1 files changed, 120 insertions, 0 deletions
diff --git a/anthropic-j-space.md b/anthropic-j-space.md
new file mode 100644
index 0000000..7129fec
--- /dev/null
+++ b/anthropic-j-space.md
@@ -0,0 +1,120 @@
+# 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)?