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      "statement": "# YOLOv4/v7/v9: detector integration and auxiliary training pathways\n\nStudied component/augmentation combinations for efficient detection, then developed aggregation-aware scaling and reparameterization, lead-guided auxiliary labels, and programmable-gradient auxiliary branches with generalized layer aggregation.\n\n## Scientific scope\n\nPapers have different author sets and are not three versions of one single-authored manuscript. Training-only additions can raise training cost even when removed at inference. Original information-bottleneck motivation is not an independently established theorem that all task information is preserved. Inherited components retain credit.\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\nAlexey Bochkovskiy; Chien-Yao Wang; Hong-Yuan Mark Liao; I-Hau Yeh\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- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://arxiv.org/abs/2004.10934v1)\n- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696v1)\n- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616v1)\n",
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