Who's Who of AI

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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Introducing Multi-Agent Self-Supervision (MASS): Recursive self-improvement through collective intelligence 🧵 Blog: pub.sakana.ai/mass/ Paper: arxiv.org/abs/2610.12176 Code: github.com/SakanaAI/mass

Recursive Self-Improvement through Multi-Agent Self-Supervision arxiv.org
AI Weekly's analysis →
  • A new arxiv paper proposes MASS, which alternates evolutionary workflow optimization with supervised fine-tuning on self-generated multi-agent trajectories.
  • With Qwen3.6-27B over two MASS cycles, the authors report 1.2-1.6x higher performance per output tokens on four open-ended public benchmarks.
  • Multi-agent traces look more training-efficient: a student trained on them beats a single-agent student trained on 1.4x more tokens.
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View on Bluesky · ♥ 32 ↻ 8 ↩ 1 · 4 from the directory shared this · 7d 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 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 · 98d ago

The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery GitHub: github.com/SakanaAI/AI-... Blog: pub.sakana.ai/agentic-scie... arXiv: arxiv.org/abs/2610.18933

The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery arxiv.org
AI Weekly's analysis →
  • The pipeline decomposes computational research into eight stages, each powered by coding agents with 'agentic self-verification' and rubric checks.
  • A development version of the system submitted a paper that was accepted for publication at ALIFE 2026 in co-scientist mode.
  • The authors' ablations argue reliable scientific autonomy depends on the cross-agent orchestration layer, not only on base model capability.
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View on Bluesky · ♥ 20 ↻ 5 ↩ 3 · 2 from the directory shared this · 3d ago

Introducing "Scaling In-Context Imitation Learning" (SAIL) to be presented at #IROS2026. This work is a collaboration between Sakana AI and the University of Tokyo. Blog: pub.sakana.ai/sail Paper: arxiv.org/abs/2603.08269 What does a robot need before it can tackle a new task?…

SAIL: Test-Time Scaling for In-Context Imitation Learning with VLM arxiv.org
AI Weekly's analysis →
  • SAIL reframes robot imitation as MCTS over full trajectories, guided by a VLM scorer, step-level feedback, and a retrieval archive of past successes.
  • Across six simulation manipulation tasks, average success rose from 25% at one rollout to 73% at 45 MCTS nodes, reaching 95% on HandOverBanana.
  • On a real-world BlockIntoBowl task with a LeRobot SO-101 arm, the method succeeded in five of six trials; the paper is accepted to IROS 2026.
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View on Bluesky · ♥ 15 ↻ 4 ↩ 2 · 2 from the directory shared this · 21d ago

The AI Picbreeder Experiment: Can AI agents be creative when nobody tells them what to create? Blog: pub.sakana.ai/picbreeder-vlm Paper: arxiv.org/abs/2605.23908 In our #GECCO2026 paper, we revisit Picbreeder, a website where people collaboratively evolved images without any p…

In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models arxiv.org
AI Weekly's analysis →
  • Researchers Sam Earle, Kai Arulkumaran, Andrew Dai, Akarsh Kumar, Julian Togelius and Sebastian Risi replicate Picbreeder with frontier vision-language models as users.
  • The system's output shows 'clear qualitative differences' from the historical human baseline on phylogenetic complexity and visual and semantic salience and novelty.
  • The team studies three candidate ingredients: exploratory noise in selection, behavioral diversity between agents, and memory-based narrative momentum.
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View on Bluesky · ♥ 20 ↻ 1 ↩ 1 · 2 from the directory shared this · 100d 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 · 119d ago

Introducing Fugu Max and Fugu Ultra v2: the next evolution of Sakana Fugu’s multi-agent orchestration system. Try: sakana.ai/fugu Blog: sakana.ai/fugu-max-rel... The frontier that actually matters is the Pareto frontier: capability on one axis, cost on the other.

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 · ♥ 30 ↻ 3 ↩ 1 · 3 from the directory shared this · 38d ago

Introducing Multi-Agent Self-Supervision (MASS): Recursive self-improvement through collective intelligence 🧵 Blog: pub.sakana.ai/mass/ Paper: arxiv.org/abs/2610.12176 Code: github.com/SakanaAI/mass

Multi-Agent Self-Supervision (MASS) pub.sakana.ai
AI Weekly's analysis →
  • In two rounds of training, the Round 2 model beat the base model on test tasks 69.9% of the time and beat its own Round 1 version 60.8%.
  • Multi-agent trajectories hit a 68.3% win rate while consuming 28.8M supervised tokens against 42M for the single-agent approach, a 31% saving.
  • On public benchmarks MASS reports 1.60× score-per-token on MLR-Bench, 1.35× on DSBench, 1.20× on ScienceAgentBench and 1.28× on AstaBench.
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View on Bluesky · ♥ 32 ↻ 8 ↩ 1 · 4 from the directory shared this · 7d ago
Their own posts

Recent commentary

Sakana AI、「日本スタートアップ大賞2026」で総務大臣賞（情報通信分野）を受賞。 表彰式では、当社より、防衛分野における指揮統制システムに最先端のAIを活用している事例について、総理にご紹介しました。 ⏵ 受賞ポイント（概略） 自然界の進化や集合知に着想を得た、効率的なAI開発手法を確立し、限られた計算資源下でも高性能な推論を実現するアーキテクチャを開発。さらに安全保障や労働力不足等の課題解決に取り組んでいる点。

View on Bluesky · ♥ 4 ↻ 0 ↩ 1 · 24d ago

At Tech Summit '26 in Christchurch, Sakana AI Research Scientist Stefania Druga spoke in front of ~700 industry leaders. She explained why we are all scientists now, why that makes human expertise matter more than ever, and why AI for Science is a Sovereign AI question.

View on Bluesky · ♥ 6 ↻ 0 ↩ 0 · 33d ago
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