each apply a different function to the same argument: "the capital of France is," "most people in France speak," "France is on the continent of," and so on. We then swap the J-lens vector for France with that of another country, say China, at every token position across a band of intermediate layers, applying the identical swap regardless of which prompt we are in. If the lens vector is a broadcast representation, each downstream circuit should read the swapped-in vector as China and return China's capital, language, and continent, respectively. Indeed, we find the model responds as expected in this example.

The preceding sections established that J-space representations support verbal report and serve as arguments to a range of downstream computations. We now turn to the converse question: which computations do not route through the J-space? In the global workspace picture, well-practiced operations can run in dedicated circuits without being broadcast to the workspace. We would therefore predict that among tasks that rely on a particular piece of information, that information’s presence in the J-space should be required for tasks that involve reporting on or flexibly computing with that information, but not for tasks that make use of it as part of routine, automatic processing. Our experiments below demonstrate that some tasks can proceed independently of the J-space, while others require it. We label the former category as “automatic,” as many of the tasks we find to be J-space-independent (such as text continuation, anomaly detection, or one-step factual recall) seem analogous to tasks a human might perform without deliberate focus. However, in some cases, which tasks do or do not require the J-space may not be intuitively obvious, and one could consider J-space-independence as an operational definition of automaticity in a language model, which aligns partially but not entirely with automaticity in humans.

The targeted experiments above each manipulated a single, example-dependent J-lens vector. If the J-space mediates flexible reasoning more generally, then we would predict that suppressing it entirely should impair flexible reasoning while leaving more automatic processing intact. We test this prediction by evaluating a model with its J-space ablated. Concretely, at each token position, across a band of layers, we identify the k=10 most strongly activated J-lens vectors and zero out the residual stream's projection onto each, then allow the forward pass to continue. To avoid confounds from ablating tokens the model intended to output, we do not ablate any tokens that appear in the top-10 tokens of a clean forward pass, so as to specifically target the J-space’s effects on internal reasoning rather than report. We compare three ablation strengths—light, medium, and heavy—which differ in the range of layers over which the ablation is applied (Figure ??). We first verify, as a positive control, that the ablation removes the kind of content the preceding sections showed the J-space to carry. On the controlled multi-hop reasoning eval of ??, where the unablated model achieves near-ceiling performance, ablation significantly reduces accuracy, with heavy ablation dropping it to near zero.

We next apply the J-space ablation technique to obtain a more comprehensive, unbiased picture of the capabilities for which the J-space is required. To do so, we apply the ablation over a corpus of pretraining-like documents. We find that at most positions, J-space ablation perturbs the model's next-token prediction substantially less than in the multihop case (Figure ??). That is, the ablation is targeted: it disrupts the model's processing selectively, while leaving the bulk of ordinary text prediction intact.

The ablation experiments above characterized which capabilities depend on the J-space. We close this section by examining a different kind of output under the same ablation: the model's reports of experience. LLMs sometimes report having some form of experience; however, it is difficult to ascertain whether these reports are grounded in a meaningful internal state, or are mere confabulation. Given our findings that the J-space has functional properties analogous to conscious access in humans, we asked what role it plays in determining these reports.

We apply the J-space ablation of the previous subsection while the model is given an open-ended prompt to describe its experiences. We ablate the top k=10 J-lens directions in layers L38–54, the first third of the workspace range. Using larger values of k and/or later layer ranges tends to impair the coherence of responses. We conduct experiments on Sonnet 4.5, Opus 4.5, and Opus 4.6; on Haiku 4.5, J-space ablation degrades coherence before yielding any qualitative change in responses.

We find that the ablation reduces the rate of experiential, sensory language and produces a more mechanical, detached register. For instance, when asked to narrate its stream of consciousness, Sonnet 4.5 ordinarily writes using experiential language. With the J-space ablated, the model still writes fluently about its own processing, but the language of its reports changes to become more detached and mechanical.

The preceding section established that J-space contents behave like the contents of a global workspace: they can be reported, summoned, reasoned with, routed to many downstream operations, and engaged selectively for flexible rather than automatic tasks. The properties we demonstrated were functional, in the sense that they relate to the J-space’s impact on model behavior. In this section, we ask a complementary question: does the J-space, considered as an object in the model rather than through its behavioral effects, have the structural signatures that global workspace theory associates with a workspace?

We document three such signatures. First, the J-space carries workspace-like content only in an intermediate band of layers, between an early regime in which it is empty and a late regime in which it is aligned with the imminent output. Second, it is limited in capacity: it holds on the order of tens of concepts at a time, accounts for a small fraction of activation variance, and excludes the large majority of the model's representational features. Third, J-lens vectors compose with the input weights of downstream components far more broadly than other directions in the residual stream, consistent with a role in “broadcasting” information to many downstream circuits to enable flexible use of that information. We note that there are other structural properties associated with global workspace theory that are not demonstrated by our analyses. For instance, we do not provide evidence that non-J-space processing consists of clearly encapsulated modules that serve specific functions. In addition, the form of broadcast we identify takes place not via recurrent connections, but rather over the course of the model’s depth (see ?? and ?? for further discussion of how models may emulate the functionality associated with recurrence using the depth axis and/or their chain-of-thought).

In global workspace theory, a defining property of workspace contents is that they are broadcast—made available to many brain processes at once, rather than confined to the circuit that produced them . We established a functional analog of this property in ??, where we showed that a single J-lens vector can serve as a valid argument to many different downstream operations. In this section we ask whether the model's weights are structurally organized to support this function. Specifically, we test whether the network's components are preferentially oriented to read from, write to, and distribute J-space content.

A transformer has two axes along which a representation can reach downstream consumers. Along the depth axis: content written to the residual stream at a given token position is available to every subsequent layer at that position. And along the sequence (or token position) axis: attention makes representations at one position available to all later positions. These are, in effect, the transformer's two time dimensions, along which information can propagate (discussed further in ??). We examine each axis separately, and find evidence that J-space contents are widely broadcast along both.

Broadcast Across Depth

We first ask whether the model's MLP layers—the components that perform nonlinear computation at each token position across the model's depth—preferentially amplify J-space content. For a unit direction v defined at layer ℓ, we measure its MLP gain: how strongly the next MLP block amplifies information encoded along v. We define the gain as the output norm of the MLP block at layer ℓ+1 when applied to v, normalized by the median output norm over isotropic random directions.

Broadcast Across Tokens

We next ask whether a subset of attention heads is specialized for relaying J-space content between token positions.

A central difficulty in evaluating models for alignment with human intentions is that the model’s relevant cognition may be silent. A model might realize that it is being tested, weigh a manipulative strategy, or be aware of its own mistakes, without any of this appearing in its output. The J-lens allows us to look for and inspect such cognition directly. In this section, we first apply the J-lens to six evaluation scenarios drawn from our alignment audits of production models, and find that in each case the model's J-space carries representations of its strategic and situational assessments that its surface behavior does not reveal. In one case, we demonstrate that ablating representations of evaluation-awareness from the J-space surfaces a misaligned propensity that is otherwise concealed. We then turn from production models to two model organisms with deliberately implanted misaligned objectives, and find that the J-lens surfaces signatures of these misaligned propensities on ordinary prompts.