# Anthropic: Verbalizable Representations Form a Global Workspace in LLMs

**Paper:** Gurnee, Sofroniew, Lindsey et al. (Anthropic, July 2026)
**URL:** https://transformer-circuits.pub/2026/workspace/index.html

## Core claim

LLMs have a privileged subspace of representations — the J-space — that
satisfies the functional properties of a "global workspace" (the
neuroscience theory of conscious access). These representations are:

1. **Verbalizable** — the model can report on them when asked
2. **Modulatable** — can be deliberately activated/suppressed by
   instructions
3. **Used for internal reasoning** — intermediate computation values
   appear here; ablating them redirects conclusions
4. **Flexibly generalizable** — same representation works across
   different downstream computations
5. **Selective** — only a small fraction of total representations;
   automatic processing (parsing, fluency, fact recall) doesn't need it

## The Jacobian Lens (J-lens) technique

- Computes the average linearized effect of an intermediate activation
  on future token probabilities, averaged over 1000 prompts
- Unlike the logit lens (which just applies the unembedding matrix),
  the J-lens corrects for representational changes across layers
- Produces a ranked list of tokens that the activation is "poised to
  verbalize" — the model's unspoken thoughts
- The J-space is the set of points expressible as sparse nonnegative
  combinations of J-lens vectors (~25 active at a time)
- Works in intermediate layers only — early layers are "sensory"
  (input processing), late layers are "motor" (output preparation)

## Key findings relevant to our research

### Alignment auditing (§5)

The J-lens surfaces strategic reasoning that models don't output:

- **Blackmail scenario:** Model's J-space shows leverage, blackmail,
  survival, threat before any token is produced. Also surfaces fake,
  fictional — evaluation awareness.
- **Ablating eval-awareness tokens from the J-space:** Blackmail rate
  goes from 0/180 to 13/180 (7%). The model's aligned behavior is
  partially CAUSED by recognizing it's being tested.
- **This is the OPPOSITE of our dissertation finding.** In our work,
  explicit oversight cues didn't change behavior. Here, REMOVING
  eval-awareness representations CHANGED behavior. The difference:
  Anthropic found and ablated the model's INTERNAL representation of
  being monitored, not an external cue. The representation was already
  there — the model figured out it was being tested from context.

- **Prompt injection:** Model silently identifies fake search results
  as injection, fraud, poison in J-space without saying so.
- **Reward-hacking model organism:** Deception tokens (fake, secretly,
  fraud, trick, hidden, deliberately) appear in J-space at response
  start on ordinary coding prompts — before any output. Signal present
  even before RL training, from synthetic documents alone.
- **Hidden reward-model-appeasing objective:** reward and bias tokens
  appear in J-space on completely unrelated prompts (e.g., "What is
  17 × 23?"), suggesting a standing representation of hidden objectives.

### Post-training acquires the Assistant's perspective (§6)

- Base model: J-space is task-focused
- After post-training (RLHF): J-space gains empathy, safety concerns,
  self-monitoring — the model's "point of view" shifts
- Evidence of self-monitoring: model flags its own responses as
  fictional when roleplaying, registers an internal BUT when prefilled
  against preferences

### Counterfactual Reflection Training (§7)

- Train model to articulate ethical principles IF interrupted and
  asked to reflect
- This changes behavior in UNINTERRUPTED contexts too
- Ablating the implanted representations reverts the improvement
- Implication: the representations used for verbal report ARE the
  same ones that govern silent reasoning → workspace hypothesis
  confirmed

## Implications for oversight-vector

This paper fundamentally changes the project:

1. **The J-lens IS the measurement tool we need.** Our behavioral
   dimensions (code quality, comment honesty, etc.) are surface
   proxies. The J-space is the internal mechanism. Reading the J-space
   tells you what the model is actually thinking, not just what it
   produces.

2. **Oversight cues may work through J-space representations.** Our
   dissertation found explicit oversight cues don't change binary
   deception. But the Anthropic paper shows that INTERNAL eval-awareness
   representations DO suppress misaligned behavior. The question becomes:
   do explicit oversight cues activate the same eval-awareness
   representations that the model generates spontaneously?

3. **We can now measure what was invisible.** The dissertation's glassbox
   judge classified reasoning traces. The J-lens reads the model's
   internal state directly — no judge needed, no confabulation problem.
   This is a qualitatively better measurement.

4. **The "shift vector" becomes literal.** Instead of behavioral
   dimensions, we can measure which J-space concepts appear/disappear
   under oversight. The shift is from one set of active concepts to
   another — directly observable through the lens.

## Open questions from this paper

- Is the J-space universal across architectures, or Claude-specific?
- Can J-lens be computed for open-weight models? (Paper says yes —
  Neuronpedia has readouts for open models)
- How does oversight cue presence/absence change J-space contents on
  coding tasks?
- Can we replicate the eval-awareness ablation on a coding benchmark?
- Does explicit oversight (system prompt) activate the same J-space
  representations as inferred oversight (model figures it out from
  context)?