This episode explores Anthropic’s paper on whether language models contain a privileged “verbalizable” subspace, functionally similar to a global workspace, whose contents can be reported, reasoned over, and deliberately controlled. It draws a clear line between access consciousness and phenomenal consciousness, then focuses on the paper’s mechanistic proposal: a Jacobian-based “J-space” that identifies internal directions causally poised to become language rather than merely easy to decode. The discussion highlights intervention results showing that swapping or ablating directions such as France/China or Soccer/Rugby changes later reports and multi-step reasoning, with broader examples in code bug detection, prompt-injection recognition, and protein-function judgments. Listeners would find it interesting because it turns a consciousness-adjacent question into a concrete engineering argument about whether models have a small, reusable internal workspace that shapes what they know, say, and do.
Sources:
1. Verbalizable Representations and the Global Workspace
https://transformer-circuits.pub/2026/workspace/index.html
2. Verbalizable Representations Form a Global Workspace in Language Models — Wes Gurnee, Nicholas Sofroniew, Jack Lindsey, Adam Pearce, Mateusz Piotrowski, et al., 2026
https://scholar.google.com/scholar?q=Verbalizable+Representations+Form+a+Global+Workspace+in+Language+Models
3. Eliciting Latent Predictions from Transformers with the Tuned Lens — Nora Belrose, Igor Ostrovsky, Lev McKinney, Zach Furman, Logan Smith, Danny Halawi, Stella Biderman, Jacob Steinhardt, 2023
https://scholar.google.com/scholar?q=Eliciting+Latent+Predictions+from+Transformers+with+the+Tuned+Lens
4. Sparse Autoencoders Find Highly Interpretable Features in Language Models — Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, Lee Sharkey, 2023
https://scholar.google.com/scholar?q=Sparse+Autoencoders+Find+Highly+Interpretable+Features+in+Language+Models
5. Do Activation Verbalization Methods Convey Privileged Information? — Millicent Li, Alberto Mario Ceballos Arroyo, Giordano Rogers, Naomi Saphra, Byron C. Wallace, 2026
https://scholar.google.com/scholar?q=Do+Activation+Verbalization+Methods+Convey+Privileged+Information?
6. Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet — Adly Templeton, Tom Conerly, Jonathan Marcus, Jack Lindsey, Trenton Bricken, Brian Chen, Adam Pearce, Craig Citro, Emmanuel Ameisen, Andy Jones, Hoagy Cunningham, Nicholas L. Turner, Callum McDougall, Monte MacDiarmid, Alex Tamkin, Esin Durmus, Tristan Hume, Francesco Mosconi, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, Tom Henighan, 2026
https://scholar.google.com/scholar?q=Scaling+Monosemanticity:+Extracting+Interpretable+Features+from+Claude+3+Sonnet
7. Implicit Representations of Meaning in Neural Language Models — Belinda Z. Li, Maxwell Nye, Jacob Andreas, 2021
https://scholar.google.com/scholar?q=Implicit+Representations+of+Meaning+in+Neural+Language+Models
8. On the Biology of a Large Language Model — Jack Lindsey, Wes Gurnee, Emmanuel Ameisen, et al., 2025
https://scholar.google.com/scholar?q=On+the+Biology+of+a+Large+Language+Model
9. A Neuronal Model of a Global Workspace in Effortful Cognitive Tasks — Stanislas Dehaene, Serge Kerszberg, Jean-Pierre Changeux, 1998
https://scholar.google.com/scholar?q=A+Neuronal+Model+of+a+Global+Workspace+in+Effortful+Cognitive+Tasks
10. Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding — Haolin Chen et al., 2024
https://scholar.google.com/scholar?q=Language+Models+are+Hidden+Reasoners:+Unlocking+Latent+Reasoning+Capabilities+via+Self-Rewarding
11. Efficient Post-Training Refinement of Latent Reasoning in Large Language Models — Xinyuan Wang et al., 2025
https://scholar.google.com/scholar?q=Efficient+Post-Training+Refinement+of+Latent+Reasoning+in+Large+Language+Models
12. SeLaR: Selective Latent Reasoning in Large Language Models — Renyu Fu and Guibo Luo, 2026
https://scholar.google.com/scholar?q=SeLaR:+Selective+Latent+Reasoning+in+Large+Language+Models
13. Measuring Faithfulness in Chain-of-Thought Reasoning — Tamera Lanham et al., 2023
https://scholar.google.com/scholar?q=Measuring+Faithfulness+in+Chain-of-Thought+Reasoning
14. Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps — Martin Tutek et al., 2025
https://scholar.google.com/scholar?q=Measuring+Chain+of+Thought+Faithfulness+by+Unlearning+Reasoning+Steps
15. Why Models Know But Don't Say: Chain-of-Thought Faithfulness Divergence Between Thinking Tokens and Answers in Open-Weight Reasoning Models — Richard J. Young, 2026
https://scholar.google.com/scholar?q=Why+Models+Know+But+Don't+Say:+Chain-of-Thought+Faithfulness+Divergence+Between+Thinking+Tokens+and+Answers+in+Open-Weight+Reasoning+Models
16. Steering Language Models With Activation Engineering — Alexander Matt Turner et al., 2023
https://scholar.google.com/scholar?q=Steering+Language+Models+With+Activation+Engineering
17. Improving Instruction-Following in Language Models through Activation Steering — Alessandro Stolfo et al., 2024
https://scholar.google.com/scholar?q=Improving+Instruction-Following+in+Language+Models+through+Activation+Steering
18. Mitigating Content Effects on Reasoning in Language Models through Fine-Grained Activation Steering — Marco Valentino et al., 2025
https://scholar.google.com/scholar?q=Mitigating+Content+Effects+on+Reasoning+in+Language+Models+through+Fine-Grained+Activation+Steering
19. AI Post Transformers: How Models Detect Hidden Activation Steering — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-08-how-models-detect-hidden-activation-stee-577f73.mp3
20. AI Post Transformers: Neural Chameleons and Evading Activation Monitors — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-14-neural-chameleons-and-evading-activation-bc470e.mp3
21. AI Post Transformers: Why Transformers Fail at Counting — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-08-why-transformers-fail-at-counting-137924.mp3
22. AI Post Transformers: RAPTOR: Stable Concept Directions From Logistic Probes — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-08-raptor-stable-concept-directions-from-lo-b37365.mp3
Interactive Visualization: Verbalizable Representations and the Global Workspace
