Linear-time sequence modeling that replaces attention's O(n²d) complexity with O(nd) summation-based aggregation. Demonstrates constraint-driven emergence: how functional representations can develop from optimization pressure and architectural constraints alone, without explicit pairwise interactions.
intelligence deep-learning embeddings transformer forecasting attention cybernetics representation representation-theory attention-mechanism self-organization learning-theory attention-is-all-you-need emergence multimodal linear-time times-series llm system-theory sequence-modelling
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Updated
Nov 3, 2025 - Python