Format Web Page "Misra and collaborators trained transformers on hidden modular recurrence rules that predicted well at supervised positions but failed at the first unsupervised position." View Source Location PART 3 - THE THIRD AXIS CHAPTER 14: AGI & THE SYNTHESIZER Can You Feel the AGI Now? Source title The Wall Between Shannon and Kolmogorov Source authors Vishal Misra Source year 2026 Confidence mid Importance high
Format Web Page "Vishal Misra distinguishes Shannon-style statistical pattern learning from Kolmogorov-style identification of compact generating mechanisms." View Source Location PART 3 - THE THIRD AXIS CHAPTER 14: AGI & THE SYNTHESIZER Can You Feel the AGI Now? Source title Shannon Got AI This Far. Kolmogorov Shows Where It Stops. Source authors Vishal Misra Source year 2026 Confidence high Importance high
Format Web Page "The OthelloGPT heuristic follow-up was conducted within Neel Nanda’s MATS 6.0 training programme." View Source Location PART 3 - THE THIRD AXIS CHAPTER 14: AGI & THE SYNTHESIZER Can You Feel the AGI Now? Source title OthelloGPT learned a bag of heuristics Source authors Jennifer Lin, JackS, Adam Karvonen, Can Source year 2024 Confidence high Importance mid
Format Web Page "OthelloGPT may calculate board states using many localized decision rules rather than a single simple, general algorithm." View Source Location PART 3 - THE THIRD AXIS CHAPTER 14: AGI & THE SYNTHESIZER Can You Feel the AGI Now? Source title OthelloGPT learned a bag of heuristics Source authors Jennifer Lin, JackS, Adam Karvonen, Can Source year 2024 Confidence high Importance high
Format Web Page "Mitchell describes later OthelloGPT research as evidence that encoded board states may arise from localized heuristics rather than a compact, general-purpose simulator." View Source Location PART 3 - THE THIRD AXIS CHAPTER 14: AGI & THE SYNTHESIZER Can You Feel the AGI Now? Source title LLMs and World Models, Part 2 Source authors Melanie Mitchell Source year 2025 Confidence high Importance high
Format Web Page "Melanie Mitchell identifies the pair of Othello studies as prominent evidence offered for emergent world models in transformers." View Source Location PART 3 - THE THIRD AXIS CHAPTER 14: AGI & THE SYNTHESIZER Can You Feel the AGI Now? Source title LLMs and World Models, Part 2 Source authors Melanie Mitchell Source year 2025 Confidence high Importance mid
Format Online paper / PDF "Nanda and collaborators found a readily decodable linear representation of Othello’s board state using the active player’s perspective." View Source Location PART 3 - THE THIRD AXIS CHAPTER 14: AGI & THE SYNTHESIZER Can You Feel the AGI Now? Source title Emergent Linear Representations in World Models of Self-Supervised Sequence Models Source authors Neel Nanda, Andrew Lee, Martin Wattenberg Source year 2023 Confidence high Importance high
Format Online paper / PDF "OthelloGPT, trained to predict legal Othello moves from move sequences, developed a causally relevant internal representation of the board state." View Source Location PART 3 - THE THIRD AXIS CHAPTER 14: AGI & THE SYNTHESIZER Can You Feel the AGI Now? Source title Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task Source authors Kenneth Li et al. Source year 2023 Confidence high Importance high
Format Web Page "Trenton Bricken cited OthelloGPT as evidence that a transformer can learn a board representation from legal move sequences." View Source Location PART 3 - THE THIRD AXIS CHAPTER 14: AGI & THE SYNTHESIZER Can You Feel the AGI Now? Source title Sholto Douglas & Trenton Bricken — How LLMs actually think Source authors Dwarkesh Patel Source year 2024 Confidence high Importance mid
Format Video / YouTube Embed "Trenton Bricken cited OthelloGPT as evidence that a transformer can learn a board representation from legal move sequences." Location PART 3 - THE THIRD AXIS CHAPTER 14: AGI & THE SYNTHESIZER Can You Feel the AGI Now? Source title Sholto Douglas & Trenton Bricken — How LLMs actually think Source authors Dwarkesh Patel Source year 2024 Confidence high Importance mid
