Sakana AI welcomes Jürgen Schmidhuber as Chief Scientific Advisor.
https://t.co/e6JxGxQWEo
Sakana AI is incredibly proud to announce that Jürgen Schmidhuber, universally recognized as the father of modern AI, is officially joining Sakana AI as Chief Scientific Advisor.
For nearly four decades, Jürgen has explored how machines can learn to learn. His foundational work in the 1990s drove core advancements in deep learning and established early frameworks for world models. Crucially, his pioneering innovations in meta-learning opened the very path toward recursive self-improvement.
These ideas have already shaped our own research, from the Darwin Gödel Machine to The AI Scientist. Now Jürgen will help guide our newly formed RSI Lab, whose objective is to trigger a compounding cycle of scientific discovery aimed at improving machine intelligence. We are assembling a critical mass of world-class experts in Tokyo to make this a reality.
Welcome, @SchmidhuberAI !
new post on harness engineering for AI self-improvement: https://t.co/ZYvGfVs61k
It is hard to forecast how much the future of RSI will rely on harnesses. Likely harness engineering will evolve in the direction of self-improvement and enable auto-research, and, in turn, smarter models keeps harnesses simple.
Even when many harness improvement get eventually internalized into core model, the need to specify goals and context will not disappear.
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: https://t.co/sVkbTPtXOl 🐡
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.
As shown in this figure, Fugu is a multi-agent system that behaves like a single model. You send a request to one endpoint, and Fugu decides how to handle it internally.
Fugu manages model selection, delegation, verification, and synthesis automatically. It solves tasks directly when that is enough, or coordinates a team of expert models when a problem calls for more. The complexity of a multi-agent system never reaches your code.
At launch, Sakana Fugu comes in two models accessed via a single OpenAI-compatible API:
• Fugu balances strong performance with low latency for everyday work. It fits naturally into tools like Codex for coding, as well as chatbots and interactive services. You can also opt specific agents out of its pool for data compliance.
• Fugu Ultra is our flagship model tuned for maximum answer quality on hard, multi-step problems. It coordinates a deeper pool of expert agents for demanding work like AI research, cybersecurity analysis, and patent investigations.
Principal Platform Engineer at Sakana AI
https://t.co/8D9kdzi2hg
At Sakana AI, we are building a sovereign, unified data platform for Japan's defense sector and largest industrial enterprises.
If you have built ontology-driven systems for national security, or engineered the core data layers that power modern defense platforms, you know exactly what the scale and stakes of this challenge entail. We need a hands-on technical leader who has shipped end-to-end and thrives in high-velocity deployment cycles.
This is a full-time position based in Tokyo, Japan. While basic level or higher Japanese proficiency is a valued asset, Japanese language is not a requirement. Your technical mastery and commitment are the core requirements.
This is a rare opportunity to build the nervous system that connects frontier AI to critical national infrastructure from the ground up.
Read the full mission and apply here:
https://t.co/8D9kdzi2hg
@SakanaAILabs@iclr_conf@fsa_JAPAN Finansal veri analizi için dile özel kıyaslama oluşturmak çok mantıklı, genel modeller bu nüansları kaçırıyor genelde.
Building AI agents for real world banking workflows is incredibly difficult. It requires structuring the implicit knowledge of veteran bankers.
We just published a behind the scenes look at how our Applied Team built the MUFG AI Lending Expert. They explain how we adapted concepts from our research on ALE Agent and The AI Scientist to handle complex enterprise workflows.
Taking AI from the lab to a major bank is not just about better prompts. The team even used AI to process nearly 1,500 pieces of human feedback, creating a high speed improvement loop that allowed the system to scale and adapt rapidly.
This interview is a great look at the engineering and product culture we are building at Sakana AI. If you want to see how we tackle hard engineering challenges and build systems for mission critical environments, I highly recommend giving it a read.
Blog (Japanese): https://t.co/RwyLKRHc4E
We are pleased to announce a strategic investment from Citi!
https://t.co/SQp1HEGzEp
This milestone marks Citi’s first such investment in a Japanese company.
The investment reflects their high regard for our advanced technical capabilities and our proven track record of implementing AI within the financial sector.
We are focused on developing new enterprise-grade AI solutions using nature-inspired intelligence.
Our goal has consistently been to bridge the gap between cutting-edge research and practical business applications.
Building on our work developing highly specialized AI agents for financial domains, we are ready to take the next step.
Through this partnership, we aim to accelerate our international expansion and drive innovation in global financial services, originating from Japan.