This record is Version 1.3.1 of the discussion paper. It updates Version 1.3, DOI: https://doi.org/10.5281/zenodo.20754799. Version 1.3.1 is a minor clarification and documentation update to Version 1.3. It includes the updated English version of the discussion paper and the corresponding Japanese machine-assisted translation as an accompanying reference file. In addition to the AI concierge input-output clarification and curated legacy knowledge updates, this version adds limited explanatory text for international readers on Japan's institutionally distinct responsibilities for policy oversight, facility ownership, public-use promotion, proposal selection, safety approval, user support, data policy, and facility operation. The files included in this version are: the English LaTeX source, the English PDF, the Japanese LaTeX source, and the Japanese PDF translation. Compared with Version 1.3, the English text has been updated in a limited and focused way. Version 1.3.1 clarifies that the current SPring-8 access model should not be interpreted as a simple or deficient system, but as a knowledge-rich system whose proposal records, user-support experience, beamline expertise, operational knowledge, and outcome-based learning are distributed across facility-specific pathways. Version 1.3.1 also clarifies the concept of curated legacy knowledge. It emphasizes that SPring-8's accumulated operational and user-support legacy should not be regarded as a constraint to be replaced by AI-based systems, but as a high-quality knowledge resource on which reliable AI-assisted support should be built. To function as AI-ready curated legacy knowledge, accumulated records and tacit practices need to be analyzed, classified, validated, curated, and continuously updated in forms usable by both human experts and AI-assisted support tools. The update further makes explicit that effective implementation requires bottom-up identification, curation, validation, and updating of facility knowledge accumulated through daily operation and user support. This includes not only past proposal and support records, but also operational workflows through which new information is captured, reviewed, and added to the knowledge base. Version 1.3.1 newly defines the input-output contract of the AI concierge. It distinguishes three layers of input: user-side input, including scientific or industrial objectives, sample information, target properties or structures, preferred or excluded methods, time constraints, confidentiality or publication requirements, safety concerns, remote or mail-in preferences, and data-analysis needs; facility-side input, including facility and beamline capabilities, energy ranges, instruments, detectors, sample environments, automation level, access routes, proposal formats, safety requirements, support capacity, and data-management conditions; and evidence-side input, including public papers, public reports, representative use cases, manuals, FAQs, benchmark or round-robin data, and permissioned or anonymized consultation and proposal examples where their use is allowed. The update also clarifies the expected outputs of the AI concierge as human-reviewable support packages rather than final decisions. These outputs may include candidate facilities, beamlines, and methods; reasons for recommendations; retrieved supporting sources; uncertainty or missing-information flags; suitable proposal routes; draft text or structured fields for facility-specific applications; preliminary safety and regulatory checklists; preparation guidance for remote, mail-in, or on-site experiments; data-management and analysis-support suggestions; and handoff summaries for user offices, beamline scientists, safety officers, or data-support teams. Version 1.3.1 further states that recommendation logs, retrieved sources, user confirmations, and human overrides should be retained as restricted operational records for audit, quality improvement, and accountability. It also clarifies that the AI concierge should translate user questions into structured, evidence-linked, and facility-aware guidance, while proposal review, safety approval, beamtime allocation, contractual acceptance, and final technical judgement remain with accountable human processes. The update additionally clarifies that the purpose of a common AI concierge layer is not to make all facilities identical. Instead, selected capability, access, safety, and use-case metadata should be made interoperable through a minimal common schema while each facility retains its own rules, strengths, support structure, and governance. Version 1.3.1 also adds a clarification for international readers that Japan's large photon-science facilities operate under institutionally distinct responsibilities. It distinguishes policy oversight, facility ownership, public-use promotion, proposal selection, safety approval, user support, data policy, and facility operation. In particular, it clarifies that MEXT is discussed as a policy and public-accountability body, not as a beamline operator or as the authority for individual experimental decisions, and that RIKEN/RSC, JASRI, participating facilities, review bodies, safety authorities, and user communities should not be collapsed into a single decision-making structure. The update further clarifies the governance boundary of the Photon Science Portal and cross-facility coordination. Coordination is defined not as centralized control, but as shared user guidance, technical referral, consultation records, common terminology, and accountable handoff where participating organizations agree that such coordination is useful and feasible. The portal would not merge proposal systems, beamtime allocation, safety approval, user agreements, data-policy authority, or facility governance; it would provide a common user-facing guidance and handoff layer across institutionally distinct facilities. Version 1.3.1 additionally clarifies the significance of the SPring-8-II transition period, including the long suspension of regular user operation. This period should not be regarded as a simple interruption of beamtime provision, but as a concentrated period for preparation, upgrade, commissioning readiness, and knowledge-infrastructure development for SPring-8-II. The update clarifies that JASRI researchers, engineers, beamline scientists, technical staff, and user-support staff will have essential work during this period, including equipment, control, detector, and data-acquisition upgrades; redesign of measurement methods; preparation for recommissioning; and the externalization, documentation, and curation of tacit operational and user-support knowledge. The update also clarifies that user-support, industrial-use, international-support, administrative, safety, contractual, and publication-policy staff have essential roles during the transition period. These include maintaining user consultation pathways, supporting alternative and complementary facility use, preparing remote and mail-in workflows, improving data-management rules, and maintaining the user community's continuity. Accordingly, the long suspension period should be evaluated not only in terms of delivered beamtime, but also as a period in which JASRI sustains and upgrades the human, technical, operational, and knowledge bases required for the prompt and reliable restart of SPring-8-II user operation. The tacit knowledge to be documented and curated includes practical judgement on proposal feasibility, beamline and method selection, sample handling, troubleshooting, data-analysis workflows, user communication, and lessons learned from successful and unsuccessful experiments. It should be curated through templates, interviews, expert review, classification, version control, and governance rules so that it can become a reusable knowledge base for both human support and AI-assisted navigation. The figure structure, conceptual framing, main proposal, and overall policy position remain unchanged from Version 1.3. The main figure-label and caption updates clarify the interpretation of the current model as a knowledge-rich but fragmented system and align the figure with the discussion of curated legacy knowledge and the AI concierge as a knowledge-circulation layer. The roadmap remains unchanged in structure, with added implementation items on documenting tacit operational and user-support knowledge, using the SPring-8-II transition period for preparation, upgrade, and knowledge-infrastructure development, and treating the AI concierge as an input-output support layer that connects user needs, facility portfolios, governed evidence sources, human review, and accountable handoff. The Japanese translation has been updated to correspond to the English Version 1.3.1, including the added clarification on institutional responsibilities, MEXT's policy and public-accountability role, and the non-centralized nature of the Photon Science Portal. It is provided to improve accessibility for Japanese readers and to support domestic discussion. The English version remains the authoritative version. The Japanese translation is provided for reference and communication purposes and should be interpreted in relation to the English original. This strategic discussion paper proposes a user-centric and AI-enabled access framework for the SPring-8-II era. It treats SPring-8-II not only as a major source upgrade, but also as an opportunity to redesign how Japan's photon science infrastructure supports scientific discovery, industrial innovation, data-intensive research, and national strategic missions. The paper argues for a transition from facility-first access, in which users must understand institutional and beamline boundaries in advance, to question-first access, in which users begin from their scientific or industrial objectives and are guided toward appropriate facilities, beamlines, access routes, consultation pathways,
Osami Sakata (Sat,) studied this question.