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sakanaai.bsky.social
@sakanaai.bsky.social
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Sakana AI is an AI R&D company based in Tokyo, Japan. 🗼🧠
https://sakana.ai/careers
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sakanaai.bsky.social
We’re thrilled to announce Sakana AI has raised ¥20B in our Series B! 🐟

From day one, we’ve taken a different path. Our research has always focused on developing efficient AI technology sustainably, driven by the belief that resource constraints—not limitless compute—are key to true innovation.
At present, we are seeing a record amount of capital pouring into AI compute at a rate which may not be sustainable, funding AI businesses without a clear path to profitability, and consuming unprecedented levels of energy, to develop AI models under the assumption of near limitless resources. But is this really the future that we want to build? As an AI R&D company in Tokyo, we also question whether this is the right approach for Japan, a nation with limited resources, to build its Sovereign AI. We believe that intelligent life has arisen not from an abundance of resources but rather from the lack of it. Nature ultimately selects systems that are able to do more with less. It is debatable whether such constraints are a requirement for intelligence to emerge, but it is undeniable that our own intelligence is a result of resource constraints. Sakana AI has always done things differently. Since the beginning, our research has focused on developing efficient AI technology sustainably. For instance, rather than reinventing the wheel by training yet another large foundation model from scratch, we instead pioneered ways of using evolution to combine existing open-source models, using tree search to combine closed models, getting models to self-improve, to push the frontier of AI capabilities. Last year, we also pioneered the use of LLM agents to automate AI science, enabling more efficient algorithms to be discovered by AI itself. We developed energy efficient language models that work on edge devices, and we are also working on entirely new AI architectures which may carry us into a vastly more efficient AI paradigm. We also created our business model with sustainability and profitability in mind. This year, we built a healthy and growing enterprise AI business, where we have partnered with some of the largest enterprises in Japan to deploy AI-enabled business applications that can truly bring return on investment to our clients with our technology.
November 17, 2025 at 12:00 AM
sakanaai.bsky.social
Announcing our Series B 🐟

sakana.ai/series-b
November 16, 2025 at 11:59 PM
sakanaai.bsky.social
「日本の文化や社会的文脈を的確に理解できるAIモデルを構築したい」

東京都の公式メディア「TOKYO UPDATES」に、Sakana AIリサーチサイエンティストのカラーヌワット・タリンのインタビューが掲載されました。

www.tokyoupdates.metro.tokyo.lg.jp/post-1670/

日本の貴重な文化資源を活用することで、日本に根ざしたAI技術の実現を目指す取り組みについて、ぜひご一読ください。
November 14, 2025 at 3:40 AM
sakanaai.bsky.social
Our GRPO and "Thought Cloning" experiments (learning from expert solvers) show current methods struggle with spatial reasoning and creative insights humans use naturally.
November 11, 2025 at 8:07 AM
sakanaai.bsky.social
Unlike Chess or Go, these puzzles require understanding novel rules through meta-reasoning, then maintaining consistency over long reasoning chains.
November 11, 2025 at 8:06 AM
sakanaai.bsky.social
GPT-5 on Sudoku-Bench 🧩

GPT-5 now leads our Sudoku-Bench leaderboard with 33% solve rate, ~2x the previous best, and is the first LLM to solve a 9x9 modern Sudoku.

Still, 67% of puzzles remain unsolved.

Read more about our update here:
🔗 Blogpost → pub.sakana.ai/sudoku-gpt5/

🧵 Thread 👇
November 11, 2025 at 8:04 AM
sakanaai.bsky.social
Introducing Petri Dish Neural Cellular Automata (PD-NCA)

pub.sakana.ai/pdnca/

In this work we explore the role of continual adaptation in artificial life, where the cellular automata in our system do not rely on a fixed set of parameters, but rather learn continuously during the simulation itself.
November 5, 2025 at 12:26 AM
sakanaai.bsky.social
How the ‘Attention is all you need’ paper was born from freedom, not pressure:
How the 'Attention is all you need' paper was born from freedom, not pressure To underscore his point, Jones described the conditions that allowed transformers to emerge in the first place — a stark contrast to today's environment. The project, he said, was "very organic, bottom up," born from "talking over lunch or scrawling randomly on the whiteboard in the office." Critically, "we didn't actually have a good idea, we had the freedom to actually spend time and go and work on it, and even more importantly, we didn't have any pressure that was coming down from management," Jones recounted. "No pressure to work on any particular project, publish a number of papers to push a certain metric up." That freedom, Jones suggested, is largely absent today. Even researchers recruited for astronomical salaries — "literally a million dollars a year, in some cases" — may not feel empowered to take risks. "Do you think that when they start their new position they feel empowered to try their wild ideas and more speculative ideas, or do they feel immense pressure to prove their worth and once again, go for the low hanging fruit?" he asked.
October 24, 2025 at 1:34 PM
sakanaai.bsky.social
Sakana AI’s CTO (Llion Jones) says he’s ‘absolutely sick’ of transformers, the tech that powers every major AI model

“You should only do the research that wouldn’t happen if you weren’t doing it.” (Brian Cheung) 🧠💡

venturebeat.com/ai/sakana-ai...
Why one AI lab is betting that research freedom beats million-dollar salaries Jones's proposed solution is deliberately provocative: Turn up the "explore dial" and openly share findings, even at competitive cost. He acknowledged the irony of his position. "It may sound a little controversial to hear one of the Transformers authors stand on stage and tell you that he's absolutely sick of them, but it's kind of fair enough, right? I've been working on them longer than anyone, with the possible exception of seven people." At Sakana AI, Jones said he's attempting to recreate that pre-transformer environment, with nature-inspired research and minimal pressure to chase publications or compete directly with rivals. He offered researchers a mantra from engineer Brian Cheung: "You should only do the research that wouldn't happen if you weren't doing it." One example is Sakana's "continuous thought machine," which incorporates brain-like synchronization into neural networks. An employee who pitched the idea told Jones he would have faced skepticism and pressure not to waste time at previous employers or academic positions. At Sakana, Jones gave him a week to explore. The project became successful enough to be spotlighted at NeurIPS, a major AI conference. Jones even suggested that freedom beats compensation in recruiting. "It's a really, really good way of getting talent," he said of the exploratory environment. "Think about it, talented, intelligent people, ambitious people, will naturally seek out this kind of environment."
October 23, 2025 at 5:30 PM
sakanaai.bsky.social
ShinkaEvolve's efficiency comes from three key innovations:

1) Adaptive parent sampling to balance exploration and exploitation.

2) Novelty-based rejection filtering to avoid redundant work.

3) A bandit-based LLM ensemble that dynamically picks the best model for the job.
September 25, 2025 at 5:59 AM
sakanaai.bsky.social
3/ LLM Training: It discovered a novel load balancing loss for MoE models, improving model performance and perplexity.
September 25, 2025 at 5:58 AM
sakanaai.bsky.social
2/ Competitive Programming: On ALE-Bench, it improved an existing agent's solution, turning a 5th place result into a 2nd place leaderboard rank for one task.
September 25, 2025 at 5:58 AM
sakanaai.bsky.social
We applied ShinkaEvolve to a diverse set of hard problems:

1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering a Pareto frontier of solutions trading performance for efficiency.
September 25, 2025 at 5:58 AM
sakanaai.bsky.social
On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a massive leap in efficiency compared to previous methods that required thousands of evaluations.
September 25, 2025 at 5:57 AM
sakanaai.bsky.social
Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution.
September 25, 2025 at 5:57 AM
sakanaai.bsky.social
We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. It leverages LLMs to find state-of-the-art solutions, orders of magnitude faster!

Blog: sakana.ai/shinka-evolve/
Paper: arxiv.org/abs/2509.19349
September 25, 2025 at 5:56 AM
sakanaai.bsky.social
We are honored that Sakana AI’s CEO David Ha (@hardmaru.bsky.social) has been named to the TIME 100 AI 2025 list. Full List: time.com/time100ai

We’re truly grateful for the recognition and will continue our mission to build a frontier AI company in Japan.

Thank you for your support!
August 29, 2025 at 1:21 PM
sakanaai.bsky.social
What if we could evolve AI models like organisms, letting them compete, mate, and combine their strengths to produce ever-fitter offspring?

Excited to share our new paper, “Competition and Attraction Improve Model Fusion” presented at GECCO 2025 (runner-up for best paper)!

arxiv.org/abs/2508.16204
Competition and Attraction Improve Model Fusion Model merging is a powerful technique for integrating the specialized knowledge of multiple machine learning models into a single model. However, existing methods require manually partitioning model parameters into fixed groups for merging, which restricts the exploration of potential combinations and limits performance. To overcome these limitations, we propose M2N2, an evolutionary algorithm with three key features: 1/ dynamic adjustment of merging boundaries to progressively explore a broader range of parameter combinations; 2/ a diversity preservation mechanism inspired by the competition for resources in nature, to maintain a population of diverse, high-performing models that are particularly well-suited for merging; and 3/ a heuristic-based attraction metric to identify the most promising pairs of models for fusion. Our experimental results demonstrate, for the first time, that model merging can be used to evolve models entirely from scratch. Specifically, we apply M2N2 to evolve MNIST classifiers from scratch and achieve performance comparable to CMA-ES, while being computationally more efficient. Furthermore, M2N2 scales to merge specialized language and image generation models, achieving state-of-the-art performance. Notably, it preserves crucial model capabilities beyond those explicitly optimized by the fitness function, highlighting its robustness and versatility.
August 25, 2025 at 2:48 AM
sakanaai.bsky.social
Sakana AI が募集しているSoftware Engineerの募集要項（Job Description）をアップデートしました。

sakana.ai/careers/#sof...

Sakana AIにおけるSoftware Engineerは、Applied Teamの一員としてビジネスのインパクトにつながるプロダクト開発を行っています。Frontend、Backend、Infrastructure構築の全体にわたって、AI技術を組み込んだアプリケーションの設計・開発に挑戦いただける方のご応募をお待ちしております！
August 22, 2025 at 5:10 AM
sakanaai.bsky.social
８/７に、Sakana AIは初となるApplied Research Engineer向けのOpen Houseを開催しました。現地で70名、オンラインで200名超の方にご参加いただいた本イベントのレポートを公開します。

sakana.ai/open-house-2...

イベントでは共同創業者2名も登壇し、研究開発とビジネスの両輪をどう回し、日本や世界の産業・コミュニティにどう貢献していくかを語りました。また、現場で活躍するAppliedチームのメンバーが、チームの特徴や働き方、AIエージェント開発の実態、Researchチームとの連携などについて紹介しました。
August 14, 2025 at 12:46 AM
sakanaai.bsky.social
【UI/UXデザイナー募集】

Sakana AIでは、当社AI技術の社会実装のフェーズに進むことに伴い、一人目のUI/UXデザイナーを募集します。

詳細： sakana.ai/careers/#uiu...

お任せしたいのはプロダクトのコンセプト設計から、プロトタイプの作成、ユーザーテストまでの全てのプロセス。AIによる価値実現に向け、目下成長するApplied Teamの一員として、Sakana AIのプロダクトづくりに挑んでくださる、意欲ある方のご応募をお待ちしています！
August 4, 2025 at 6:35 AM
sakanaai.bsky.social
今回の2名を含む多彩なメンバーが、最先端のAI技術の社会実装に取り組んでいます。引き続きメンバーを募集していますので、ご関心のある方は、ぜひ当社の募集要項をご覧ください。

Sakana AI採用情報： sakana.ai/careers/
July 29, 2025 at 9:32 AM
sakanaai.bsky.social
「Sakana AIは学術研究のイメージが強いけど、どうやってそれをビジネスにつなげるの？」最先端AIの社会実装に挑む「Applied Team」インタビュー！
sakana.ai/applied-team...

Sakana AIでは、世界トップレベルの生成AI技術を社会実装するために「Applied Team」を本格始動しています。 Applied Teamについて知っていただくことを目的として、AI研究の社会実装に挑む二人のメンバーのインタビュー記事を公開しました。

「事業専門性とR&Dの強みが社内に揃っているスタートアップの環境は、世界で見ても非常に珍しいのではないかと思います。」
July 29, 2025 at 9:32 AM
sakanaai.bsky.social
【Sakana AIエンジニアの著書刊行🎉】

Sakana AIのApplied Research Engineer、太田真人が共著者を務める『現場で活用するための AIエージェント実践入門』（講談社）が刊行されました。進歩を続けるAIエージェント技術を実践に繋げるための知見が満載ですので、ぜひご覧ください！

Amazon: www.amazon.co.jp/dp/4065401402/

8/7開催のApplied Engineer Open Houseには太田も登壇します。ご参加お待ちしています！

Event: connpass.com/event/362760/
July 18, 2025 at 8:27 AM
sakanaai.bsky.social
TransEvalniaは、当社で独自構築した日・英のデータセットや、WMT共有タスク（機械翻訳の代表的なベンチマーク）における複数の言語ペアのデータにおいて、最先端の評価システムであるMT-Ranker（Moosa et al., 2024）と同等またはそれ以上の性能を示しました。

評価用LLMとしてClaude-3.5-SonnetやQwen-2.5-72B-Instructを用いた場合、その評価結果は人間の評価者にとって妥当なものでした。また、Claude-3.5-SonnetをはじめとするLLMが付与したスコアは、人間の評価者によるスコアと高い相関を示しました。
July 18, 2025 at 5:26 AM