OMRON SINIC X Corporation (HQ: Bunkyo-ku, Tokyo; President and CEO: Masaki Suwa; hereinafter "OSX") presents the latest research findings at The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS2025).

"NeurIPS 2025" is one of the largest and most influential top conferences in the field of machine learning and artificial intelligence (AI). In 2025, 5,290 papers (approximately 24.52%) were accepted out of 21,575 submissions, and the conference will be held in San Diego, USA, from December 2 to December 7 (local time).

The two research papers to be presented by OSX are as follows. In particular, the first paper was accepted as a Spotlight presentation.

1) Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation

Authors	Toshinori Kitamura (OSX), Arnob Ghosh (New Jersey Institute of Technology), Tadashi Kozuno (OSX), Wataru Kumagai (OSX), Kazumi Kasaura (OSX), Kenta Hoshino (Kyoto University), Yohei Hosoe (Kyoto University), Yutaka Matsuo (The University of Tokyo)
Research Introduction	Constrained Markov Decision Processes (CMDPs) are widely used as a formulation for reinforcement learning problems that take safety into account. In this study, we propose a reinforcement learning algorithm that ensures no constraint violations occur in each episode. Most existing methods guarantee constraint satisfaction only on average over many episodes, meaning that violations may still occur in individual episodes. Such methods are unsafe and impractical for real-world applications.
Preprint	https://arxiv.org/abs/2502.10138

2) Self Iterative Label Refinement via Robust Unlabeled Learning

Authors	Hikaru Asano (OSX), Tadashi Kozuno (OSX), Yukino Baba (The University of Tokyo)
Research Introduction	In recent years, it has become increasingly common to use large language models (LLMs) themselves to generate training data for their own learning. However, when the LLM lacks sufficient knowledge in a particular domain, the generated data may contain inaccuracies, and training on such data can lead to degraded performance. To address this issue, we propose a novel approach that leverages unlabeled learning to mitigate the negative impact of inaccurate generated data.
Preprint	https://arxiv.org/abs/2502.12565

※Author information is current as of the date of writing or submission. Please be advised that the information may become outdated after that point.

About OMRON SINIC X Corporation

OMRON SINIC X Corporation is a strategic subsidiary seeking to realize the "near-future design" that OMRON forecasts. It is comprised of researchers with cutting-edge knowledge and experience across a wide range of technological domains, including AI, Robotics, IoT, and Sensing. With the aim of addressing social issues, OSX integrates innovative technologies with business models and strategies in technology and IP to create near-future design. Additionally, the company accelerates the creation of these designs through collaborative research with universities and external research institutions.

◆Website: https://www.omron.com/sinicx/en/
◆Activities: https://www.omron.com/sinicx/en/activity/
For any inquiries about OSX, please contact us here.

OMRON SINIC X Presents Latest Research Findings at NeurIPS 2025, Top-Tier Conference on AI and Machine Learning