He Co-Invented the Transformer. Now: Continuous Thought Machines [Llion Jones / Luke Darlow]

Too Long; Didn't Watch — Summary

Llion Jones, co-inventor of the Transformer, argues that the AI industry is hindered by the Transformer's success, leading to a "local minimum" of innovation. He and Luke Darlow introduce Sakana AI's Continuous Thought Machine (CTM), a biology-inspired architecture designed for adaptive, sequential reasoning, which they believe offers a path beyond current limitations, exemplified by its performance on a new, challenging Sudoku reasoning benchmark.

Main Takeaways

• The AI industry is experiencing "success capture" with Transformers, leading to incremental tweaks rather than fundamental architectural breakthroughs.
• Current AI models often "fake" understanding, solving problems through brute-force approximation rather than true conceptual grasp, resulting in "jagged intelligence."
• Sakana AI promotes research freedom, inspired by Kenneth Stanley's philosophy, to encourage speculative, long-term architectural exploration.
• The Continuous Thought Machine (CTM) is a novel, biologically inspired recurrent model featuring adaptive computation, neuron-level models, and synchronization as a representation of thought.
• The CTM demonstrates human-like sequential reasoning, adaptive thinking time, and superior model calibration, as shown in maze-solving and ImageNet tasks.
• The new Sudoku Bench benchmark, derived from human thought traces, reveals the limitations of current LLMs in complex, nuanced reasoning, highlighting the need for architectures like the CTM.

Detailed Summary

Stepping Back from Transformers (0:00 - 1:09)

• Llion Jones, a co-inventor of the Transformer, has decided to significantly reduce his research on them due to the field being oversaturated.
• He believes the industry is stuck in a "local minimum" where everyone focuses on minor modifications to the existing architecture.
• The Continuous Thought Machine (CTM) is introduced as a new recurrent model with native adaptive compute, using higher-level neuron concepts and synchronization for biologically inspired problem-solving.
• The CTM received a spotlight at NeurIPS 2025.

The Changing Atmosphere of AI Research (1:09 - 4:13)

• AI research during the Transformer's inception was characterized by more freedom and bottom-up innovation, with ideas emerging from informal discussions.
• The current environment has reduced freedom, with pressures to publish and commercialize, hindering exploration of novel architectures.
• Llion Jones recounts pitching evolutionary search algorithms in an environment focused solely on Transformers, receiving no interest, which led him to start his own company.
• The discussion references Kenneth Stanley's book, "Why Greatness Cannot Be Planned," advocating for research driven by interest rather than strict objectives.

Sakana’s Philosophy: Research Freedom (4:13 - 7:45)

• Sakana AI embraces the philosophy of research freedom, actively protecting its researchers from external pressures.
• As companies grow, pressures for returns on investment and product development often curtail creativity and autonomy in research.
• Llion Jones emphasizes that he encourages Sakana AI employees to work on what they find interesting and important.

The Local Minimum of Large Language Models (7:45 - 18:30)

• The success of large language models (LLMs) has led to a "technology capture," where the industry is overly focused on them, potentially trapping it in a local minimum.
• A parallel is drawn to the era of recurrent neural networks (RNNs), where incremental improvements were made until Transformers provided a "crushingly better" alternative, rendering previous research redundant.
• The current AI landscape sees many papers making slight modifications to the Transformer architecture, which might be a waste of time if a new breakthrough emerges.
• Llion Jones argues that being merely "better" than Transformers is not enough; a new architecture needs to be overwhelmingly superior to shift the industry.
• The concept of "jagged intelligence" is introduced, where LLMs can solve complex problems but also make glaringly obvious errors, suggesting a fundamental flaw in their architecture.

Representation Problems: The Spiral Example (18:30 - 29:12)

• Current neural networks are powerful but may not "want" to represent things in a human-like way, acting as universal approximators that are forced to fit data.
• An obscure paper on "intelligent matrix exponentiation" is cited, showing how a model could represent a spiral dataset as an actual spiral, unlike ReLU MLPs that use piecewise linear approximations.
• This highlights that current models "fake" understanding by fitting data without grasping the underlying concept, leading to poor extrapolation.
• The discussion touches on the "impostor" nature of AI, where models seem intelligent but lack fundamental properties like adaptive computation and constructive learning.
• The speakers express skepticism about video generation models that fix issues like extra fingers through brute-force scaling rather than a deeper, conceptual understanding.

Technical Deep Dive: CTM Architecture (29:12 - 36:00)

• Luke Darlow introduces the Continuous Thought Machine (CTM), an 8-month project at Sakana AI.
• The CTM has three novelties: an internal thought dimension for sequential computation, neurons as individual models (MLPs), and synchronization as a representation of thought over time.
• The maze-solving task is used as a "hello world" problem for CTM, where it solves mazes sequentially, step-by-step, unlike traditional deep learning that processes the entire image at once.
• The CTM's neurons are not simple ReLU activations but rather "neuron level models" that use a finite history of activations to process a single output, building dynamics into the system.
• Synchronization is measured by the dot product between the time series of neuron activations, capturing how neurons fire together over time.

Adaptive Computation & Maze Solving (36:00 - 47:15)

• The CTM uses an autocurriculum system for maze solving, where it learns to predict one more step than it currently can, enabling self-bootstrapping.
• The model observes the full maze image at every step but uses attention to retrieve information and has a fixed number of "thinking steps" (e.g., 100).
• For ImageNet classification, the CTM's loss function encourages adaptive computation by optimizing for both best performance and highest certainty, leading to easy examples being solved quickly and harder ones taking more thought.
• The CTM's stability is attributed to synchronization, which helps with gradient propagation by touching all neurons through all time.
• The use of exponential decay rates in synchronization allows for capturing both short-term and long-term firing patterns between neurons, enriching the representation space.
• The CTM's sequential reasoning and internal thought process are seen as potentially superior to Transformers for tasks requiring planning and discrete reasoning, like the ARC challenge.

Model Calibration & Uncertainty (47:15 - 1:00:43)

• Neural networks are often poorly calibrated, meaning their predicted probabilities don't accurately reflect their confidence.
• The CTM, after training, was found to be nearly perfectly calibrated, which was an emergent property rather than an explicit design goal.
• This adaptive computation time in CTM emerged naturally from the loss function design, unlike previous attempts that required explicit penalties for computation.
• The CTM's ability to build understanding step-by-step and explore trajectories in spaces is seen as crucial for agents that can continuously learn and construct path-dependent knowledge.
• The architecture's approach to carving up the world in different ways could address issues like hallucinations in language models.
• The CTM's design, inspired by biology, allows for different ways of approaching problems to emerge, leading to philosophical questions about intelligence.
• Luke Darlow suggests that language itself can be viewed as a maze, implying the CTM's potential for next-generation language models.
• During training, the CTM was observed to backtrack and explore multiple paths in mazes, a behavior not typically seen in current AI models.
• When constrained for time, the CTM exhibited a "leapfrogging" behavior in mazes, quickly jumping ahead and tracing paths backward, suggesting different algorithmic strategies under constraint.
• The speakers discuss the potential for scaling out CTMs with shared memory structures, creating a "cultural memory" for collective intelligence, which is an active area of research.

Sudoku Bench: Measuring True Reasoning (1:00:43 - 1:13:28)

• Llion Jones introduces the Sudoku Bench, a new benchmark dataset for reasoning, designed to be more challenging than standard Sudoku.
• The benchmark features "variant Sudokus" with highly diverse, handcrafted rules, often requiring complex natural language understanding and even meta-reasoning (e.g., identifying a wrong number in the rule description).
• Current best models achieve only about 15% on the Sudoku Bench, solving only the simplest puzzles, and GPT-5 also struggles with it.
• The dataset was created by scraping thousands of hours of human thought traces from the YouTube channel "Cracking the Cryptic," where experts verbalize their reasoning processes.
• This provides high-quality human reasoning data for imitation learning, addressing Andrej Karpathy's idea of learning from "thought traces."
• The Sudoku Bench is designed to be difficult because each puzzle requires finding a new and unique "break-in" (a specific reasoning step) that current AI models cannot generalize to.
• The "tree of reasoning" analogy is used to describe the vast and complex space of problem-solving, with humans and AI models possessing different parts of this tree.
• Current RL algorithms struggle with the Sudoku Bench because the required breakthroughs are in a "rare space," making sampling inefficient.
• Sakana AI is actively hiring ML engineers and scientists, offering significant research freedom.

Notable Quotes

"I actually made the decision uh earlier this year that I'm going to drastically reduce the amounts of of research that I'm doing specifically on the transformer because of the feeling that I have that it's it's an oversaturated space, right?" — Llion Jones

"We're kind of in the situation right now where a lot of the papers are just taking the same architecture and making these endless amount of different tweaks... and we might be wasting the time in exactly the same way." — Llion Jones

"If you ask a standard neural network to understand a spiral shape, it solves it by drawing tiny straight lines that just happen to look like a spiral. It 'fakes' the shape without understanding the concept of spiraling." — Llion Jones

"Being better is not good enough. It has to be obviously crushingly better." — Llion Jones

"The CTM... has native adaptive compute. It's a new way of building a recurrent model that uses higher level concepts for neurons and a synchronization as a representation that lets us solve problems in ways that seem more human by being biologically and nature inspired." — Luke Darlow

"What we're after here is trying to do something biologically plausible... and find a line somewhere where we can take some sprinkling of biological inspiration but still train it with deep learning." — Luke Darlow

"The Sudoku Bench... if anyone was actually be able to beat our benchmark they would necessarily have to have created an extremely powerful reasoning system." — Llion Jones

"What was perfect I realized is they tell you in agonizing detail exactly what reasoning they used to solve those particular puzzles." — Llion Jones

"It's such a rare space, you know, it's it's such an specific kind of reasoning that's required to get to the the specific breakthrough that this kind of technique doesn't work." — Llion Jones