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Sakana AI has released the Namazu API, an updated large language model with roughly 1 trillion parameters supporting live web search and code execution, powered by Modal.

Sakana AI introduces AB-MCTS, a new inference-time scaling algorithm that enables multiple frontier AI models to cooperate, significantly improving performance on the ARC-AGI-2 benchmark.

Sakana AI, in collaboration with MIT/NYU, published a study nominated for the GECCO 2026 Best Paper Award, which fully replicated the classic Picbreeder system using VLM agents to explore the key ingredients needed for open-endedness.

Lilian Weng's blog post argues that recursive self-improvement (RSI) in AI will be realized through refining the design and optimization of the 'harness' (the system surrounding the model), and highlights research examples from Sakana AI.

Sakana AI has released AI Scientist-v2, an end-to-end automated research system that can autonomously go from generating research hypotheses to writing papers, and has been accepted by the ICLR2025 Workshop after peer review.

Sakana AI launched Fugu, a multi-model orchestration API, after Anthropic suspended access to Claude Fable 5 and Mythos 5. Fugu Ultra scored 93.2 on LiveCodeBench, outperforming Fable 5, and starts at $5 per million input tokens.

The Sakana Fugu technical report introduces a trained orchestrator that dynamically selects and coordinates specialist models for tasks, with a faster version (Fugu) and a slower workflow version (Fugu-Ultra) that can design custom teamwork patterns per request.

Sakana AI releases Fugu, a multi-agent orchestration system with only 0.6B parameters. By intelligently splitting tasks and coordinating multiple models, it achieves state-of-the-art performance while bypassing traditional parameter scaling. This marks the transition of multi-agent orchestration from a lab curiosity to a practical productivity tool.

Sakana AI announces Fugu Ultra, a multi-agent orchestration model that matches frontier performance of Fable and Mythos while avoiding export controls.

A comparison of recent AI models, including Sakana AI's Fugu Ultra, demonstrates their ability to generate endless procedural terrain in Three.js with one-shot generation, highlighting frontier capabilities without export control risks.

Sakana AI releases Fugu Ultra, an orchestration layer that routes subtasks across multiple models via a unified OpenAI-compatible endpoint, matching performance of leading systems.

Sakana Fugu is a new tool that enables combining multiple AI models into one, inspired by the concept of 'one model to command them all'.

Sakana AI released Fugu Ultra, a multi-agent orchestration system accessible via a single model API, achieving performance competitive with Fable and Mythos models.

Elie Bakouch critiques Sakana AI's Fugu system as a closed-source orchestration layer over closed-source models, arguing it lacks transparency and true AI sovereignty, with technical limitations in routing and cost efficiency.

Sakana AI announced Sakana Fugu, a multi-agent orchestration system accessible via a single model API, with the Fugu Ultra model matching frontier performance without export control risks.

Sakana AI's new model Fugu outperforms Fable 5 on LiveCodeBench and Terminal Bench 2.1 by small margins, according to their corporate blog, though results are not yet independently confirmed.

Sakana Fugu dynamically orchestrates a diverse pool of top models to tackle complex, multi-step tasks via a single API, leveraging their ICLR 2026 papers on learned orchestration to achieve frontier-level performance without single-vendor dependency.

Sakana AI launches its first commercial product, Sakana Marlin, an autonomous research assistant that completes strategy work in hours by generating structured slides and detailed reports.

Sakana AI launches RSI Lab in Tokyo, dedicated to recursive self-improvement (RSI) where AI builds AI, aiming to achieve self-improvement without unlimited computational resources.

The article discusses new research from Sakana AI and Meta on self-improving AI agents, specifically the Darwin-Gödel Machine and Hyperagents, which autonomously rewrite their own code and infrastructure to enhance performance without human intervention.