Sakana AI partnered with DEEP DIVE, a private intelligence organization, to combine Sakana's AI technology with DEEP DIVE's defense and geopolitical expertise and open-source data for information analysis. The partnership aims to conduct analysis at scales, speeds, and resolutions previously difficult to achieve manually through joint research. Sakana AI is positioning defense and intelligence alongside finance as a strategic focus area to accelerate implementation of advanced AI technology.
Sakana AI launched an Applied Team in early 2025 to implement AI technology based on swarm intelligence into finance and defense sectors. Software engineers at the company are developing AI agents to support banking loan workflows, handling tasks like initial analysis, information organization, and memo drafting while preserving human decision-making. The company aims to make its technology a standard for AI adoption in Japanese enterprises by creating software where AI naturally integrates into work processes alongside human judgment.
Sakana AI announced the establishment of its RSI Lab in Tokyo to develop recursive self-improvement technology for AI systems that can autonomously improve themselves through efficient, sample-based optimization rather than compute scaling. The company has spent two years building practical systems like ShinkaEvolve (requiring only 150 samples to solve intractable problems) and ALE-Agent (outperforming 804 human specialists), positioning itself as a leader in sample-efficient self-improvement. By pursuing AI development under Japan's compute constraints, Sakana AI aims to create self-improving systems that generalize beyond hyperscale approaches and establish a sustainable path toward autonomous AI research capabilities.
Sakana AI released Sakana Marlin, its first commercial product, an autonomous research assistant that conducts business research independently for up to eight hours and generates structured summary slides and detailed reports. The system underwent a closed beta test from April 2026 with approximately 300 professionals from financial institutions, consulting firms, and think tanks who used it for strategy formulation, market research, and competitive analysis. The release enables research teams to shift focus from information gathering to higher-value decision-making by automating comprehensive research and analysis tasks.
Sakana AI released Fugu Ultra, a multi-agent orchestration system that dynamically coordinates multiple language models to perform complex tasks through a single API. Fugu Ultra matches the performance of leading models like Anthropic's Fable 5 and Mythos Preview on engineering, scientific, and reasoning benchmarks while avoiding export control restrictions. The system allows organizations to reduce vendor dependency and maintain access to frontier-level AI capabilities even if individual model providers restrict access.
Sakana AI co-founder Ren Ito has been appointed to the newly established UN AI for Good Global Commission, joining approximately 40 global leaders from government, industry, and international organizations. The commission, co-chaired by Salesforce's Marc Benioff and including NVIDIA's Jensen Huang and Pfizer's Albert Bourla, will convene its inaugural meeting in Geneva in July 2026. Sakana AI will participate in international discussions on AI trustworthiness, governance, and social implementation to advance responsible AI adoption aligned with UN sustainable development goals.
Sakana AI launched Sakana Translate, a free web-based translation service supporting Japanese, English, and Chinese bidirectional translation using their Namazu model adapted for Japanese language and culture. In XCOMET-XL evaluation on WMT 2024 General Translation tasks, Sakana Translate achieved scores competitive with leading translation models. The service offers three modes—translate, proofread, and ask—with plans for industry-specific variants and enterprise features including API access and on-premises deployment.
Sakana AI is integrating NVIDIA's Nemotron open models into its Fugu multi-agent orchestration system, which coordinates multiple specialized models to handle complex tasks. In early evaluations, the orchestration-based approach has shown strong performance alongside leading frontier systems, demonstrating that model coordination can serve as a scaling path. This collaboration aims to create a feedback loop where open models, orchestration, and real-world use reinforce each other, reducing dependence on single providers and enabling more capable AI systems.
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