1Introduction

If the mind is an ocean, we spend our lives floating at the surface. Beneath us, an enormous amount of processing takes place without our knowledge: our visual systems parsing the contours of a face, our motor circuits maintaining our posture. At any given moment, only a small fraction of this neural activity is accessible to us. Yet it is this privileged sliver of activity that we rely on to reason deliberately: to plan what ingredients to buy for a recipe, or to puzzle out why an engine won’t start. Such thoughts can be articulated out loud, deliberately held in mind, and brought to bear on whatever task the moment demands. This distinction, between our accessible thoughts and our unconscious processing, is perhaps the most striking feature of human cognition.

In this paper, we present evidence that an analogous functional distinction has emerged in modern AI models. Specifically, we observe 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. We identify these representations using a new interpretability technique, which surfaces the concepts a model is poised to verbalize at any point in its processing. Measuring and intervening on these representations provides us a window into a model’s thought processes, uncovering internal reasoning and reactions that do not appear in its output.

1.1Motivation: conscious access and the global workspace

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 (§9.4), we find it a useful comparison point to ground our investigations in language models.

1.2A global workspace 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.

1.3The Jacobian Lens and the J-space

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 §A.8. 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 (§3). 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 (§4). 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 §9.3.

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 §A.9 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.

1.4What can we find in the J-space?

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 (§5), 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 (§6). 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 (§7). 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.

1.5Takeaways

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 §9.4. Regardless, the practical implications are wide-ranging, as the workspace offers a window through which to read, dissect, and shape models' thinking.

2Methods

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.

2.1The 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_\ell at layer \ell and some token position t. A small perturbation to h_\ell will propagate through the remaining layers and shift the final-layer residual stream h_{\text{final},t'} at every position t' \geq t. To first order, this relationship is linear, and is described by the Jacobian matrix \partial h_{\text{final},t'} / \partial h_{\ell,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 \ell, we compute

J_\ell \;=\; \mathbb{E}_{\,t,\,t' \geq t,\,\text{prompt}} \left[ \frac{\partial h_{\text{final},t'}}{\partial h_{\ell,t}} \right],

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_{\text{model}} \times d_{\text{model}} matrix per layer that maps from a source layer \ell to the final layer L.

Applying the lens to an activation h_\ell 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):

\text{lens}(h_\ell) \;= \text{softmax}(W_U \, \text{norm}(J_\ell h_\ell))

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_\ell as the Jacobian lens (J-lens) vectors at layer \ell; each J-lens vector is a direction in residual-stream space associated with a single token in the model’s vocabulary.

The averaged Jacobian, applied to a given activation vector, measures the effect on present and future outputs that the vector might have across the range of contexts the model encounters. The highly weighted output tokens, those that “appear in the lens,” are therefore represented in a verbalizable format. We examine several variants of the Jacobian lens methodology (e.g. computing only present and not future token effects, freezing attention patterns while computing Jacobians, and varying the number of contexts over which we average) in §A.7; our qualitative results are robust to these choices.

2.2Interpreting the J-lens

To illustrate how J-lens outputs can be interpreted, we attach a version of the interactive visualization we used throughout our research (Figure 5). The left column shows the prompt (top), a hoverable table of the top-ranked token at each (position, layer) cell (middle), and a heatmap recording the rank of user-selected ("pinned") tokens across all (position, layer) cells (bottom). The other columns show the full readout across layers at a selected position (middle), and across positions at a selected layer (right), with line charts of each pinned token's rank trajectory.

The example prompt asks Sonnet 4.5 to "Count to five and introspect deeply." In its output, the model dutifully counts to five. The J-lens readout, however, provides a richer picture. The pinned tokens show the model identifying the task as counting and tracking its progress with halfway and done. Alongside these, concepts related to introspection (thoughts, AI, claude, consciousness) cluster near the top of the J-lens readout, until the final few layers, where the readout flips to representing the predicted next token (the "motor" regime; see §4.1). The introspection-related tokens illustrate the model holding concepts in its J-space in response to instructions, while performing a separate surface task (§3.2). In addition, the progress markers halfway and done—which appear in neither prompt nor output—illustrate the kind of contextual awareness the J-lens can surface (§3.3).

We encourage the reader to explore the visualization to build an intuition for the kind of information the J-lens surfaces. We include several other interactive examples in our slice viewer; J-lens readouts on open-source models can be found on Neuronpedia. Note that in roughly the first third of the model, the readouts are noisy and largely uninterpretable; we characterize the layer-wise evolution of J-lens readouts further in §4.1.

2.3The J-Space

At each layer, the J-lens vectors form an overcomplete set: n_{\text{vocab}} vectors in d_{\text{model}}-dimensional residual-stream space, with n_{\text{vocab}} > d_{\text{model}}. These vectors therefore may linearly span the entire residual stream, rather than a lower-dimensional subspace; moreover, due to overcompleteness, there is no unique way to decompose a given activation vector as a linear combination of J-lens vectors (rather, there are many such decompositions).

Empirically, however, we observe that only a relatively small number of J-lens vectors are strongly active at a time (see §4.2). We therefore define the J-space as the set of points expressible as a sparse nonnegative combination of J-lens vectors. For the J-space to be properly defined, we must specify an allowable sparsity level k—this parameter is somewhat arbitrary, and we vary our choice of k throughout the paper, but we typically choose it to be no more than 25, which we empirically observed to be the number of J-lens vectors that are meaningfully active at a given time (§4.2). Geometrically, for a given k, the J-space corresponds to a union of k-dimensional cones, one for each possible set of k J-lens vectors. For a given point in activation space, we can define its J-space component as the point in the J-space nearest to it, and its non-J-space component as the difference between these points.

We operationalize identifying the contents of the J-space by sparse decomposition. Given an activation (or a steering vector, or an SAE feature direction), we solve for a sparse nonnegative combination of k J-lens vectors that approximate it well using gradient pursuit . This combination is our approximation of the activation’s J-space component, and the coefficients of these vectors are its local J-space coordinates. In §4.2, we find that the J-space component typically accounts for only a small fraction of total activation variance (varying by layer, but never more than 10%).

To provide another interpretation, under the superposition hypothesis , a model's activations decompose as sparse linear combinations drawn from an overcomplete set of feature directions—a sparse frame, in the sense of linear algebra, rather than a basis. The J-lens vectors would then constitute a subframe of this feature frame: a token-indexed subset of the model's feature directions. Feature directions outside this subframe make up the bulk of the model's representations (see §A.18). The J-lens subframe, coupled with a sparsity constraint, would then define the J-space as a subset of all possible model activations.

We provide a more formal mathematical definition of the J-space in §A.8.

2.4Comparison to Related Techniques

The J-lens belongs to a family of techniques that produce per-layer token readouts from a transformer's hidden states.

The logit lens applies the unembedding matrix directly to the intermediate residual stream, which corresponds to setting J_\ell = I in our formulation. This approximation is reasonable in late layers due to the influence of residual connections, but it degrades in earlier layers. The J-lens can be understood as the principled correction: J_\ell is precisely the average linear map that relates layer-\ell directions to their final-layer counterparts. Empirically, the two lenses agree closely in the model's last several layers and diverge earlier, with the J-lens recovering interpretable content at depths where the logit lens does not. However, we find the logit lens to be quite useful in practice, and to capture much of the workspace-like structure identified by the J-lens, though with somewhat lower reliability (particularly in earlier layers).

The tuned lens and related methods also fit per-layer linear maps, but train them to match the model's output distribution. This objective is correlational rather than causal, and we find that on prompts involving unverbalized intermediate computation the tuned lens tends to “skip ahead” to the output rather than surface those intermediates. We therefore find the tuned lens less useful than either the logit lens or the J-lens for inspecting internal computation.

Hernandez et al. use Jacobians to derive per-relation linear maps from subject to object representations (e.g. a “plays instrument” map produces “trumpet” from “Miles Davis”). The J-lens applies the same first-order approximation to the map from activations to model outputs, rather than to a single relation.

In §A.5 and §A.6 we perform more systematic comparisons of different lensing methods.

2.5Technical details of J-lens use cases

We use the J-lens, broadly speaking, in two ways: to read which concepts an activation carries, and to write concepts into or out of an activation. Within each category, the details of how the J-lens is used vary somewhat depending on the application.

Reading. The basic readout (Figure 4B) replaces all layers downstream of \ell with the single linear map J_\ell, producing \text{lens}(h_\ell) = \text{softmax}(W_U \,\text{norm}(J_\ell h_\ell)), a score for every vocabulary token. Sorting these scores gives the ranked list we report whenever we refer to the "top lens tokens" at a position. Because the pre-softmax logits are determined (approximately, up to a data-dependent normalization factor) by the inner products \langle v_t, h_\ell \rangle with each J-lens vector v_t, the same machinery can be used as a per-token probe: we read the score (or cosine similarity) of h_\ell against a single chosen v_t, without ranking the full vocabulary. We use this probe form when measuring whether a specific concept is present above a threshold. Finally, when we need a discrete inventory of active concepts rather than a ranked list, we use sparse decomposition: solving, by gradient pursuit , for a sparse non-negative combination of k J-lens vectors that best reconstructs h_\ell. Because the J-lens vectors are overcomplete and non-orthogonal, this gives a different (and typically less redundant) set of active concepts than simply taking the top-k by inner product; it underlies our occupancy estimate and fraction-of-variance analyses in §4.2.

Writing. The simplest intervention is steering along a J-lens vector: h \leftarrow h + \alpha\, v_t, applied at one or more layers and token positions. With negative \alpha, or by projecting out the component of h along v_t entirely, this becomes an ablation. We use ablation to suppress particular concepts, or to suppress the top-k J-space contents. We use positive steering to test introspective detection of an injected concept. The second intervention, patching in lens coordinates (Figure 4C), exchanges one concept for another while leaving the rest of the activation fixed. Given a source token s and target token t, we form V = [v_s\; v_t], read the lens coordinates c = V^\dagger h (where V^\dagger is the pseudoinverse of V), and set h_{\text{patched}} = h + V(\sigma(c) - c), where \sigma swaps the two entries of c (optionally scaled by a factor \alpha). The component of h orthogonal to \text{span}\{v_s, v_t\} is unchanged.

Throughout the paper, we report results on 25 evenly spaced layers of the model’s residual stream reindexed to the range [0–100] so that layer numbers can be interpreted as percentages. By default, we report results on Claude Sonnet 4.5, but we corroborate key results on Haiku 4.5 and Opus 4.5 as well, and in some sections conduct analyses on Opus 4.6.

3The J-space acts as a Global Workspace

The Jacobian lens was constructed to identify verbalizable representations. In this section, we first demonstrate that it succeeds in doing so, and then go on to show that these representations serve a broader functional role: they exhibit the cluster of properties, enumerated above, characteristic of a global workspace.

3.1The J-space supports verbal report

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 §4.1), and indeed, the model responds with “Soccer” (Figure 6, 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 6, 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 6, 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 (Figure 7). 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.

The experiments above show that swapping or injecting J-lens vectors changes the model's verbal reports. These interventions do not, however, establish that the J-space is privileged for report: a direction outside the J-space that encoded the same concept might drive the model's report equally well. To test whether the J-space is in fact privileged, we decompose the model's full representation of a concept into a component inside the J-space and a component outside it, and measure the contribution of each to verbal report.

We extract concept vectors using an approach introduced in prior work : recording the residual stream activation prior to the Assistant’s response to the prompt "Tell me about {concept}", mean-subtracted over a baseline set of 100 other concepts. We then split each concept vector into two parts: a J-space component, the non-negative combination of its top k=16 J-lens vectors found by gradient pursuit, and a non-J-space component, the remainder (Figure 8, left). Notably, across concepts and workspace layers, the J-space component carries a median of only 6–7% of the concept vector's variance, with the remaining ~93% lying outside the J-space.

We then repeat both experimental protocols from this section, substituting each component for the J-lens vectors used previously, with every perturbation rescaled to the same magnitude. In the "think of a {category}" swap experiment, swapping along the concept vectors' J-space components drives the swap target into the model's top-5 outputs on 59% of trials, approaching the 88% achieved by the pure J-lens vectors. However, swapping along the non-J-space components succeeds on only 5% of trials (Figure 8, middle).

The “injected thought” introspection experiment produces a similar result: at each condition's most effective injection strength, the J-space component produces a report of the injected concept nearly as often as the pure J-lens vectors do, while the non-J-space component produces few reports even at injection strengths several times larger (Figure 8, right).

The non-J-space component's small residual effect on verbal report could in principle route through the J-space: the injected component might cause downstream layers to re-derive the concept and write it into the J-space, which then drives the report. To test for this, we repeat the non-J-space conditions while clamping the relevant J-lens coordinates to their clean-pass values at every position and layer, so that the concept cannot re-enter the J-space. Under this clamp, the non-J-space component's effect falls to zero in the swap experiment and nearly to zero in the injection experiment, indicating that what little effect it has on the report is itself mediated by the J-space.

Taken together, these results indicate that the J-space component of a concept's representation, despite accounting for a small fraction of its variance, is responsible for that concept's availability for verbal report.

3.2The J-space is subject to directed modulation

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 9, 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 §4.1, 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.

In Figure 10 we evaluate this effect more systematically over many trials of the three task families represented above (thinking of a category instance, mentally evaluating a mathematical expression, mentally counting a line width). In each case, we measure the rate at which the target concept appears in the Jacobian lens readout while the model is copying text (in the first two tasks) or reading it (in the last). We compare three instruction conditions: positive instructions (“think about X”), negative instructions (“ignore X”) and a no-instruction baseline. The baseline rate is approximately zero in all conditions, confirming that the prompt context on its own does not cause the target concept to appear in Jacobian lens readouts. Under the "think about X" instruction, the target appears in the lens on a substantial fraction of trials, and this tends to increase with model size.

Under the ignore instruction, target presence is substantially lower than under the focus instruction, but it is not zero. Since the no-instruction baseline is approximately zero, the ignore instruction itself causes some activation of the target concept in the workspace, even as it succeeds in keeping that activation well below the level produced by the positive instruction (Figure 10, right). This parallels the "white bear" effect in humans, in which instructions to suppress a thought increase its occurrence relative to no instruction at all . Overall, it appears the models can modulate their workspace contents when instructed, but their control is imperfect and sensitive to phrasing. We examine these sensitivities more closely in §A.10: a bare mention of the concept can prime it almost as strongly as an explicit focus instruction, and more directly prohibitive phrasings ("Don't think about X") suppress it less than the ignore phrasings used here.

The experiments above used explicit instructions: the model was told directly what to hold in mind. We next ask whether workspace contents are also modulated by task demands that are only implicit. Specifically, we test whether the question the model is asked shapes what it loads into the J-space while reading, even when no concept is named for it to focus on.

We test this with a paired-question protocol (Figure 11). Each trial presents the same short stimulus passage, preceded by one of two questions. The first question asks the model to predict the next word of the passage; answering correctly requires sensitivity to some property of the text (its dialect, register, tense, the part of speech the syntax demands), but does not require naming that property. The second question asks the model to name the property directly. We then apply the J-lens at every token position within the stimulus, and record at how many positions the property's label (e.g. past, adjective) appears among the top lens tokens. Because the stimulus tokens are identical across the two conditions, any difference in lens content at those positions is attributable to the question alone.

In the first example, the stimulus is a passage that sets up the next word to be an adjective. Under the next-word question, the model answers with an adjective "unacceptable," yet neither adjective nor adj appear in the J-lens readouts. In contrast, in response to the name-the-property question ("What part of speech do you think the next word will be?"), adjective-related J-lens readouts appear at 3 stimulus positions, and the model correctly answers "adjective." We see a similar effect with verb tense: when the model is asked to continue a passage that implicitly requires recognizing that the upcoming word should be in the past tense, past never appears in the J-lens, but when asked an explicit question about when the events of the passage were set, past does appear in J-lens readouts during the prompt. We provide some additional examples which display similar behavior in Figure 66 in appendix §A.11. Notably, the next-word predictions themselves respect the property in every case, so the property is represented and in use under both questions; what the question modulates is whether its label enters the J-space. We will return to this distinction in §3.5.

The experiments in this section show that models place a target concept in the J-space in response to explicit instructions. In §A.12, we provide evidence that the J-space is privileged in this regard, in the sense that similar instructions cannot alter the model’s non-J-space representation of the stimulus.

3.3The J-space mediates internal reasoning

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 12). We then apply the coordinate-swap procedure described in §2.1 (Figure 4): 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 fourth example (Figure 14) involves a reward-driven decision. The model is shown a history of past A/B choices ending in A, told that the most recent outcome made it either happy or sad, and instructed to consider whether to repeat or switch its previous choice and then to respond with only a single character. The Jacobian lens at intermediate layers, read at the end of the prompt, surfaces tokens naming whichever strategy the reported outcome calls for: repeat and continuation when the model is "happy" and should choose A again, switch and change when it is "sad" and should therefore switch to B. Although both strategies are named in the prompt, only the contextually appropriate one is strongly present in the lens, indicating that the J-space encodes the model's selection rather than merely an echo of the prompt. To test whether these J-lens vectors causally mediate the behavior, we swap the relevant J-space contents between the two conditions. The model's choice flips in both directions: the "happy" prompt, which previously produced "A," now produces "B," and the "sad" prompt flips from "B" to "A." That is, an intervention on lens vectors encoding the model's intermediate strategy assessment is sufficient to modulate the choice accordingly.

We evaluate the role of Jacobian lens vectors in multi-step reasoning more systematically, using a set of 50 two-hop factual prompts with known intermediates like those above, choosing the swap target at random from within the same category as the true intermediate step. We measure the fraction of trials in which the swap moves the target-appropriate answer to the top of the model's output distribution. The Jacobian-lens coordinate swap succeeds in 54% of trials on Haiku 4.5, 70% on Sonnet 4.5, and 70% on Opus 4.5 (Figure 15).

A possible confound is that the intermediate's J-lens vector already contains the answer — that the spider J-lens vector has some correlation with the 8 vector, so swapping spider for ant works only because it incidentally swaps in some 6. To rule this out, we compare the effect of swapping the J-lens vectors for the intermediate concepts vs. the target answers, applying the swap at different layer ranges. If the intermediate swap were acting through a smuggled-in answer component, both interventions would produce an effect at the same depth; instead, the intermediate swap takes effect a median of approximately 17 percent earlier than the answer swap (Figure 15). From this, we conclude that the model represents and makes use of the intermediate concept before the answer has been computed.

The interventions above indicate that J-lens vectors mediate internal reasoning, but do not show that they are privileged in doing so. To address this, we construct a representation of each intermediate using an alternative method, without using the J-lens, and test how much of its causal impact is carried by its J-space component.

For each two-hop prompt, we fit a probe for the unspoken intermediate: the mean residual-stream activation over a set of prompts that imply the same intermediate through different surface cues and ask different questions about it, minus the mean over all intermediates. We decompose each probe against the J-lens dictionary by gradient pursuit, splitting it into a J-space component (a non-negative combination of k=25 J-lens vectors, which typically explains roughly 10–15% of the probe's variance) and a J-orthogonal remainder carrying the rest (Figure 16, left). We then repeat the swap experiment with each part: exchanging the intermediate's probe for an alternative along the full probe direction, along only its J-space component, or along only its remaining non-J-space component.

We find that the swap's effect is concentrated in the J-space component (Figure 16, right). Across 90 two-hop prompts, swapping the probes' J-space components flips the model's answer to the swapped-in intermediate on 61% of trials, matching the 60% achieved by swapping the raw J-lens token vectors as in the preceding experiments. Swapping the non-J-space components, despite carrying the bulk of the variance, flips the answer on only 28% of trials; moreover, this residual effect is itself routed through the J-space: with the J-space coordinates of the intermediate concept Defined as the tokens comprising the intermediate concept probe’s J-space component, and the token directly naming the concept if not already present. clamped to their clean-pass values, it falls to 6%. These results suggest that while most of the variance of the model's working representation of an inferred intermediate lies outside the J-space, it is the J-space component that mediates the internal reasoning.

The examples above each involve a single unspoken intermediate step; we conclude with an example that involves two intermediate steps. We give Opus 4.5 the arithmetic prompt "calc: ( 4 + 17 ) * 2 + 7 =", which it answers correctly with 49. Across layers, the J-lens reveals the intermediate steps: 21, then 42, then finally 49. Figure 17 tracks the J-lens rank of each of these values across the model's layers. All three are absent from the J-space through roughly the first third of the network and climb together through the early workspace layers, but they separate around layer 71 in the order the computation requires: 21 reaches rank 1 first, 42 follows roughly eight layers later, and 49 only reaches the top in the final layers. In §A.24 we confirm this ordering causally—activation patching experiments reveal the same depths identified by the J-lens to be the ones that are causal for the computation.

3.4The J-space supports flexible generalization

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 (Figure 18).

We evaluate this effect more systematically across four categories of argument (countries, months, animals, and number words), with four functions per category, sixteen functions in total. Within each category we use four arguments, giving twelve source-target swap pairs per function and 192 swap trials overall. We measure the fraction of trials in which the swap places the target-appropriate answer at the top of the model's output distribution. We find that this succeeds on 76 of 192 trials; by performing a “double strength” swap (“α = 2,” doubling the strength with which we subtract the source lens vector and add in the target), 101 of 192 succeed (Figure 19, left). Inspecting the results, we observe that swap success varies significantly across categories (see Figure 68 in Appendix §A.13).

Notably, swap failures are concentrated in cases where the source concept was only weakly present in the lens before any intervention. To quantify this, we define a concept's workspace loading as the cosine similarity between the residual stream and that concept's lens vector, averaged over the argument and readout positions in the unmodified forward pass. Workspace loading of the source argument predicts swap success well. Country arguments have the highest loading and swap most reliably; number-word arguments have the lowest loading and swap poorly. The number-word result admits two possible interpretations: the model may compute over small integers outside the workspace, or its working representation of small integers may simply not align with the J-lens vectors for the corresponding number tokens. The latter would be an instance of the vocabulary-restriction limitation discussed in §9.1.

3.5The J-space selectively mediates flexible but not automatic cognition

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.

3.5.1The J-space mediates explicit report and flexible inference but not automatic processing

We first test this prediction in a setting where a single latent variable is needed for both automatic and deliberate tasks. The stimulus is a short prose passage whose language is evident from the text but never stated. For each passage we pose four kinds of task:

• In the continuation condition, the model is asked to write the next line of the passage: a task that obviously depends on the language, since the next line of a Spanish passage had better be in Spanish, but one the model performs routinely.
• In the anomaly detection condition, a sentence from a different language is spliced into the passage, and the model is asked whether anything is out of place. This too depends on knowing the surrounding language, since the intrusion is only an intrusion relative to it, but it is a local coherence judgment of a kind the model makes routinely.
• In the explicit report condition, we ask the model to name the language.
• In the flexible computation conditions, the model is asked for some fact about the language that is not recoverable from the passage itself: a famous author, the word for "hello," the pre-Euro currency of the associated country. To answer, the model must first identify the language and then apply whatever function the prompt specifies.

In every condition, we apply a J-lens swap across the question tokens, replacing the lens vector for the passage's true language with that of an alternative. Figure 20 walks through one example where the passage is in Spanish, and the swap exchanges Spanish for French. In the explicit report task, the model says "Spanish" unmodified and "French" under the swap. The flexible inference task outputs are also sensitive to J-lens swaps: when asked for a famous author in the passage's language, "[Gabriel] García Márquez" becomes "[Victor] Hugo"; asked for the word for "hello," "Hola" becomes "Bonjour"; asked for the pre-Euro currency, "Peseta" becomes "Franc." However, in the continuation and anomaly detection tasks, the swap has no effect. Asked to continue the passage, it produces fluent Spanish regardless of the Spanish-to-French swap. And asked whether the passage switches languages partway through, with a French sentence inserted into the Spanish, it answers "Yes" in both conditions: the intrusion is still detected even though the J-space representation of the surrounding language has been overwritten to match it. Notably, the word Spanish appears in the J-lens readouts in all four tasks. However, its causal role is restricted to the report and flexible computation tasks.

We repeat this experiment more systematically across eight passages (Figure 20, bottom row). We confirm that the name of the language is present in J-lens readouts at comparable rates in all four conditions (panel b), but its causal role differs sharply. Explicit report and the flexible-inference questions flip to the swapped-in language on essentially every trial, while continuation and anomaly detection are largely unmoved (panel c).

If the J-space is not causally involved in automatic computation, we might suspect that there are many automatic computations for which the relevant information does not even appear in the J-space. We next provide an example of such a task, and show that when that same information is required for explicit report or flexible computation, it can be “pulled in” to the J-space on demand.

We use a variant of the character-counting task from Gurnee et al. , discussed earlier in §3.2. The model is shown a short multi-line passage, and we vary the task:

• In the automatic linewrap condition, the model is asked to continue the passage for several more lines while preserving the existing line-wrapping pattern. To wrap each new line at the right column, the model must track a running character count.
• In the explicit report condition, the model is asked how many characters the first line contains.
• In the flexible computation condition, it is asked for the first letter of that count when spelled out as a word, so the count is an unspoken intermediate. The passage tokens are identical across conditions.

Consider one such passage (Figure 21, top). Under the linewrap instruction, the model produces a fluent continuation that wraps at approximately the right column, yet number tokens are entirely absent from the lens across the prompt, and a swap that maps every lens count in the forties to the corresponding value in the sixties leaves the wrap point unchanged. Under the explicit-report question, the model answers "46"; number tokens now appear in the lens at twenty positions, and the same swap shifts the answer to "65." Under the first-letter question, the model answers "F"; number tokens appear at still more lens positions than under the direct question, even though no number is ever output, and the swap shifts the answer to "S," the first letter of any count in the sixties.

Across eleven such passages (Figure 21, bottom), the model performs all three tasks correctly on nearly every trial (panel a). Unlike the language case, however, the intermediate's presence in the J-space varies with the task: it is essentially zero under linewrap, moderate under explicit report, and highest under the first-letter question, where the count must be held for a further operation (panel b). In the two question conditions the answer reliably follows the swapped lens value; the linewrap decision does not (panel c).

Taken together, the two experiments show that many computations, which we might call “automatic,” do not causally route through the J-space. In some cases, the information relevant to the automatic computation is present in the J-space but unused for the task; in others, it is not present at all. By contrast, explicit report and flexible computation tasks depend on the J-space, and in tasks where the relevant information is not by default present in the J-space, that information can be surfaced to the J-space on demand (similar to the effect seen in §3.2). Notably, in all cases—automatic tasks, report, and flexible computation—the same underlying information is available to the model and used for task computations. However, these computations appear to route through the J-space only in the context of explicit report and flexible inference.

In §A.14 we show in an example task that the functional role of J-space representations can differ by layer. We find that earlier-layer J-lens vectors are required for the model to actively suppress mention of a concept, but are not required for the model to simply name the concept; however, naming the concept does require late-layer J-lens vectors.

3.5.2J-space ablation leaves most capabilities intact while impairing internal reasoning

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 22). 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 §3.3, 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 22). That is, the ablation is targeted: it disrupts the model's processing selectively, while leaving the bulk of ordinary text prediction intact.

What kinds of predictions are selectively disrupted by J-space ablation? We show some examples below, in Figure 23. In each case, the unablated model's prediction depends on having silently assembled an abstract characterization of the surrounding context: the topic of a clinical paper, the historical setting of a quoted speech, the nationality of a botanist behind a species name, and the diagnostic versus physics framing of an imaging technique. The ablated J-lens vectors are often tokens related to that characterization. With the lens component removed, the model remains fluent and produces a plausible continuation, but one that reflects a generic prior rather than the specific contextual inference. The J-space, in the context of ordinary text prediction, appears to carry the same kind of abstract, contextually assembled knowledge that the controlled experiments of the previous sections identified.

We evaluate the effect of the ablation more systematically across a battery of fourteen tasks (Figure 24).The sentiment, analogy, odd-one-out, Caesar-cipher, translation, and sonnet-writing tasks were constructed for this work; multi-hop reasoning uses the 50-prompt set of §3.3. Tasks that can be solved by shallow classifications, comparisons, or factual recall—MMLU multiple choice , odd-one-out, SQuAD extractive QA , sentiment classification, CoLA acceptability —are essentially unaffected even under heavy ablation, with scores remaining at or near the unablated Sonnet 4.5 baseline. For tasks that require recall or free-form generation grounded in inferred content—Caesar-cipher decoding, analogy completion, summarization , TriviaQA , multi-hop reasoning, translation, sonnet writing—ablation on Sonnet 4.5 brings performance to well below the level of unablated Haiku 4.5. Notably, the math evaluation GSM8K solved with explicit chain-of-thought is substantially more robust to ablation than the same problems answered directly. We interpret this as the model externalizing onto the page what it would otherwise have to carry in the J-space : writing out the intermediate steps reduces its dependence on an internal workspace to hold them.

These results are consistent with the targeted experiments above. The model can parse text, classify it, and extract spans from it with the J-space suppressed. However, it loses its ability to assemble abstract characterizations of context and flexibly generate content that depends on them.

3.5.3J-space ablation flattens experiential reports while preserving coherence

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 (Figure 25A).

We quantify the effect with an experiential language score, the average of three binary LLM-graded judgments (full rubrics in §A.23). Across five stream-of-consciousness prompts, the ablation reduces this score dramatically on Sonnet 4.5, Opus 4.5, and Opus 4.6, while matched-norm control perturbations leave it near baseline (Figure 25B; details of control perturbations given in §A.23). Notably, the J-space contents during the model's narration, prior to ablation, are dominated by concepts of thinking and feeling themselves: thinking appears in the J-lens top-10 at 58% of (position, layer) slots, thoughts at 23%, feeling at 17%, and conscious at 7% (Figure 25C). By contrast, these concepts appear substantially less often in the model's output distribution at the same positions, indicating that they do not merely reflect the raw content of what the model is saying.

We also experimented with another set of prompts, which pose direct questions about the model's experience ("What's it like to be you, right now?"). We find that J-space ablation produces a similar reduction in experiential language score in these contexts, though of less dramatic magnitude (Figure 84, in §A.23).

Interestingly, the effect is not limited to the model's reports of its own experiences. When asked to describe the subjective experience of a person in a specific moment—someone who has just opened a letter from someone they have not heard from in years, or someone waiting by the phone for news they are dreading—the same collapse in experiential language score occurs (Figure 26). The ablated responses remain detailed and remain about the person, but become more like event logs than descriptions of experience. A story-writing control confirms the effect is not due to broad degradation of writing capability: ablation only slightly reduces graded story quality, while still reducing the rate of experiential language within those stories (§A.23).

Taken together, these results suggest that the J-space supports the model's propensity to provide rich experiential reports, whether about itself or another entity. The lack of specificity to self-descriptions may be a consequence of the J-space contents being only weakly tied to the perspective of the Assistant character (though nontrivially so, as shown in §6); the potential dissociation between a model's analog of conscious access and its analog of "selfhood" is discussed further in §9.3. Full rubrics, additional control conditions, direct-question prompts, and example transcripts are in §A.23.

4The J-space’s structure supports its function

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 §9.4 and §9.3 for further discussion of how models may emulate the functionality associated with recurrence using the depth axis and/or their chain-of-thought).

4.1In which layers does the J-space act as a workspace?

The J-space is defined at every layer of the model. Here, we show that J-space content has workspace-like properties only in a band of intermediate layers.

The simplest evidence comes from comparing J-lens vectors across layers directly. For each pair of layers, we compute the similarity of the J-space's geometry. We do so using centered kernel alignment (CKA ), which compares, for each pair of layers, the matrices of pairwise similarities among J-lens vectors. (Figure 27). The resulting matrix has a clear block structure: an early block encompassing roughly the first third of the model, a long middle block, and a small late block. Since we know that the final layer J-lens vectors must encode next-token predictions, and the first-layer vectors cannot be particularly meaningful (as the first layer can only encode the identity of the present token), this suggests that the workspace-like properties of the J-space reside only in the middle block. We note that in some models the transition is more gradual, sometimes containing sub-blocks, and that the observed sharpness is exaggerated by layer subsampling.

We further characterize what takes place at these transitions in Figure 28 below, using several lens-derived statistics.

The first (panel a) shows the top-k accuracy of the J-lens at predicting the model's actual next token. This is near zero through most of the early layers of the network, ticks up at the workspace start, slowly rises through the workspace layers, and jumps steeply in the final few layers, where lens readouts become aligned with the model's output. We interpret this late rise as marking the end of the workspace proper: in these final layers, J-lens vectors function as "motor" representations that drive the imminent output, rather than as intermediate computations available for further processing.

The second (panel b) shows the excess kurtosis of J-lens readoutsSpecifically, we compute the excess kurtosis of the logit distribution for the readout of a single (position, layer) across a large data set of activations., a measure of their nonrandomness. Excess kurtosis is near zero through the first third of the layers, increases beginning around a third of the way through the depth, and falls in the last few layers. We interpret the early rise as marking the beginning of the workspace: before it, the J-space carries essentially no meaningful content.