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2026-023 - Framework for Maximizing Storage Efficiency in Neural Networks

Market Opportunity In neural networks—especially in binary, sparse, or hardware-constrained settings—there's a limit to how many distinct patterns can be reliably stored and recalled. Traditionally, this capacity has been poorly understood or achieved only through random methods that often fall short of optimal use. Training and maintaining...