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[2602.16192] Revolutionizing Long-Term Memory in AI: New Horizons with High-Capacity and High-Speed Storage
Summary
This article discusses innovative approaches to long-term memory in AI, emphasizing the importance of retaining raw experiences for better task adaptability and knowledge retention.
Why It Matters
As AI systems evolve, enhancing their memory capabilities is crucial for achieving artificial superintelligence. This paper explores alternative memory strategies that could lead to more efficient learning and application of knowledge, addressing the limitations of current paradigms.
Key Takeaways
- Current AI memory strategies risk losing valuable information during extraction.
- The 'store then on-demand extract' approach may enhance adaptability and knowledge retention.
- Exploring deeper insights from probabilistic experiences can lead to improved AI performance.
- Sharing stored experiences can increase the efficiency of experience collection.
- Identifying and overcoming challenges in memory research is essential for future advancements.
Computer Science > Artificial Intelligence arXiv:2602.16192 (cs) [Submitted on 18 Feb 2026] Title:Revolutionizing Long-Term Memory in AI: New Horizons with High-Capacity and High-Speed Storage Authors:Hiroaki Yamanaka, Daisuke Miyashita, Takashi Toi, Asuka Maki, Taiga Ikeda, Jun Deguchi View a PDF of the paper titled Revolutionizing Long-Term Memory in AI: New Horizons with High-Capacity and High-Speed Storage, by Hiroaki Yamanaka and 5 other authors 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 proba...
