The Kyoto Vision for a Golden Age of Science is a shared policy direction, not a funded implementation plan. Endorsed in Kyoto on October 4, 2026, by the United States and 16 other countries, it links AI-enabled discovery with research-system reform, scientific integrity and investment in talent. It encourages wider use of what it calls “Super Intelligence” (SI), but does not define that term or set a common budget, timetable or measurable target.
What the Kyoto Vision is
The one-page declaration was endorsed alongside the 23rd annual Science and Technology in Society (STS) Forum meeting in Kyoto, Japan. The White House described the coalition as 17 countries in total: the United States and 16 other endorsers. The declaration presents science and technology as contributors to healthier, more prosperous societies, and argues that new tools and ways of organizing research are opening new frontiers.
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Its central aim is to renew science ecosystems so they can produce new knowledge. In the White House announcement, Michael Kratsios, Director of the White House Office of Science and Technology Policy, said: “The way we make scientific discoveries has changed, and the Kyoto Vision is a recognition of that shift and a call to action.” The declaration itself says: “We therefore endeavor to renew our science ecosystems and strengthen their capacity to produce new knowledge, so that the decades ahead may mark a new golden age of discovery and prosperity.”
What it proposes for research institutions and funding
The Vision treats AI as one part of a broader change in how science is organized and supported. It calls for more researcher productivity, fewer unnecessary administrative burdens and funding systems that make room for ambitious, transformative ideas. Rather than prescribing one funding model, it values a portfolio of approaches:
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- Long-duration awards that allow researchers time to pursue difficult questions.
- Rapid grants for work that needs timely support.
- Prizes and challenges to encourage ambitious research.
- Experiments with institutional models offering different levels of autonomy, resources and direction.
It also welcomes metascience: the use of empirical methods to improve how science is conducted, organized and funded. That focus matters because the declaration is not simply asking researchers to add AI tools to existing workflows; it also encourages institutions to examine whether their incentives and structures support discovery.
What “Super Intelligence” means in the declaration
“Super Intelligence” is the document’s own term, but it supplies no formal definition, technical threshold or claim that a general-purpose, human-level AI scientist already exists. Instead, it describes functions that SI could support in research:
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- Reasoning across large bodies of knowledge.
- Building higher-fidelity models of physical and biological systems.
- Driving autonomous, closed-loop discovery, in which computational systems can help guide successive research or experimental steps.
The declaration says SI is accelerating some existing research tasks and enabling new kinds of inquiry. Its emphasis is on thoughtful integration into research workflows, not adoption of a specified product or a claim that these capabilities are equally mature across scientific fields.
What access the Vision encourages
The signatories express an aim to expand access to SI tools, scientific data, computing infrastructure and experimental facilities “at the scale researchers now require.” The text does not allocate infrastructure, name suppliers or set country-by-country access rules. It identifies the resources considered important without establishing how they will be funded or distributed.
Data and laboratory readiness can be field-specific constraints. RIKEN AGIS, separately from the Kyoto declaration, describes polymer research as involving costly, multistage experiments and fragmented datasets that are not yet sufficiently suitable for AI. That example illustrates why access to computing alone may not be enough: useful AI research can also depend on well-documented, appropriately structured data and experimental capacity.
How the Vision addresses integrity and public trust
The declaration pairs faster, more automated discovery with expectations for trustworthy science. It states: “We acknowledge that public trust is the foundation on which the scientific enterprise rests and must be earned through rigor and integrity.” The practices it highlights include:
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- Reproducibility and transparency.
- Clear communication of error and uncertainty.
- Unbiased peer review.
- Valuing negative and null results.
These safeguards are especially relevant when AI systems contribute to analysis or decisions: a result should remain open to scrutiny even when the method is complex. RIKEN AGIS notes that AI opacity can create tension with reproducibility, transparency and accountability; careful experiments and systematic records of research conditions can help address that tension. This is implementation context, not a provision of the Kyoto Vision itself.
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The talent section calls for early investment in high-potential students and early-career researchers, giving them support, freedom and opportunity to pursue ambitious work. It also recognizes that advanced science relies on people who can operate, maintain and improve sophisticated instruments, not only on researchers who design studies or interpret results.
Among the approaches it encourages are hands-on training, joint doctoral programs, fellowships and collaboration across scientific environments. This broad view of scientific talent connects AI-enabled research to the practical expertise needed to run experimental facilities and sustain research infrastructure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the Vision does—and does not—commit its endorsers to
The declaration records common aims and encouragements. It does not set a shared budget, numerical targets, an infrastructure allocation scheme or a common delivery date. The White House release reports the endorsement and summarizes the agenda; neither document establishes that each signatory has already put every aim into practice. Specific national actions and results therefore need to be assessed through separate, country-level evidence.
Kyoto also has local scientific initiatives that should not be confused with the declaration. Kyoto University’s Institute of Generative Science preparatory office describes an interdisciplinary effort spanning physics, mathematics, information science, philosophy, literature and law to explore how scientific knowledge and principles emerge alongside generative AI. The site identifies itself as a temporary preparatory-office website; it is not presented as a signatory or as the body behind the Vision.
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The Vision’s significance is its breadth: it connects AI-supported research with funding design, institutional experimentation, integrity, access to scientific resources and workforce development. Its language sets an agenda for cooperation, while leaving the operational choices and evidence of progress to future action. For readers assessing what happens next, the key questions are whether institutions make the promised resources accessible, whether research workflows preserve scrutiny and reproducibility, and whether talent programs include both scientific researchers and the people who sustain complex instruments.
Read the Kyoto Vision for a Golden Age of Science and the White House announcement of the international endorsement.
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