Huge congratulations to @JeffDean and the legendary founding team on the launch! 🚀 I share a deep conviction in this mission. Automating the scientific method will profoundly alter the trajectory of AI over the next few years. Bringing the AI Scientist into a loop of recursive self-improvement will fundamentally change the landscape of our field. I strongly believe this automated experimental approach is the next major paradigm shift in AI beyond building large foundation models. It is a true honor to be included on this slide alongside such an incredible group of alumni, and I am excited to see what you will build next.
After rigorous testing, our joint AI project with Daiwa Securities is entering the full-scale production phase. We're bringing our agentic AI systems to @Daiwa_JP’s wealth management teams to accelerate complex market analysis in volatile markets. Big milestone for Sakana AI! x.com/SakanaAILabs/s…
To give some more context on what we are building with Daiwa Securities: During our technical verification phase, we integrated our AI agent technologies, specifically our AI Scientist and AB-MCTS frameworks, to tackle the core data challenges in traditional finance. We focused strictly on automating the rigorous gathering and analysis of complex market information. We successfully demonstrated that these agentic systems can reliably process financial data at scale while continuously improving their analysis quality by incorporating direct feedback from the end users. The ultimate goal of this deployment is human-AI collaboration. By bringing these systems into Daiwa’s wealth management division, we are automating the heavy lifting of data processing. This directly frees up their financial consultants to spend more time deeply understanding their clients' diverse situations and providing highly personalized, optimal advice. We are excited to provide the core technology that advances the future of financial consulting and wealth management in Japan. Full blog: t.co/u2B4m4VmGc
Sakana Namazu: An LLM API with Japanese-vibes! 🎏 Built for Japanese enterprises, featuring frontier-level reasoning and built-in agentic tools. 開発者の皆様、大変お待たせしました！Sakana Chatのモデルが遂にAPIとして公開です。ぜひお試しください！ Blog: sakana.ai/namazu-api#Eng… x.com/SakanaAILabs/s…
Sakana Namazu API sakana.ai/namazu/
Dreaming in Voxels: How AI is Generating Playable Minecraft Worlds Generative AI has conquered images, video, text. But what about interactive 3D environments? We trained models on billions of cubes to generate fully playable, structured worlds. Blog: pub.sakana.ai/dream-cubed 🧱 x.com/SakanaAILabs/s…
Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubes pub.sakana.ai/dream-cubed/ arxiv.org/abs/2604.22847
Running K3 locally on m5 max at 0.3 tok/s This is probably as slow as it will ever be😅 x.com/antirez/status…
Axios interview with @JensenHuang (Full Interview: t.co/914NKiphig) Jensen Huang’s comment regarding whether open source models should be allowed to distill closed models: “Distillation, learning from AI, learning from other, other sources of knowledge is fundamental to intelligence. We're constantly learning from other people. I'm learning from you and just the questions you're asking and you're learning from me. And all day long we're learning from each other. And so AI has to learn from something. And so the original AI, no matter who’s it is, whether it's a closed model, it obviously scraped the internet of all of the previously learned knowledge. And now AI is generating more content than humans. In another few more years, the internet would be 99% AI generated content, and that content is generated by some AI. And so you're constantly distilling the intelligence of some other AI's anyways. And so the idea that that AI can learn, that's a good thing. We want AI’s to be smart. We want every AI to be smart, and a smart AI is a safer AI. Should AI's be able to learn as much as they possibly can from many sources of knowledge as they can? The answer is yes. Can AI violate privacy? No. Can AI violate terms of an agreement? The answer is no. And if somebody violates a term of agreement, contact that company. Take care of it.” Source: x.com/rohanpaul_ai/s…
Open ecosystems are the foundation of a healthy AI industry. We have always believed that collective intelligence is the future. Proud that @SakanaAILabs is standing alongside global tech leaders to sign the open-weights letter. 🐟 x.com/SakanaAILabs/s…
Open ecosystems are the foundation of a healthy AI industry. We have always believed that collective intelligence is the future. Proud that @SakanaAILabs is standing alongside global tech leaders to sign the open-weights letter. 🐟 x.com/SakanaAILabs/s…
Fugu-Ultra now works with Claude Code 🐡 x.com/SakanaAILabs/s…
今夜はデニーズのドリンクバーでオープンモデルの未来について語り合うしかない ☕️ x.com/DennysDiner/st…
Our team just shipped Fugu-Ultra v1.1! 🐡 By dynamically orchestrating the latest frontier models, we pushed performance up by 7.9 points. We are now beating Fable 5 in complex coding and reasoning tasks without even having Fable 5 in our agent pool. Collective intelligence is the future.
Read the full release blog: sakana.ai/fugu-1-1-claud… 🐟
UnMaskFork: Test-Time Scaling for Masked Diffusion via Deterministic Action Branching t.co/W6ZvNHESRp Scaling test-time compute for Diffusion Models requires a different approach. Here, instead of sampling a model multiple times, UnMaskFork (UMF) uses MCTS to let multiple models collaborate on a single answer. UMF dynamically switches between different pre-trained diffusion models at inference time. Each model contributes exactly where it is most confident. True intelligence is collaborative! 🐟🚀
Incredibly proud of the Sakana AI team. We have developed an orchestration model right here out of Japan that achieves state-of-the-art performance on real-world cybersecurity benchmarks! 🎌 x.com/SakanaAILabs/s…
Release Notes: sakana.ai/fugu-cyber-rel…
Real brains follow Dale's principle: a neuron can either excite its neighbors or suppress them, but never both. Standard deep learning ignores this and uses backpropagation. In our new paper, Diffusing Blame, we fix this disconnect. By introducing a routing method that broadcasts error signals directly to the hidden layers, we can train networks made of dedicated positive and negative neurons to strictly obey Dale's principle, all without relying on backprop! This method works surprisingly well on image recognition tasks despite the strict biological constraints. We also achieved competitive, backprop-free reinforcement learning on complex locomotion tasks and the open-ended Craftax environment. It is neat to see that representation learning remains possible even when we force deep learning to play by the rules of real neurons.
Diffusing Blame: Task-Dependent Credit Assignment in Biologically Plausible Dual-Stream Networks arxiv.org/abs/2606.31700
We’re excited to collaborate with NVIDIA to build the next generation of Fugu orchestration models together, by incorporating leading open-weights models. x.com/SakanaAILabs/s…
Language models and coding agents are great, but there is more to life, and more to AI, than just LLM agents.
How do physical systems achieve collective intelligence and self-repair without a central brain? A new paper published today in Nature Communications by my Sakana AI colleague Sebastian Risi (@risi1979), along with co-authors from IT University of Copenhagen and Autodesk Research, presents a beautiful realization of biologically inspired robotics: Smart Cellular Bricks. The team built a system of physical 3D cubic units that can collectively infer their global shape and autonomously guide their own damage recovery using purely local interactions. Here is a deep dive into the paper’s key contributions: 1/ Neural Cellular Automata-based Architecture: Modular robots usually rely on central processors. This system flips that paradigm. Every block independently runs the exact same neural network on local microcontrollers. With no master plan or global coordinates, they communicate only with immediate neighbors. By passing continuous state vectors, hundreds of bricks achieve global consensus on their shape in under 3 minutes. 2/ Emergent Biological Morphogens: How does a block know it is part of a chair, not a table? The network’s internal memory automatically learns to establish continuous gradients across the structure. This beautifully mirrors how biological morphogens give positional info to developing cells. The bricks naturally form left-right, radial, and head-to-tail axes to align their identity. 3/ Performance and Generalization: Validated in large-scale simulations, the networks transferred seamlessly to nearly 200 physical hardware bricks, achieving a 100% convergence rate. Instead of rigid template-matching, the system infers broad categories. Even when tested on unseen variations, like an asymmetric table with five random legs, the collective correctly classified the structure. 4/ Fault Tolerance and Autonomous Damage Recovery: Hardware fails in the real world. This system easily tolerates up to 15% module failure without losing accuracy. By predicting spatial damage directions, the cells pinpointed missing components with 95% accuracy. They actively use these local signals to guide a self-repair process, regenerating back into the intended morphology. I believe this is a significant piece of research, bridging collective intelligence and Physical AI. This work represents the first successful physical realization of large-scale, decentralized 3D self-recognition and damage detection. By moving away from centralized control, this architecture paves the way for highly adaptive smart materials and resilient robotics that can survive and repair themselves. Read the full open-access paper: t.co/friVEdvfT6 Congratulations to the team on this achievement!
One of my first journeys in neural networks started over a decade ago with implementing CPPN-NEAT! Back then, I built a clone of ‘Picbreeder’ not only to study the mechanics of neural nets, but to explore the human creativity process itself, and generate some cool abstract art. Neurogram: t.co/ytwBChaHu7 Gallery: t.co/aAaUGomkv2 Today, things have come full circle. We are now trying to use modern VLMs and frontier LLM agents within open-ended exploration algorithms. We want to see if we can finally computationally derive the underlying mechanics of human creativity: serendipity, memory, exploration versus exploitation, and novelty search. Can modern AI actually replicate the magic of human open-endedness? Dive into our new AI Picbreeder Experiment here: t.co/hrLhTsIMfe
Here is a visualization of the AI Picbreeder engine in action. (From our blog: t.co/hrLhTsIMfe) To recreate a collaborative human ecosystem, we run 10 VLM “breeder” agents in parallel. These agents constantly sample from a shared archive, interactively evolve new candidates through mutation and crossover, and publish their favorites. Meanwhile, VLM “critic” agents step in to evaluate the growing phylogenetic tree of art, forming an endless loop of digital cultural production.
We just launched Sakana Translate! t.co/KeleCLOLOT I personally rely on this tool every day. Huge congratulations to the team for shipping this! Standard translation tools often miss the deep nuance of Japanese business honorifics, cultural concepts, and internet slang. We built a tool that actually translates the context and tone.
Happy Independence Day, from Tokyo 🇺🇸
I’m looking to hire a Program Manager to help manage Sakana AI’s fast growing Recursive Self-Improvement (RSI) Lab 🚀 RSI Lab (English): sakana.ai/rsi-lab/ RSI Lab (日本語): sakana.ai/rsi-lab-jp/ Job Description: sakana.ai/careers/progra… x.com/SakanaAILabs/s…
Program Manager (RSI Lab) sakana.ai/careers/progra…
Goal! ⚽️🚀🇯🇵
@FakePsyho Congrats @FakePsyho !
皆が集団として希望を感じていることが必要だと思います。その希望こそが、物事を動かしていく。日本に希望をもたらすのは何でしょうか。お金でしょうか。減税でしょうか。社会保障の充実でしょうか。おそらく、そのどれでもないはずです。もし魔法の杖を一本持てるとしたら、日本の人々が未来をもっと楽観的に捉えられるようにしたい。希望によって変化が起こり、その変化がさらなる希望をもたらすのではないでしょうか。
To really move the needle in terms of innovation, like the era in Japan when you had companies like Sony or Panasonic come out, there are a few factors at play. One is that the nation needs to have a collective sense of hope. A nation needs to feel hopeful. It could be poor. Everyone can be dirt poor, but they have hope. And hope leads to things being done. So, what creates hope in Japan? Is it more money? Is it less taxes? Or is it more benefits? Probably not. I think that it is stories and narratives that can create hope. If I have a magic wand, if the population in Japan is more optimistic about the future of this country, suddenly they’re persuaded, to be more optimistic. People should be optimistic. With optimism comes change. And change leads to more optimism. 🎏 t.co/1wQcGa5zdO
Goal! ⚽️🇯🇵🚀
Excited to partner with @OpenRouter ⚡ Products like OpenRouter Fusion and Sakana Fugu have sparked a serious conversation about dependency and resilience in AI. I believe this is just the start of a great architectural shift to come in AI development. x.com/SakanaAILabs/s…
Sakana Fugu Technical Report github.com/SakanaAI/fugu/… Release Notes: sakana.ai/fugu-release/
