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This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2610.28958", "title": "Transformers as Generic Deep Neural Networks: Exact Compilation, the Cost of Removing Activation-Activation Products, and Multi-Agent Attention Clustering", "summary": "The method replaces Transformer-specific NPU operators and Transformer-specific quantization pipelines with an exact compilation of attention, LayerNorm, and GELU MLP into generic affine, routing, square, sigmoid, and inverse-square-root primitives, plus an optional top-k attention scheme where centroid clustering reduces computed scores from N^2 to Nk per head. The cost is implementation complexity: exact compilation requires an elementwise activation-activation product to avoid an N^2 blowup in scalar nonlinear evaluations, and the top-k gains depend on centroid-based key preselection and tail-mass estimation rather than standard dense attention. 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