• Computer Science > Artificial Intelligence [Submitted on 18 Feb 2026] Title:Revolutionizing Long-Term Memory in AI: New Horizons with High-Capacity and High-Speed Storage View PDF HTML (experimental)Abstract:Driven by our mission of “uplifting the world with memory,” this paper explores the design concept of “memory” that is essential for achieving artificial superintelligence (ASI). • Rather than proposing novel methods, we focus on several alternative approaches whose potential benefits are widely imaginable, yet have remained largely unexplored. • The currently dominant paradigm, which can be termed “extract then store,” involves extracting information judged to be useful from experiences and saving only the extracted content. • However, this approach inherently risks the loss of information, as some valuable knowledge particularly for different tasks may be discarded in the extraction process. • In contrast, we emphasize the “store then on-demand extract” approach, which seeks to retain raw experiences and flexibly apply them to various tasks as needed, thus avoiding such information loss. • In addition, we highlight two further approaches: discovering deeper insights from large collections of probabilistic experiences, and improving experience collection efficiency by sharing stored experiences.
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- Computer Science > Artificial Intelligence [Submitted on 18 Feb 2026] Title:Revolutionizing Long-Term Memory in AI: New Horizons with High-Capacity and High-Speed Storage View PDF HTML (experimental)Abstract:Driven by our mission of “uplifting the world with memory,” this paper explores the design concept of “memory” that is essential for achieving artificial superintelligence (ASI). Rather than proposing novel methods, we focus on several alternative approaches whose potential benefits are widely imaginable, yet have remained largely unexplored. The currently dominant paradigm, which can be t
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