Hello, this is Koji Maruhashi from the Artificial Intelligence Laboratory. This article is the third in the GraphAI series. In the first post, we introduced methods for learning large-scale graphs with billions of nodes. In the second post, we explained how to make inherently black-box GraphAI explainable and interpretable. In this article, we will introduce our efforts on a further challenge: "uncovering the underlying phenomena of large-scale graphs."
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Hello, we are Jun Takahashi and Takuto Sato from the Artificial Intelligence Laboratory at Fujitsu. We participated in the international Agentic AI competition, the AgentX AgentBeats Competition, and won 2nd place. This post introduces our work for the competition.
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Hello, we are Vempalli Saketh, Siddartha Reddy Thummaluru, Harsh Pandey and Mahesh Chandran from the Artificial Intelligence Research Laboratory at Fujitsu Research of India (FRIPL). We are excited to share our latest research focused on making Graph AI more transparent, trustworthy, and actionable.
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Hello. We are Mohit Meena, Yash Punjabi, and Mahesh Chandran from the Artificial Intelligence Research Laboratory at Fujitsu Research of India (FRIPL). We are excited to share our latest research focused on addressing scalability challenges in large-scale Graph AI.
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Hello everyone. We are Yuya Edazawa, Yuma Muto, and Takuya Okamoto from Fujitsu’s Advanced Technology Development Division. We are working on the development of FUJITSU-MONAKA[^1] series, including the next-generation Arm processor FUJITSU-MONAKA, which will be the foundation of the future of cutting-edge domains such as AI, HPC, and cloud. In addition, in June 2025 we launched development of FugakuNEXT, the successor system to Fugaku. This project is currently being carried out jointly with RIKEN and NVIDIA.
To share these initiatives and their latest progress with the global community, we participated on-site in SC25, the International Conference for High Performance Computing, Networking, Storage and Analysis (hereafter, SC25 | https://sc25.supercomputing.org/), held in St. Louis, USA from November 16 to 21, 2025.
In this article, we report on the exhibit content related to FUJITSU-MONAKA and FugakuNEXT, as well as the latest trends we observed through attending technical sessions and visiting other exhibitors’ booths.
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Hello, this is Hiraga, Kinoshita, and Ohtsuji from the Computing Laboratory at Fujitsu Research.
This article is the third installment of our four-part series reporting on SC25, the international conference held in St. Louis, USA in November 2025. We will introduce what the Computing Laboratory presented at the Fujitsu booth, along with trends in supercomputer performance rankings (Top500, Green500, IO500), and highlights from other events at the conference.
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Namaskara! We are members of the FUJITSU-MONAKA Software R&D team at Fujitsu Research of India Pvt Ltd (FRIPL). Our unit is dedicated to advancing and optimizing the High-Performance Computing (HPC) and Artificial Intelligence (AI) software ecosystem for Arm CPUs. A significant part of our focus is geared towards maximizing performance for FUJITSU-MONAKA, a collaborative effort with our esteemed colleagues at Fujitsu Limited Japan. Our expertise spans a comprehensive range of software verticals, including databases, machine learning frameworks, deep learning and Generative AI frameworks, as well as confidential computing.
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Hello, I'm Takita from the Quantum Research Institute. Since joining the company, I have engaged in research and development across various fields, starting with optical communication systems and networks, then moving on to general networking, edge computing, and optimization (quantum-inspired optimization). Starting in fiscal year 2024, I became involved with quantum computing and am currently working on creating applications for it.
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Hello. We are Fujii and Yanashima from the Artificial Intelligence Research Laboratory.
In this article, we will introduce Fujitsu's Causal AI knowledge-guided causal discovery technology and its application examples.
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Please note that this blog post has been translated using machine translation.
Hello, I'm Ichiba from Fujitsu Research, Computing Laboratory. I participated in the international conference SC25, held in St. Louis, USA, from November 16 to 21, 2025. In this article, I will share my report on the conference. Various departments within Fujitsu plan to publish a four-part series of reports on SC25. This article will primarily focus on the paper presentations that I found particularly noteworthy. At SC, various organizations, including companies, universities, and research institutions, hold exhibitions, and Fujitsu is also one of the exhibitors. Details about Fujitsu's exhibition will be covered in separate articles.
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[machine translation]
Hello everyone. I'm Kimura from the Quantum Applications CPJ team at the Quantum Research Institute. I recently presented our latest research findings at FTU 2025, held at Fujitsu Technology Park on December 2, 2025. To share this presentation with a wider audience, I am now providing a detailed introduction on the Fujitsu Tech Blog.
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Hello, this is Sakai from Artificial Intelligence Laboratory of Fujitsu Research. In this blog, I’ll introduce 1-bit quantization in an easy-to-understand way. The background of this technology lies in the growing size of generative AI models and the accompanying challenges in computational resources. Our AI research team has developed a groundbreaking solution—1-bit quantization—and even released it as open-source software (OSS). This article explains the background and the technology in simple terms.
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Hello, we are Uno, Miyake, and Inderjeet from the Data & Security Research Laboratory.
In recent years, the development of AI technology has been remarkable. A future where AI agents from multiple companies and organizations collaborate and co-create to solve more complex and diverse challenges is becoming increasingly realistic. As knowledge essential for improving AI performance approaches its limits based solely on web-based information, AI agents trained on knowledge accumulated by enterprises are expected to solve industry-wide challenges, such as those in supply chains, and create new innovations through cross-organizationa collaboration.
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Hello. We're Suzuki and Kurume from the Converging Technologies Laboratory. Our research group is developing "Policy Twin" technology that dramatically advances policy-making by local governments and nations.
Have you ever heard the keyword "digital twin"? A digital twin is a general term for technologies that construct and simulate a twin of the real world in a virtual space, and it is utilized in various fields such as manufacturing and smart cities. We went further and developed "Social Digital Twin" [^1],[^2] technologies to reproduce people and society in the digital world and solve social issues. And to further deepen these efforts, we are taking a further step by attempting to digitalize "Policy" itself, which forms the foundation of social design. This is the "Policy Twin" technology we are developing.
This time, we would like to introduce the full scope of this innovative Policy Twin technology and its application in actual municipalities. Aren't you excited that our real society will also evolve into a better one by freely testing policies in a digital space, creating such possibilities for new social design? Please look forward to it!
(NOTE: This article was generated using machine translation.)
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