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RIKEN Center for Computational Science AI for Science Foundation Model Research Team

Team Principal: Rio Yokota (Ph.D.)

Research Summary

Rio Yokota

The Advanced General Intelligence for Science Program (TRIP-AGIS) aims to contribute to the dramatic acceleration of the scientific research cycle and the broadening of the scope of exploration within scientific fields, by developing and applying advanced technologies based on artificial superintelligence. The AI for Science Foundation Model Research team aims to develop technology for efficiently training and deploying foundation models with advanced reasoning capabilities, which can assist scientists with tasks such as, proposal of research concepts, planning of experimental programs, development of research code, conducting experiments and simulations, and the analysis of experimental results. To achieve this, we will 1) seek appropriate interfaces and interactions for the foundation models in each scientific field, 2) improve the architecture of the foundation models based on this, 3) develop evaluation benchmarks for scientific domains, and 4) maintain the software infrastructure to enable training and inference to be carried out at maximum speed on next-generation supercomputers.

Main Research Fields

  • Complex Systems

Related Research Fields

  • Informatics
  • Interdisciplinary Science & Engineering
  • Mathematical & Physical Sciences

Keywords

  • Ai for Science
  • Foundation Model
  • Reasoning Model
  • Scalable Deep Learning
  • Agentic AI

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.

  • 1. *Nakamura, T., Mishra, M., Tedeschi, S., Chai, Y., Stillerman, J. T., Friedrich, F., Yadav, P., Laud, T., Chien, V. M., Zhuo, T. Y. et al.
    "Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code"
    The 31st International Conference on Computational Linguistics (COLING), Industry Track, Abu Dhabi, UAE, Jan. 2025.
  • 2. *Niwa, K., Ishii, H., Sawada, H., Fujino, A., Harada, N., Yokota, R.
    "Natural Gradient Primal-Dual Method for Decentralized Learning"
    Transactions on Signal and Information Processing over Networks, 10, 417-433, https://doi.org/10.1109/TSIPN.2024.3388948 (2024).
  • 3. *Ma, Q, Yokota, R.
    "An Inherently Parallel H^2-ULV Factorization for Solving Dense Linear Systems on GPUs"
    International Journal of High Performance Computing Applications, 38(4), https://doi.org/10.1177/1094342024124202 (2024).
  • 4. *Ootomo, H., Ozaki, K., Yokota, R.
    "DGEMM on Integer Matrix Multiplication Unit"
    International Journal of High Performance Computing Applications, 38(4), https://doi.org/10.1177/10943420241239588 (2024).
  • 5. *Okazaki, N., Hattori, K., Hirai, S., Iida, H., Ohi, M., Fujii, K., Nakamura, T., Loem, M., Yokota, R., Mizuki, S.
    "Building a Large Japanese Web Corpus for Large Language Models"
    Conference on Language Modeling (COLM), Oct. 2024.
  • 6. *Fujii, K., Nakamura, T., Loem, M., Iida, H., Ohi, M., Hattori, K., Hirai, S., Mizuki, S., Yokota, R., Okazaki, N.
    "Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities"
    Conference on Language Modeling (COLM), Oct. 2024.
  • 7. *Otani, G., Tadokoro, R., Yamada, R., Asano, Y., Laina, I., Rupprecht, C., Inoue, N., Yokota, R., Kataoka, H., Aoki, Y.
    "Rethinking Image Super-Resolution from Training Data Perspectives"
    European Conference on Computer Vision (ECCV), Sep. 2024.
  • 8. *Nakamura, R., Tadokoro, R., Yamada, R., Asano, Y., Laina, I., Rupprecht, C., Inoue, N., Yokota, R., Kataoka, H.
    "Scaling Backwards: Minimal Synthetic Pre-training?"
    European Conference on Computer Vision (ECCV), Sep. 2024.
  • 9. *Yamada, R., Hara, K., Kataoka, H., Makihara, K., Inoue, N., Yokota, R., Satoh, Y.
    "Formula-Driven Visual-Geometric Pre-training"
    European Conference on Computer Vision (ECCV), Sep. 2024.
  • 10. *Shen, Y., Daheim, N., Marconi, G. M., Nickl, P., Cong, B., Clement, B., Yokota, R., Gurevych, I., Cremers, D., Khan, M. E., Möllenhoff, T.
    "Variational Learning is Effective for Large Deep Networks"
    The 41st International Conference on Machine Learning (ICML), Jul. 2024.

Related Links

Lab Members

Principal investigator

Rio Yokota
Team Principal

Careers

Position Deadline
Seeking Senior Scientist, Seeking Research Scientist or Postdoctoral Researcher (K24106) Open until filled

Contact Information

Nihonbashi 1-chome Mitsui Building, 15th floor
1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027,
Email: rio.yokota@riken.jp

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