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Introduces SHD-CCP v2.0, a novel AI architecture that replaces transformer token sequences with 3D point cloud data structures using Grassmannian manifold fusion and zero-copy memory-mapped streaming, achieving low latency and memory footprint on consumer hardware.
This paper presents a retrospective on the design evolution of SWave, a complex-valued recurrent language model, detailing which architectural components were retained, reframed, superseded, or proved non-load-bearing, along with formal characterizations of failure modes like cos-domination collapse.
Google researchers introduce Nested Learning, a new architecture that replaces the Transformer by treating models as nested optimization problems, solving catastrophic forgetting and achieving 100% long-context memory stability.