Sakana AI

Benchmarks tell only part of the story. Fugu’s real value shows up in long, messy, real-world workflows. During our beta with 500 users, we saw Fugu Ultra drive meaningful progress in fully automated tasks from data science to complete cybersecurity assessments. Our early users saw Fugu explore, interpret failures, and sustain progress with almost zero human intervention. The feedback has been incredible. Here is what they are saying:

Use Case 1: Autonomous ML Research Can an AI autonomously improve another AI’s training recipe? We tasked Fugu Ultra with improving a small GPT model using AutoResearch. Over 14 hours on a single H100 GPU, Fugu ran > 100 experiments. It iteratively edited the training code, ran tests, and kept any changes that successfully lowered the validation error rate. Watch the animation. The callouts track every time Fugu Ultra autonomously discovered a new improvement across batch size, model depth, learning rates, and optimizer settings. We pitted Fugu against three frontier models (Gemini 3.1 Pro, Opus 4.8, and GPT 5.5). To keep the focus purely on agentic behavior rather than brand wars, we anonymized them as Models A, B, and C. The Results: • Fugu Ultra (bold red) finished with the best mean performance (0.9774). • Fugu Ultra also achieved the best single run of the entire experiment (0.9748), leading every single baseline. For long horizon, agentic ML research, using Fugu to dynamically orchestrate a pool of strong models significantly outperforms relying on any individual monolithic model.

GIF

🐡 Sakana Fugu リリース 🐡 マルチエージェントを指揮する、一つのモデル ブログ：t.co/8Bac6JpVEN 本日、Sakana AIは新しいプロダクト「Sakana Fugu」の提供を開始しました。Sakana Fuguは、最先端のモデル群からなるマルチエージェントシステム全体を、単一のモデルAPIとして提供します。 難しいタスクは、強力なモデルを一度呼ぶだけでは解けません。どのモデルを使い、タスクのどの部分を委譲し、どう検証し、強みをどう組み合わせるか。Sakana Fuguは、その「協調の仕方」そのものを学習した言語モデルです。一つのAPIにリクエストを送るだけで、背後で専門モデル群を自動的に連携させ、フロンティアレベルの性能を複雑な運用なしで引き出します。 詳しくはこちら： t.co/hhO6qTawgb 🐡