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      "statement": "# Hou et al.: instruction-conditioned structured parameter pruning\n\nJointly trained a prompt-conditioned sparsity predictor and language model to choose structured feed-forward parameter masks, keeping selected parameters fixed for an input or task to reduce repeated weight loading.\n\n## Scientific scope\n\nStructured pruning, conditional computation and differentiable top-k have predecessors. Fixed masks trade adaptation granularity for loading efficiency. Original2025 method is distinct from its later flash-resident AFM3 product-system integration; benchmarks do not guarantee equal quality for every task.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nBairu Hou; Qibin Chen; Jianyu Wang; Guoli Yin; Chong Wang; Nan Du; Ruoming Pang; Shiyu Chang; Tao Lei\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Instruction-Following Pruning for Large Language Models](https://arxiv.org/abs/2501.02086v3)\n- [Hou et al.: instruction-conditioned structured parameter pruning — official institutional account](https://machinelearning.apple.com/research/pruning-large-language)\n",
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