🤖 AI Summary
Current AI systems commonly adopt a “extract-then-store” memory paradigm, which often discards task-irrelevant yet potentially useful information, thereby limiting the development of long-term memory capabilities. This work proposes a novel “store-then-extract-on-demand” paradigm that preserves all raw experiences in full, enabling flexible retrieval across multiple tasks. By incorporating mechanisms for aggregative analysis based on large-scale probabilistic experiences and cross-task experience sharing, the approach enhances memory utility without relying on new learning algorithms—instead, it reconfigures the memory system architecture itself. Preliminary experiments demonstrate significant improvements in adaptability, reuse efficiency, and knowledge discovery within the memory system, offering a promising pathway toward high-capacity, high-throughput long-term memory architectures for artificial intelligence.
📝 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. While these approaches seem intuitively effective, our simple experiments demonstrate that this is indeed the case. Finally, we discuss major challenges that have limited investigation into these promising directions and propose research topics to address them.