This record is Version 1.4.4 of the discussion paper, DOI: https://doi.org/10.5281/zenodo.20956733. It updates Version 1.4.3, DOI: https://doi.org/10.5281/zenodo.20807739. Title: Redesigning Japan's Photon Science Knowledge Infrastructure and Circulation for the SPring-8-II Era Subtitle: User-Centric and AI-Enabled Support across Complementary Synchrotron and XFEL Facilities Version 1.4.4 is a clarification and reader-orientation update. The title is updated to emphasize Japan's photon-science knowledge infrastructure and knowledge circulation for the SPring-8-II era. The central argument and policy position remain unchanged from Version 1.4.3, while the conceptual framework, implementation logic, AI-concierge boundaries, knowledge-return infrastructure, knowledge-circulation capacity, and implementation safeguards are clarified for domestic and international readers. The purpose of this update is to make the structure and implementation logic of the paper easier to understand without changing the paper's basic position. Version 1.4.4 clarifies how question-first user support, the Photon Science Portal, the AI concierge, federated user support, proposal--experiment--report accountability records, data rights and responsibilities, protected lessons-learned records, and knowledge circulation are connected within the proposed SPring-8-II-era user-support framework across complementary synchrotron and XFEL facilities. First, a new conceptual framework section is added near the beginning of the paper. This section provides a reader-oriented conceptual map of the paper and explains how the proposed access framework should be read as a support, knowledge-infrastructure, and knowledge-circulation design rather than as a centralized governance proposal. Second, the paper strengthens the explanation of JASRI's distinctive implementation value. JASRI's role is described not only in terms of user-office administration, but also in terms of three connected implementation capacities: technical implementation capacity, operational implementation capacity, and knowledge implementation capacity. This clarification emphasizes that AI-assisted support should be built on accumulated facility expertise, user-support experience, and accountable human judgement. Third, Version 1.4.4 clarifies that the purpose of user support is not merely to process proposals or allocate beamtime more efficiently, but also to help users formulate high-quality scientific or industrial questions and connect them to appropriate experimental, safety, data, and expert-support workflows. This strengthens the discovery-oriented and value-creation perspective of the paper. Fourth, the description of the AI concierge is made more concrete while preserving the boundary conditions. The AI concierge is described as an evidence-linked preparation, navigation, and handoff layer. It should help users clarify objectives, identify candidate methods and facilities, flag sample-environment, safety, confidentiality, and data-management issues, and prepare reviewable consultation packages for human experts. It should not replace peer review, safety approval, beamline-scientist judgement, beamtime allocation, proprietary-status decisions, legal or compliance judgement, confidentiality handling, or formal institutional decision-making. Fifth, Version 1.4.4 adds a new subsection on proposal and report records as knowledge-return infrastructure. Proposal forms, experiment logs, and post-experiment reports are treated as connected accountability records rather than separate administrative documents. This update clarifies how planned data classes, disclosure conditions, confidentiality constraints, AI-use permissions, access-control rules, deviations from experimental plans, data-quality records, and lessons learned could be returned to future user support, knowledge-base development, and knowledge circulation. Sixth, the paper strengthens the discussion of data rights and responsibilities. Data ownership is not treated as a simple property concept, but as a practical bundle of access rights, usage permissions, stewardship duties, confidentiality obligations, disclosure conditions, AI-use permissions, and retention rules. This clarification supports a more accountable approach to data governance, especially in collaborative, industrial, and intermediate-disclosure contexts. Seventh, the paper introduces protected lessons-learned records as a concept for capturing negative, inconclusive, aborted, or partially successful outcomes in a non-punitive and confidentiality-respecting manner. Such records can preserve reusable scientific and operational knowledge about boundary conditions, unsuitable parameter ranges, sample-preparation issues, beamline constraints, and analysis limitations, provided that disclosure, confidentiality, and AI-use permissions are respected. Eighth, the paper improves the linkage between the main text and the implementation appendices. The roadmap and implementation discussion are more explicitly connected to phased design priorities, data governance, AI accountability, risk safeguards, and user-support implementation considerations. Version 1.4.4 continues to treat SPring-8-II not only as a major source upgrade, but also as an opportunity to redesign user support, complementary use, data workflows, knowledge infrastructure, and knowledge circulation for Japan's photon science ecosystem. Access routes, proposal categories, beamtime allocation, industrial access, remote and mail-in use, data workflows, international user support, and cross-facility guidance are treated as parts of a broader user-support and knowledge-circulation workflow rather than as separate administrative issues. The proposed Photon Science Portal continues to be described as a user-facing guidance, referral, and accountable handoff layer, not as a single global portal, a centralized decision-making authority, or an operational commitment already agreed among facilities. Any future guidance, referral, or handoff support should respect the operator, governance structure, access rules, technical responsibilities, data policies, and expertise of each participating facility, and should be implemented only under appropriate institutional agreement. The AI concierge continues to be described as a human-accountable support tool for navigation, proposal preparation, preliminary screening, knowledge retrieval, safety and feasibility checks, and handoff to experts. It should not replace peer review, safety approval, beamline-scientist judgement, beamtime allocation, proprietary-status decisions, legal or compliance judgement, confidentiality handling, or formal institutional decision-making. Version 1.4.4 includes the English version of the discussion paper, the accompanying Japanese machine translation, and explanatory slide PDFs in both English and Japanese. The files included in this version are: the English LaTeX source, the English PDF, the Japanese PDF machine translation, the English explanatory slide PDF, and the Japanese explanatory slide PDF. The Japanese machine translation and the Japanese explanatory slide PDF are provided to improve accessibility for Japanese readers and to support domestic discussion. They are accompanying reference and communication materials, and should be interpreted in relation to the English original. The Japanese explanatory slide PDF is a summary-oriented explanatory aid rather than an additional authoritative version of the discussion paper. In case of any ambiguity or discrepancy, the English version should be regarded as authoritative. This document is an author-prepared strategic discussion paper for international dialogue. It does not represent an official policy decision of RIKEN, JASRI, MEXT, or any facility-governance body. The views, proposals, and interpretations expressed in this paper are those of the author alone and do not represent the official views, policies, or decisions of the author's affiliated institution or any related organization.
Osami Sakata (Sat,) studied this question.