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Sakana AI

100 trust researcher @sakanaai.bsky.social · 1,304 followers
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Researcher with public evidence across AI research.

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Sakana AI is an AI R&D company based in Tokyo. 🗼🧠 Our website → https://sakana.ai/
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We are pleased to share our latest research, now published in Nature Communications: “Smart Cellular Bricks: Physical Modules That Recognize Their Own Shape and Repair Themselves.” Blog: sakana.ai/smart-cellul... Paper: www.nature.com/articles/s41... Thread 🧵

Smart cellular bricks for decentralized shape classification and damage recovery | Nature Communications nature.com
AI Weekly's analysis →
  • Cubic bricks running identical neural cellular automata policies classified four 3D shapes with 98.97% accuracy in simulation and 100% on physical hardware.
  • Physical builds ranged from 26 bricks for a guitar to 197 for a round table, converging on a shape label in fewer than 60 update cycles.
  • The same decentralized framework detects structural damage with over 90% accuracy and guides regrowth by predicting one of six axis directions.
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View on Bluesky · ♥ 51 ↻ 6 ↩ 2 · 3 from the directory shared this · 36d ago

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 (Th…

Sakana AI sakana.ai
View on Bluesky · ♥ 21 ↻ 2 ↩ 1 · 6 from the directory shared this · 57d ago

We are pleased to share our latest research, now published in Nature Communications: “Smart Cellular Bricks: Physical Modules That Recognize Their Own Shape and Repair Themselves.” Blog: sakana.ai/smart-cellul... Paper: www.nature.com/articles/s41... Thread 🧵

Smart Cellular Bricks: Towards Collective Intelligence for the Physical World sakana.ai
AI Weekly's analysis →
  • IT University of Copenhagen, Sakana AI, and Autodesk built cubic bricks that classify their own assembled 3D shape using only neighbor-to-neighbor communication.
  • In simulation the system hit 98.97% accuracy across 500+ bricks; four physical objects (26 to 197 bricks) all reached correct consensus in under 60 cycles.
  • The same Neural Cellular Automata substrate detects local damage at 94.8% average accuracy, with some shapes degrading only minimally at 15% brick failure.
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View on Bluesky · ♥ 51 ↻ 6 ↩ 2 · 3 from the directory shared this · 36d ago

Announcing Fugu-Ultra v1.1 🐡 We’ve been thrilled by the reception to the Fugu model family. Thanks to everyone who tried it, shared feedback, and trusted Fugu with real work. Today, we’re releasing Fugu-Ultra v1.1 → sakana.ai/fugu Upgraded to incorporate the latest frontier mo…

Sakana Fugu — Multi-agent System as A Model sakana.ai
AI Weekly's analysis →
  • Fugu routes tasks through a dynamic multi-agent pipeline exposed as a single OpenAI-compatible API, removing orchestration setup from users.
  • The system draws on two ICLR 2026 papers: TRINITY assigns Thinker/Worker/Verifier roles; Conductor uses reinforcement learning to design coordination strategies.
  • Fugu Ultra scored 73.7 on SWE Bench Pro and 93.2 on LiveCodeBench; base Fugu reached 95.5 on GPQA-D, per Sakana's own benchmark reporting.
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View on Bluesky · ♥ 57 ↻ 3 ↩ 1 · 2 from the directory shared this · 25d ago

The next frontier of Recursive Self-Improvement is Physical AI. Japan sparked the robotics revolution. We’re expanding our RSI Lab to build world models that allow agentic reasoning systems to recursively self-improve their ability to simulate, plan, and act in the real world.…

Sakana AI sakana.ai
AI Weekly's analysis →
  • Sakana AI is hiring a Member of Technical Staff for its Tokyo-based Recursive Self-Improvement (RSI) Lab, targeting researchers frustrated with brute-force scaling.
  • The role spans four tracks including world models as verifiable simulators for agentic reasoning and open-ended evolutionary dynamics applied to algorithmic domains.
  • Sakana points to prior work as receipts: Darwin Gödel Machine on SWE-bench, ALE-Agent winning AtCoder Heuristic Contest 058, ShinkaEvolve with 150 samples.
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View on Bluesky · ♥ 33 ↻ 2 ↩ 0 · 2 from the directory shared this · 4d ago

JST-CRDS（科学技術振興機構 研究開発戦略センター）のショートレポートに、Sakana AIのThe AI Scientistが、日本発の代表的な取り組みとして取り上げられました。 www.jst.go.jp/crds/column/... 同レポートは、科学研究におけるAIエージェントの現在地を、国内外の取り組みも交えて整理いただいています。AI Scientistについては、研究アイデアの生成、文献探索、コード作成、計算実験、データ解析、図表作成、論文執筆、査読までを一連の流れとして実行するシステムとして紹介されています。

jst.go.jp
View on Bluesky · ♥ 2 ↻ 1 ↩ 1 · 2 from the directory shared this · 8d ago

ベースモデルに依存しないオーケストレーションに向けて ブログ： sakana.ai/fugu-gemma4/ Sakana Fuguは、マルチエージェントのオーケストレーションを一つの基盤モデルとして提供するプロダクトです。一つのエンドポイントにリクエストを送ると、Fugu自身が処理の仕方を判断しモデル群に適切にタスクを割り振り、単一モデルを超える性能やコスト性能を達成します。 Fuguは、実際の処理を担う「モデルプール」と、どのモデルにどう任せるかを判断する「指揮者モデル」の二層構造です。モデルプールは当初から入れ替え可能な設計になっています。

Sakana AI sakana.ai
AI Weekly's analysis →
  • Sakana AI retrained its Fugu conductor model on Gemma 4 E2B and reports comparable performance to the earlier Qwen-based version.
  • Fugu is a two-layer setup: a small conductor routes work to a pool of frontier models the company calls hundreds-of-billions-scale.
  • Sakana frames the result as base-model-agnostic training, groundwork for a future conductor built on a domestically-developed base model for sovereignty use cases.
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View on Bluesky · ♥ 5 ↻ 2 ↩ 1 · 2 from the directory shared this · 8d ago
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