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Fast LapSum enables exact differentiable top-k at million scale

Researchers have developed Fast LapSum, a novel method for efficiently computing the top-k elements in large-scale computations. This technique preserves the exact selection mass of k while remaining fully differentiable, addressing limitations of previous hard top-k operations and costly continuous relaxations. Fast LapSum's GPU solver operates in linear time, processing millions of scores in milliseconds, making it practical for applications like sparse routing, retrieval, and large-scale optimization. AI

IMPACT Enables more efficient sparse computations, potentially accelerating training and inference for large-scale AI models.

RANK_REASON The item is an academic paper detailing a new computational method. [lever_c_demoted from research: ic=1 ai=1.0]

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Fast LapSum enables exact differentiable top-k at million scale

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · {\L}ukasz Struski, Joanna Wojciechowicz, Jakub Antczak, Marcin Mazur, Kamil Ksi\k{a}\.zek, Jacek Tabor ·

    Fast LapSum: Exact Differentiable Top-k at Million Scale

    arXiv:2608.06912v1 Announce Type: new Abstract: The top-$k$ operation is a fundamental building block of modern sparse computation, enabling token routing, expert activation, memory selection, and attention pruning. Yet standard hard top-$k$ blocks gradients, while existing conti…