“A Review of Anthropic’s Global Workspace Paper” by Neel Nanda

The below is a public review Anthropic asked me to write for their new global workspace paper. I recommend at least skimming their paper first. TLDR: I think this is a fantastic paper - it presents compelling evidence for some kind of "cognitive space" in models, that is used as a "working memory" for intermediate variables during a forward pass, shows that J-Lens is a useful technique for accessing this space. I believe these key claims.I believe J-Lens will be a useful (but limited) tool in practice for model forensics, e.g. generating hypotheses about unusual model behaviour during alignment audits.I discuss my mental models for why a cognitive space should exist, and first principles arguments for why J-Lens should work for accessing itI assess the paper's evidence that this cognitive space exists, and the paper's evidence that J-Lens is practically useful.We have replicated the core claims on Qwen 3.6 27B, and also share preliminary evidence of extending this work by finding abstract "interpretative meta-tokens", like Chinese characters for "what does this mean" that seem to activate and play a causal role on processing ambiguous sentences. What claims is this paper making? In my opinion this [...] ---Outline:(01:27) What claims is this paper making?(05:32) Why does J-Lens work? First principles reasoning(06:03) Why have a "working memory"?(07:59) Worked Example: Factual Recall(09:47) Why have consistent directions for concepts?(10:41) Why are tokens relevant?(11:35) Why are intermediate concepts related to output logits?(14:24) J-Lens is an approximation, but a useful one(16:26) Why Jacobians rather than linear regression?(17:33) What does this working memory actually give us?(18:18) Assessment of evidence for the existence of a cognitive space(19:21) Multihop factual recall(21:20) Other multihop causal interventions(24:17) Further Musings(25:16) Is J-Lens useful?(28:34) Blackmail (5.1)(29:11) Prompt injection (5.2)(29:56) Monitoring for hidden deception (5.3)(30:24) Emergent misalignment (5.4)(30:41) Reward model appeasing (5.5)(31:23) Measuring eval awareness (A.21)(32:17) Equipping an automated auditing agent with J-Lens (A.22)(33:15) Replicating J-Lens and Interpretative Meta-Tokens(34:02) Replication(37:50) Cost and Difficulty of Replicating J-Lens(39:42) Case Study: Interpretative Meta-Tokens(41:49) Where do interpretative meta-tokens appear?(43:52) Are interpretative meta-tokens causal?(49:04) Implications --- First published: July 6th, 2026 Source: https://www.lesswrong.com/posts/zFJ3ZdQwrTWE9jT5S/a-review-of-anthropic-s-global-workspace-paper --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.