Sakana AI presents the Continuous Thought Machine

Sakana AI’s Continuous Thought Machine mimics the human thought process by thinking step-by-step

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Dear Readers,

Today is Research Wednesday, as always. So, we summarized the hottest AI papers of the past week for you! Also: Sakana AI presents the Continuous Thought Machine! A big breakthrough.

But before we get to today's main article, after yesterday's interview we would like to refer to Hashem Al-Ghaili's latest video, which he uploaded today. 100% AI generated! He wrote about it:

"I’m happy to share my new AI-assisted short film, “The Colorless Man.” This film took 2 weeks to complete during my free time, with a budget of $600 USD. I used various AI tools to explore how far AI-assisted film production has come. Based on estimates from other producers and filmmakers, a film like this would typically cost between $300K and $500K without AI, depending on the scale of production. It would also require around 70 people and at least 2 months of work. Thanks to AI, this was reduced to just 1 person, $600 USD, and 2 weeks of non-continuous work. First, I wrote the story and screenplay, then I used various AI tools to turn my script into visuals. I used ChatGPT, MidJourney, and Dreamina for images; Kling AI for videos; ElevenLabs for voices; Dreamina AI for lip sync; Suno AI for music; and MMAudio or ElevenLabs for sound effects."

Hashem Al-Ghaili

In Today’s Issue:

Sakana AI presents the Continuous Thought Machine

The TLDR
Japanese startup Sakana AI has unveiled the Continuous Thought Machine, a new AI model that solves problems by thinking sequentially rather than all at once. Inspired by the brain’s temporal dynamics, it offers more explainable and energy-efficient decision-making, marking a potential paradigm shift in how AI “thinks.”

Imagine an AI that doesn't just answer at lightning speed, but takes time to think about a problem - step by step, just like a human. This is exactly what the Japanese start-up Sakana AI has developed with its Continuous Thought Machine (CTM).

In contrast to conventional neural networks, which process information in one go, the CTM uses the synchronization of neurons over time. This temporal dynamic enables the model to handle complex tasks such as solving mazes or classifying images not only more efficiently but also more comprehensibly. The CTM actively “looks” at different areas of the image, similar to the human eye, and makes decisions based on these focused observations.

This is a significant step for the AI community: CTM brings us closer to an artificial intelligence that is not only powerful, but also interpretable and energy-efficient. It shows that new horizons can be opened up by integrating biological principles into AI models.

Could this be the beginning of a new era in which AI not only thinks faster, but also deeper?

Why it matters: The Continuous Thought Machine from Sakana AI demonstrates how temporal dynamics and neural synchronization can make AI systems not only more powerful, but also more human. It paves the way for an AI that thinks like us - only faster and more precisely.

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In The News

Glowing Against the Rules: Carbon Nanotubes Defy Physics

Japanese researchers have discovered that carbon nanotubes can emit light with more energy than they absorb—something long thought impossible. This phenomenon, called up-conversion photoluminescence, is powered by tiny atomic vibrations known as phonons that boost electron energy. Even stranger, heating the nanotubes increases their glow, defying the typical behavior of overheating tech. This breakthrough could revolutionize solar energy, optical cooling, and light-powered devices.

Copilot Gets Eyes—For Free

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Molecule Silences Heat with Physics Trick

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Graph of the Day

Microsoft announced X-REASONER Towards Generalizable Reasoning Across Modalities and Domains

Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Researchers introduce “Absolute Zero”: An AI called the “Absolute Zero Reasoner” (AZR) learns complex tasks such as programming and mathematics by setting itself problems and solving them – without any external training data. The novelty: Surprisingly, AZR outperforms models that have been trained with huge, human-curated data sets. This could break the data bottleneck for AI and enable more autonomous systems that transform technology and society.

Rewriting Pre-Training Data Boosts LLMPerformance in Math and Code

Quality over quantity in AI training data: Researchers show that rewriting code and math datasets instead of just filtering them dramatically improves the performance of AI models. The new “SwallowCode” and “SwallowMath” datasets, enhanced by AI, significantly increase capabilities. This “transform and retain” approach is a breakthrough for more specialized and powerful AI, as it maximizes the value of existing data.

All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning

Researchers are investigating why reinforcement learning (RL) often works better than direct optimization (maximum likelihood estimation) when fine-tuning language models. The core idea is that RL has an advantage in tasks with a “generation-verification gap,” where it is easier to verify a solution than to generate one. It learns a simpler “verifier” model and then optimizes the “generator” AI based on it. This is often more efficient than training the complex generation AI directly and leads to better results despite a theoretical loss of information.

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