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 in Nature Communications from Sakana AI, IT University of Copenhagen, Autodesk, presents a beautiful realization of biologically inspired robotics: Smart Cellular Bricks. Thread 🧵

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.

Fugu-Ultra is now available on Vercel AI Gateway vercel.com/changelog/sa... ✨

Excited to share CoffeeBench!!☕️☕️☕️ We evaluate LLM agents in a 90-day B2B coffee supply-chain economy spanning farmers, roasters, and retailers, where autonomous firms negotiate, manage inventory, set prices, handle invoices, and manage cash flow. arxiv.org/abs/2606.16613 github.com/sakanaai/cof...

Fugu-Ultra is now live on OpenRouter! ⚡ We share a core vision with the OpenRouter team: the future of AI isn’t a single monolithic model, but the collective intelligence of the world’s best models working together. Try it: openrouter.ai/sakana/fugu-... 🐡

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 massive architectural shift to come in AI development.

Human intelligence is fundamentally a collective intelligence. We solve complex problems by participating in a vast cultural network that builds upon ideas across generations. I believe the strongest AI systems will become a collective intelligence, too.

Sakana Fugu: One Model to Command Them All Frontier-level performance without single-vendor dependency. Fugu dynamically orchestrates the world's best models to tackle complex, multi-step tasks. Plug collective intelligence directly into your workflows today with a single API. sakana.ai/fugu/

How does it work? Sakana Fugu is itself an LLM, trained to call various LLMs in an agent pool, including instances of itself recursively. Fugu dynamically orchestrates the world's best models to tackle complex, multi-step tasks. Here, Fugu is a multi-agent system that behaves like a single model.

Fugu stands shoulder-to-shoulder with leading models like Fable and Mythos across the industry's most rigorous engineering, scientific, and reasoning benchmarks. Read the full blog: sakana.ai/fugu-release Beyond Bigger Models: Why are Orchestration Models the Next Frontier (Thread Below)

Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API. Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls. Try it: sakana.ai/fugu 🐡

Reproducing all of Jürgen Schmidhuber’s papers (1990-2025) using an AI coding assistant. Cool project by Yaroslav! It even reproduced the “World Models” paper by me and Schmidhuber (2018) using a toy environment, with a full VAE + RNN world model implementation. Project: github.com/cybertronai/...

How do we make LLMs faster and lighter? Don’t force the GPU to adapt to sparsity. Reshape the sparsity to fit the GPU! Our latest work with NVIDIA introduces new CUDA kernels & data formats for faster inference and training of sparse transformer language models: Blog: pub.sakana.ai/sparser-fast...

Excited to share Sakana AI’s new #ICML2026 paper in collaboration with NVIDIA: "Sparser, Faster, Lighter Transformer Language Models" arxiv.org/abs/2603.23198 This work introduces new open-source GPU kernels and data formats for faster inference and training of sparse transformer LLMs: 🧵 Thread 👇

If GitHub were built in: Japan 🇯🇵 China 🇨🇳 North Korea 🇰🇵 The EU 🇪🇺

For the past few years, humans have been doing “prompt engineering” to coax the best performance out of different LLMs. In this work, we explored what happens if we train an AI to do that job instead. Link to our #ICLR2026 paper: arxiv.org/abs/2512.04388 Thread:

Introducing our new work: “Learning to Orchestrate Agents in Natural Language with the Conductor” accepted at #ICLR2026 arxiv.org/abs/2512.04388 What if we trained an AI not to solve problems directly, but to act as a manager that delegates tasks to a diverse team of other AIs? Thread:

日経クロステックの連載記事「R&Dを根底から変革、普及始まる『AI科学者』」にて、Sakana AIのResearch Scientist、Robert Langeへの取材記事が掲載されました。 当社のAIサイエンティストについて、テーマ探索から論文査読まで研究の全工程を自動的に遂行する仕組みと、その現状の到達点・限界について解説されています。 記事でも触れていただいているとおり、AIサイエンティストに関する論文は2026年3月に学術誌Natureに掲載されました。基盤となるAIモデルの性能向上に伴って生成される論文の質が改善されうることを実験的に示せた点は、本研究の重要な成果の一つです。

Scaling up massive LLMs continues to yield incredible results. But to truly unlock their full potential, the next frontier is test-time compute and dynamic orchestration.

What if instead of building one giant AI, we evolved a coordinator to orchestrate a diverse team of specialized AIs? 🐟 Excited to share our new #ICLR2026 paper: “TRINITY: An Evolved LLM Coordinator”! Paper arxiv.org/abs/2512.04695 OpenReview openreview.net/forum?id=5Ha... Fugu sakana.ai/fugu-beta

We’ve been using Sakana Fugu internally for our own research and coding. Instead of relying on a single model, it dynamically orchestrates the best combination of open and closed models for any task. The future of AI is collective intelligence. Excited to open the beta API: sakana.ai/fugu-beta

Available as an OpenAI-compatible API, you can seamlessly integrate Fugu into your existing workflows with minimal changes. 🐟 Fugu Mini: High-speed orchestration optimized for latency 🐡 Fugu Ultra: Full model pool utilization for deep complex reasoning Apply for the beta: forms.gle/BtKkhc2CfLKk...

We’re launching the beta for our new commercial AI product: Sakana Fugu 🐡, a multi-agent orchestration system! sakana.ai/fugu-beta Fugu dynamically coordinates frontier models, autonomously selecting the optimal agent combinations and roles for each task, hits SOTA on SWE-Pro, GPQA-D, ALE-Bench!

Getting LLMs to simulate “true” randomness or generate diverse outputs is surprisingly difficult. We found a simple prompting trick that solves this by having the model generate and manipulate a random string. To be presented at #ICLR2026 this week! Blog: pub.sakana.ai/ssot

Can LLMs flip coins in their heads? When prompted to "Flip a fair coin" 100 times, the heads to tails ratio drifts far from 50:50. LLMs can understand what the target probability should be, but generating outputs that faithfully follow a given distribution is a separate problem. pub.sakana.ai/ssot

I am very proud of our team for releasing EDINET-Bench, and it is fantastic to see a Japanese financial dataset recognized at #ICLR2026 this week. We need more diverse, non-English datasets to evaluate models in the real world. Paper: openreview.net/forum?id=Dxn...

Digital Ecosystems: Interactive Multi-Agent Neural Cellular Automata pub.sakana.ai/digital-ecos...

We are hiring Software Engineers in Tokyo to help us scale Sakana AI’s R&D efforts. If you are interested in building the data pipelines and full stack infrastructure needed to push the boundaries of automated scientific discovery, we would love to hear from you. 🗼🎌 sakana.ai/careers/#sof...

A “Neural Computer” is built by adapting video generation architectures to train a World Model of an actual computer that can directly simulate a computer interface. Paper: arxiv.org/abs/2604.06425 Code: github.com/metauto-ai/N... Cool work led by Mingchen Zhuge et al. from Schmidhuber’s lab!

I’m incredibly proud of The AI Scientist team for this milestone publication in Nature. We started this project to explore if foundation models could execute the entire research lifecycle. Seeing this work validated at this level is a special moment.

Sakana AI 初の一般向けサービス Sakana Chat を公開しました🐟 Try Sakana Chat: chat.sakana.ai 強力なWeb検索エージェントを備え、高速で信頼性の高い情報を引き出せます。 世界の高性能なオープンモデルには、開発元のバイアスが不可避的に内在しています。我々は独自の事後学習により、①これらのバイアスの除去、②日本の価値観の反映、③安全かつ文脈に即した適応を実現する技術を開発しました。 今回のリリースは、その技術実証の第一弾。国内で誰もが安心して使えるAIの選択肢の一つとして、ぜひお試しください！

“When AI Discovers the Next Transformer” Full Interview on YouTube: youtu.be/EInEmGaMRLc Robert Lange (Sakana AI) joins Tim Scarfe (ML Street Talk) to discuss Shinka Evolve, a framework that combines LLMs with evolutionary algorithms to do open-ended program search.

Instead of forcing models to hold everything in an active context window, we can use hypernetworks to instantly compile documents and tasks directly into the model's weights. A step towards giving language models durable memory and fast adaptation. Blog: pub.sakana.ai/doc-to-lora/

「How Competition is Stifling AI Breakthroughs」 Sakana AI共同創業者 Llion Jones のTED AIトークが公開されました。目標を定めすぎないオープンエンドな研究がブレークスルーを生む理由、Transformerの成功が業界にもたらした状況、それを乗り越える次の構想と成果を語りました。 www.ted.com/talks/llion_...

Our journey at Sakana AI is just getting started. We are looking for people to help us pioneer the next generation of AI—building from Japan to the world. Join us: sakana.ai/careers

I founded Sakana AI after my time at Google, so it is incredibly meaningful to be able to partner with them now. It feels like a special connection to be working together again to advance the AI ecosystem in Japan. sakana.ai/google#en

Our work on The AI Scientist and ALE-Agent has already shown the power of these models. Now, we are scaling reliable AI in mission-critical sectors like finance and government to ensure the highest security and data sovereignty. Full details: sakana.ai/google#en

We are thrilled to announce a strategic partnership with Google! Google is also making a financial investment in Sakana AI to strengthen this collaboration. We are combining Google’s world-class products like Gemini and Gemma with our agile R&D to accelerate automated scientific discovery.

We just published an unofficial guide on what we look for when interviewing research candidates at Sakana AI. Written by Stefania Druga, Luke Darlow, and Llion Jones. The biggest differentiator? Understanding over implementation. Read it: pub.sakana.ai/Unofficial_G...

RePo moves us toward models that intelligently curate their own working memory rather than passively accepting input order. Read the full breakdown on our website: pub.sakana.ai/repo/ Paper: arxiv.org/abs/2512.14391

Introducing RePo: Language Models with Context Re-Positioning Standard LLMs force a rigid linear structure on context, treating physical proximity as relevance. Cognitive Load Theory suggests this is inefficient—models waste capacity managing noise instead of reasoning. arxiv.org/abs/2512.14391

2026 is just getting started 🚀✨ We are hiring. Join our team in Tokyo! sakana.ai/careers

AI導入は「雇用不安小さい正社員制度が強みに」 日経ビジネスにて、Sakana AI CEO @hardmaru.bsky.social のインタビューが公開されました。企業へのAI実装が本格化する2026年における現状と課題、そして日本企業の組織文化がAI導入にとってポジティブに働く可能性について語りました。 business.nikkei.com/atcl/gen/19/... 【記事のハイライト】🧵

Introducing DroPE: Extending Context by Dropping Positional Embeddings We found embeddings like RoPE aid training but bottleneck long-sequence generalization. Our solution’s simple: treat them as a temporary training scaffold, not a permanent necessity. arxiv.org/abs/2512.12167 pub.sakana.ai/DroPE

One of my favorite findings: Positional embeddings are just training wheels. They help convergence but hurt long-context generalization. We found that if you simply delete them after pretraining and recalibrate for <1% of the original budget, you unlock massive context windows. Smarter, not harder.

We are taking our technology far beyond competitive programming to unlock a new era of AI-driven discovery. We are hiring. Join our team in Tokyo. sakana.ai/careers/#sof...

We’re hiring. sakana.ai/careers/#sof...

When agents compete for limited resources, intelligence reorganizes around survival, not elegance.

Survival of the fittest code! Our paper explores LLMs driving an evolutionary arms race in Core War, where assembly programs fight each other. We task LLMs with evolving "Warriors" in a virtual machine, producing chaotic, self-modifying code dynamics. Blog: sakana.ai/drq Paper: pub.sakana.ai/drq/