Excited to release our new work: Petri Dish Neural Cellular Automata! pub.sakana.ai/pdnca We investigate how multi-agent NCAs can develop into artificial life 🦠 exhibiting complex, emergent behaviors like cyclic dynamics, territorial defense, and spontaneous cooperation.

Proud to release ShinkaEvolve, our open-source framework that evolves programs for scientific discovery with very good sample-efficiency! 🐙🧠 Paper: arxiv.org/abs/2509.19349 Blog: sakana.ai/shinka-evolve/ GitHub Project: github.com/SakanaAI/Shi...

Just received my copy of “What Is Intelligence?” by @blaiseaguera.bsky.social 🧠🪱 Thanks for sending it to Japan! 🗼 whatisintelligence.antikythera.org

Due to physical resource constraints, we currently estimate that around 300–400 of the candidate papers recommended for acceptance by the ACs will need to be rejected. We seek the support of our 41 SACs in addressing this distributed optimization problem in a fair and professional manner.

NeurIPS has decided to do what ICLR did: As a SAC I received the message 👇 This is wrong! If the review process cannot handle so many papers, the conference needs yo split instead of arbitrarily rejecting 400 papers.

How Sakana AI’s new evolutionary algorithm builds powerful AI models without expensive retraining venturebeat.com/ai/how-sakan...

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!

Why Greatness Cannot Be Planned Both the English and Japanese editions now found a home in the Sakana AI library ✨ @sakanaai.bsky.social

Our new GECCO’25 paper builds on our past work, showing how AI models can be evolved like organisms. By letting models evolve their own merging boundaries, compete to specialize, and find ‘attractive’ partners to merge with, we can create adaptive and robust AI ecosystems. arxiv.org/abs/2508.16204

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

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！

1910: The Year the Modern World Lost Its Mind Good piece comparing the anxieties of the early 1900s, an era of great and rapid technological change, to the present time. www.derekthompson.org/p/1910-the-y...

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。

Coverage of Darwin Gödel Machine and The AI Scientist in MIT Technology Review article. @technologyreview.com www.technologyreview.com/2025/08/06/1...

【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！

Andrew Ng’s piece on 🇺🇸 vs 🇨🇳 competition in AI worth reading: Full article: www.deeplearning.ai/the-batch/is...

Kenneth Stanley & Joel Lehmanによる名著『Why Greatness Cannot Be Planned』の日本語版がBNN社より刊行されました！ 『目標という幻想：未知なる成果をもたらす、〈オープンエンド〉なアプローチ』 監修：岡瑞起、翻訳：牧尾晴喜、解説：岡瑞起・鈴木健 本書は、科学・技術・芸術・ビジネスなど 、あらゆる領域でブレークスルーを起こすための「目標を定めない」オープンエンドなアプローチを提唱しています。 『WIRED JAPAN』日本版にて、『目標という幻想』日本語版解説が全文公開されました。 wired.jp/article/why-...

「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。」

ICML’s Statement about subversive hidden LLM prompts We live in a weird timeline… icml.cc/Conferences/...

Not Even Bronze: Evaluating LLMs on 2025 International Math Olympiad 🥉 matharena.ai/imo/ Nice blog post from the team behind MathArena: Evaluating LLMs on Uncontaminated Math Competitions (arxiv.org/abs/2505.23281) providing independent analysis, debunking some claims about LLM performance on IMO.

New Essay by Blaise Agüera (@blaiseaguera.bsky.social): “AI Is Evolving—And Changing Our Understanding Of Intelligence”. Advances in AI are making us reconsider what intelligence is and giving us clues to unlocking AI’s full potential. www.noemamag.com/ai-is-evolvi...

【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/

8月7日18時からSakana AI初のApplied Engineer Open Houseを開催します！ Sakana AIのApplied Teamのメンバーが業務についてやSakana AIで働く魅力についてお話しします。会場参加（抽選）または、オンライン参加が可能です。 connpass.com/event/362760/ Connpassからの参加登録をお待ちしております！

【ハラリ×宇多田】「人間より創造的なAI」と、どう生きるか 「昔から私たちは、皆でたき火を囲んだり、物語を語り合ったり、動物を追い払ったり、あるいはただ楽しむために音楽を奏でてきました」 newspicks.com/news/1465471...

Hikaru Utada and Yuval Noah Harari talk about The Evolution of AI and Creativity youtu.be/xw-9mwZxl-0 Gotta admit, this was not on my bingo card!

Google’s Gemini 2.5 paper has 3295 authors arxiv.org/abs/2507.06261

Tokyo-based Sakana AI details a new Monte Carlo tree search-based technique that lets multiple LLMs cooperate on a single task, outperforming individual models (Ben Dickson/VentureBeat) Main Link | Techmeme Permalink

This post appeared under this Techmeme headline:

A nice 15-min video introducing the recent thought-provoking paper: “Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis” By Akarsh Kumar, Jeff Clune, Joel Lehman, Ken Stanley Paper: arxiv.org/abs/2505.11581 www.youtube.com/watch?v=o1q6...

Sakana AI’s TreeQuest: Deploy multi-model teams that outperform individual LLMs (VentureBeat) venturebeat.com/ai/sakana-ai...

Sakana AIではApplied Teamの立ち上げを急速に進めており、優秀なApplied Research Engineerを引き続き募集しています🚀 sakana.ai/careers/#app... 正社員だけでなく学生インターンシップも歓迎です✨ 金融・保険などのエンタープライズ分野から政府・防衛などの公共分野での業務に興味のある方 最先端のAI技術を実社会に導入してインパクトを出したい方 雇用期間や勤務スタイルの相談もできますのでぜひご応募ください！

Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search arxiv.org/abs/2503.04412

AIも「3人寄れば文殊の知恵」、Sakana AIが新しい推論手法を開発 3人集まれば1人よりも優れた知恵が出るということわざ「3人寄れば文殊の知恵」が、AIにも当てはまった格好だ。 🐡🐟🐠 xtech.nikkei.com/atcl/nxt/new...

AIにもっと“試行錯誤”と“集合知”を─Sakana AIが開発する新アルゴリズム wired.jp/article/saka... フロンティアモデルと呼ばれるAIを単体ではなく“混ぜて”使えば、個々のモデル─ChatGPT、Gemini、DeepSeek─を使うよりも大幅に上回る成績を出すことが可能だと、日本発AIスタートアップのSakana AIが発表した。

Just published a blog post on our new LLM answer search method: "Multi-LLM AB-MCTS”🚀 It's designed to flexibly explore how to search and which LLM to use for any given problem. We've also open-sourced the implementation and experiments. Check it out! 🙌

フロンティアAIモデルを「混ぜて使う」── 「試行錯誤」と「集合知」で新たな推論時スケーリングへ ブログ： sakana.ai/ab-mcts-jp/ 論文： arxiv.org/abs/2503.04412 このたびSakana AIは新アルゴリズム「AB-MCTS」を開発し、ARC-AGI-2ベンチマークで有望な結果を得ました。

Inference-Time Scaling and Collective Intelligence for Frontier AI sakana.ai/ab-mcts/ We developed AB-MCTS, a new inference-time scaling algorithm that enables multiple frontier AI models to cooperate, achieving promising initial results on the ARC-AGI-2 benchmark.

We believe this work represents a step toward a future where AI systems collaboratively tackle complex challenges, much like a team of human experts, unlocking new problem-solving capabilities and moving beyond single-model limitations. Algorithm (TreeQuest): github.com/SakanaAI/tre...

This research builds on our 2024 work on evolutionary model merge, shifting focus from “mixing to create” to “mixing to use” existing, powerful AIs. At Sakana AI, we remain committed to pioneering novel AI systems by applying nature-inspired principles such as evolution and collective intelligence.

Our initial results on the ARC-AGI-2 benchmark are promising, with AB-MCTS combining o4-mini + Gemini-2.5-Pro + R1-0528, current frontier AI models, significantly outperforming individual models by a substantial margin.

AB-MCTS (Adaptive Branching Monte Carlo Tree Search) harnesses these individualities, allowing multiple models to cooperate and engage in effective trial-and-error, solving challenging problems for any single AI.

Inspired by the power of human collective intelligence, where great achievements arise from the collaboration of diverse minds, we believe the same principle applies to AI. Individual models possess unique strengths and biases, which we view as valuable resources for collective problem-solving.

We’re excited to introduce AB-MCTS! Our new inference-time scaling algorithm enables collective intelligence for AI by allowing multiple frontier models (like Gemini 2.5 Pro, o4-mini, DeepSeek-R1-0528) to cooperate. Blog: sakana.ai/ab-mcts Paper: arxiv.org/abs/2503.04412

When AI Is Designed Like A Biological Brain 🧠 youtu.be/dYHkj5UlJ_E

Reinforcement Learning Teachers of Test Time Scaling arxiv.org/abs/2506.08388 We introduce a new way to teach LLMs how to reason by learning to teach, not solve! Here, a teacher model is rewarded based on how effectively its explanations help the student model recover correct solutions.

RLTs are as effective even when distilling 32B students, much larger than the teacher itself—unlocking a new standard for efficiency in developing reasoning language models with RL.

Remarkably, an RLT with only 7B parameters produces superior results when distilling and cold-starting students in competitive and graduate-level reasoning tasks than orders-of-magnitude larger LLMs.

Enter our RLTs—a new class of models prompted with not only a problem’s question but also its solution, and directly trained to generate clear, step-by-step “explanations” to teach their students.

Traditional RL focuses on “learning to solve” challenging problems with expensive LLMs and constitutes a key step in making student AI systems ultimately acquire reasoning capabilities via distillation and cold-starting.

Introducing Reinforcement-Learned Teachers (RLTs): Transforming how we teach LLMs to reason with reinforcement learning (RL). Blog: sakana.ai/rlt Paper: arxiv.org/abs/2506.08388 Code: github.com/SakanaAI/RLT We introduce a new way to teach LLMs how to reason by learning to teach, not solve.