So Kuroki @sharp_computer

Today, we are launching our first commercial product: Sakana Marlin, Your Virtual CSO. Marlin is an autonomous research assistant for business, built around hours of long-horizon reasoning. Try Marlin: sakana.ai/marlin Blog: sakana.ai/marlin-release… You provide a research topic, and that is the only input required. From there, Sakana Marlin works autonomously for up to roughly 8 hours. It forms hypotheses, gathers information, and verifies its own findings as it works through a vast body of material. It returns a structured set of summary slides and a research report dozens of pages long. It is designed to take on the kind of deep strategy work that a CSO and a small team might otherwise spend weeks on. Unlike instant chat or general-purpose deep research, Sakana Marlin executes a long reasoning process that unfolds over 8 hours. Underpinning it is the research we have pursued over the past two years into long-horizon reasoning and AB-MCTS, our method for coordinating multiple models to reason more effectively together. But Sakana Marlin did not come from the lab alone. It grew out of our work deploying AI agents across real industries in Japan, making it the direct product of what we have learned in both research and the field. Marlin is available today, offering a pay-per-use tier with no monthly fees, alongside Pro, Team, and Enterprise plans. It is also the first of many products to come from Sakana AI, stay tuned!

Sakana AI @SakanaAILabs

a day ago

🐠 Sakana Marlin リリース 🐠 本日、Sakana AIは、初の商用プロダクトとして、ビジネス向けの自律型リサーチアシスタント「Sakana Marlin」を提供開始しました。 詳細はこちら：sakana.ai/marlin ブログ：sakana.ai/marlin-release

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🐠 Sakana Marlin リリース 🐠 本日、Sakana AIは、初の商用プロダクトとして、ビジネス向けの自律型リサーチアシスタント「Sakana Marlin」を提供開始しました。 詳細はこちら：sakana.ai/marlin ブログ：sakana.ai/marlin-release

Today, we are officially launching the Sakana AI RSI Lab in Tokyo to build open-ended, adaptive AI systems that collectively self-improve. I am incredibly proud of our team’s work over the past 2 years, shipping the breakthrough research that laid the foundations for this moment. Building in Japan provides us with the ultimate design constraint. Just like Japan’s historical dominance in manufacturing was achieved by fundamentally redesigning the factory floor to do more with less, we are focused on compute-efficiency. We are not building the most compute-hungry self-improvement engine. We are building the most sample-efficient one. If you are entirely unsatisfied with the brute-force status quo and ready to build the self-improving future in Japan, come join us.

Sakana AI @SakanaAILabs

2 weeks ago

Building AI that Builds AI: Introducing the Sakana AI RSI Lab 🚀 sakana.ai/rsi-lab Today, we are announcing the Sakana AI Recursive Self-Improvement (RSI) Lab: a dedicated research group in Tokyo tasked with redesigning the AI development process itself using AI. While

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Building AI that Builds AI: Introducing the Sakana AI RSI Lab 🚀 sakana.ai/rsi-lab Today, we are announcing the Sakana AI Recursive Self-Improvement (RSI) Lab: a dedicated research group in Tokyo tasked with redesigning the AI development process itself using AI. While the industry increasingly speculates about the theoretical potential of self-improving AI, we’ve spent the last two years actively laying the foundations to make it a reality: ▪ LLM²: AI models automating research to invent better preference optimization algorithms. ▪ Darwin Gödel Machine: Agents autonomously rewriting their own codebase to double software-engineering performance. ▪ ShinkaEvolve: Hyper-sample-efficient program evolution that builds novel loss functions for MoE models. ▪ ALE-Agent: Reinforcement agents outperforming hundreds of human experts via self-learning. ▪ Digital Red Queen: Open-ended adversarial coevolution laying the groundwork for RSI in cybersecurity. ▪ The AI Scientist: Towards end-to-end automation of AI research, recently published in Nature. Now, we are unifying these breakthroughs. The Sakana AI RSI Lab is officially tasked with building open-ended, adaptive architectures that collectively self-improve. Human intelligence did not emerge from limitless resources; it was forged through the open-ended, compounding process of evolution operating under strict constraints. We are applying this exact principle to AI. We believe recursive self-improvement is achievable on modest, sample-efficient compute. It shouldn’t be a winner-take-all asset locked inside hyperscale clusters, but a democratized public good. We’re scaling our team to execute this mission. We are looking for frontier scientists and engineers who are entirely unsatisfied with the brute-force status quo. If you are ready to break away from standard benchmarking and build the self-improving future in Japan, come build with us.

Several people asked about the compute required for this research. Training takes about half a day on 2-3 GPUs. Ofc we used more compute during research and experimentation, but each iteration itself is lightweight. If you can SFT a 7B model (like Moshi), you can try it too.

KAME🐢 will be presented at tomorrow’s final session! Please stop by before you leave. 5/8 2:00–4:00 PM Poster Area 30.4 #ICASSP

Sakana AI @SakanaAILabs

2 months ago

We’re excited to introduce KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI, accepted at #ICASSP2026! 🐢 Blog pub.sakana.ai/kame/ Paper arxiv.org/abs/2510.02327 Can a speech AI think deeply without pausing to process? In real

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#ICASSP 我々のKAME🐢論文 (SLP-P56.4)は、本日の現地時間14:00からPoster Area 30で発表されます。学習データの構成法からモデル学習の実際まで、なんでも聞いてください。私も発表者の近くにいるハズなので、よろしくお願いします。一応ヘッドホン持って行きますが、スペース十分にあるかな？

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. By training a Conductor model with RL, we found that it naturally learns to write highly effective, custom instructions for a whole pool of other models. It essentially learns to ‘manage’ them in natural language. What surprised me most was how it dynamically adapts. For simple factual questions, it just queries one model. But for hard coding problems, it autonomously spins up a whole pipeline of planners, coders, and verifiers. Really excited to see where this paradigm of “AI managing AI” goes next, especially as we start moving from single-agent chain-of-thought to multi-agent “chain-of-command”. Link to our #ICLR2026 paper: arxiv.org/abs/2512.04388 Along with our TRINITY paper which we announced earlier, this work also powers our new multi-agent system: Sakana Fugu (sakana.ai/fugu-beta) 🐡

Sakana AI @SakanaAILabs

2 months ago

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?

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#ICML2026 に論文がacceptされました！今度は拡散言語モデルのtest-time scalingの研究です。 複数の拡散言語モデルに協力させるとコーディングや数学の能力が大きく上げられるという研究で、性能も良いしアルゴリズム自体も面白くてお気に入りの研究です。韓国でお会いしましょう🇰🇷

We’re excited to introduce KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI, accepted at #ICASSP2026! 🐢 Blog pub.sakana.ai/kame/ Paper arxiv.org/abs/2510.02327 Can a speech AI think deeply without pausing to process? In real conversation, we don’t wait until we’ve fully worked out what we want to say—we start talking, and our thoughts catch up as the sentence unfolds. Fast speech-to-speech models achieve this, but their reasoning tends to stay shallow. Cascaded pipelines that route through a knowledgeable LLM are smarter, but the added latency breaks the flow—they fall back to "think, then speak." In our new paper, we propose a way to break this trade-off. We call it KAME (Turtle in Japanese). A speech-to-speech model handles the fast response loop and starts replying immediately. In parallel, a backend LLM runs asynchronously, generating response candidates that are continuously injected as "oracle" signals in real time. This shifts the AI paradigm from "think, then speak" to "speak while thinking." The backend LLM is completely swappable. You can plug in GPT-4.1, Claude Opus, or Gemini 2.5 Flash depending on the task without changing the frontend. In our experiments, Claude tended to score higher on reasoning, while GPT did better on humanities questions. Try the model yourself here: huggingface.co/SakanaAI/kame

kameのweightとブログ記事公開だ！！ｻｶｰﾅいつもありがとう Cascade な full-duplex 大好きマンだけどﾒﾁｬやりたくなる huggingface.co/SakanaAI/kame

Sakana AI @SakanaAILabs

2 months ago

We’re excited to introduce KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI, accepted at #ICASSP2026! 🐢 Blog pub.sakana.ai/kame/ Paper arxiv.org/abs/2510.02327 Can a speech AI think deeply without pausing to process? In real

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音声AIの素早さと賢さを両立できるか？ 私たち人間は会話の中で、言いたいことを全部まとめてから話し始めるのではなく、話しながら考えを整理していきます。応答の速い Speech-to-Speech モデルは、この「話しながら考える」を実現しましたが、そのぶん思考が浅くなりがちです。かといって知識豊富な LLM を挟むカスケード型では、遅延が生じるため「話しながら」が成立しません。 そこで Sakana AI は、このトレードオフを克服するKAMEモデルを開発しました。Speech-to-Speech モデルが高速な応答ループを担当し、即座に話し始めます。その裏でバックエンドの LLM が非同期に推論を進めて応答候補を生成し、それをオラクル信号としてリアルタイムに注入します。これにより「考えてから話す」ではなく「話しながら考える」ことが可能になります。 バックエンドの LLM は差し替えが可能で、タスクに応じてGPT-4.1、Claude Opus、Gemini 2.5 Flashなどを使い分けられます。フロントエンド側の変更は必要ありません。私たちの実験では、Claudeは推論系のタスクで、GPTは人文系のタスクで、それぞれ高いスコアを出す傾向が見られました。 本研究は #ICASSP2026 で発表されます。 ぜひ、お試しください。 ブログ: pub.sakana.ai/kame/ 論文: arxiv.org/abs/2510.02327 モデル: huggingface.co/SakanaAI/kame

Sakana AI @SakanaAILabs

2 months ago

We’re excited to introduce KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI, accepted at #ICASSP2026! 🐢 Blog pub.sakana.ai/kame/ Paper arxiv.org/abs/2510.02327 Can a speech AI think deeply without pausing to process? In real

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🐢の紹介です。重みとコードが公開されます。是非お試ししてみてください！

Sakana AI @SakanaAILabs

2 months ago

We’re excited to introduce KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI, accepted at #ICASSP2026! 🐢 Blog pub.sakana.ai/kame/ Paper arxiv.org/abs/2510.02327 Can a speech AI think deeply without pausing to process? In real

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音声対話モデルの研究をしていました！既存のspeech-to-speech (Moshi)と話した時に、その応答の自然さに感動する一方で、もう少しだけ賢くしたいと思ったのが研究のきっかけです。 5月のICASSPで発表します。モデル、コードも公開しているのでぜひ試してみてください！

Sakana AI @SakanaAILabs

2 months ago

We’re excited to introduce KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI, accepted at #ICASSP2026! 🐢 Blog pub.sakana.ai/kame/ Paper arxiv.org/abs/2510.02327 Can a speech AI think deeply without pausing to process? In real

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We’re launching the beta for our new commercial AI product: Sakana Fugu 🐡, a multi-agent orchestration system! Blog: sakana.ai/fugu-beta Fugu hits SOTA on SWE-Pro, GPQA-D, and ALE-Bench, and has been our internal secret weapon. It dynamically coordinates frontier models, autonomously selecting the optimal agent combinations and roles for each task. 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 test here: forms.gle/BtKkhc2CfLKk1d…

音声系のインターンを募集しています。音声インターフェースを使ったアプリケーションを作ることに興味のある人、誰か一緒に働きませんか？DMください。

サカナAI @SakanaAILabs の伊藤社長と意見交換。防衛大臣直轄の吉田AIチーム長ら職員も参加して非常に有意義な時間になりました。ありがとうございました！

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Joined Sakana AI as a Research Intern 🐟 Super excited 🔥

🐟Ultra Deep Researchアシスタント「Sakana Marlin」、βテスター募集🐟 Sakana AIは、当社初の商用プロダクトとして、独自のエージェント技術によるビジネス向けAIリサーチアシスタント「Sakana Marlin」を開発しました。 sakana.ai/marlin-beta Sakana Marlinは、高度なビジネス調査を完遂する 、独自の長期推論技術に基づく自律型リサーチアシスタントです。 主な特徴 ・ テーマを与えると、8時間近くにわたり自律的にリサーチ ・ 詳細な調査ドキュメントとまとめスライドを自動生成 ・ 複数人のチームが数週間かけるプロフェッショナルな戦略調査を想定 複雑な社会情勢の中で良質な判断を下すため、AIのポテンシャルを最大限生かすソリューションとして構想しました。 本技術は、先日Nature誌にも掲載された科学的発見の自動化「AIサイエンティスト」の知見と、戦略的探索を可能にする「AB-MCTS」を融合。長く考えた分だけアウトプットの質が向上する「効率的な推論スケーリング」を実現しています。 クローズドβテストを実施します 金融機関・事業会社の経営戦略/事業企画部門、コンサルファーム、シンクタンクなど、日常的に高度なリサーチに取り組む方が対象です（期間中無料）。皆様からのフィードバックをもとに改善を重ねていきます。 ▼ クローズドβテスター応募はこちら forms.gle/MYHGP1wi2q4PHY…

Sakana Chatの公開です！ 今回開発した「Namazu」モデルは、DeepSeek-V3.1等のオープンLLMに事後学習を適用したものです。優れた性能を維持しながら、日本での利用に適した振る舞いをします。Web検索機能についてもよく作り込んでいるので、日常用途には十分実用的だと思います。是非お試し下さい。

Sakana AI @SakanaAILabs

3 months ago

🐟 Sakana Chat 公開 🐟 Sakana AIは、Sakana Chatを無料公開しました。 chat.sakana.ai Web検索機能と高速レスポンスを備えたAIチャットです。日本国内から、どなたでもお使いいただけます。ぜひ、お試しください。

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