most citedByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs

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cs.IR2026

Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking

Jun Feng, Jiahui Tang, Zhicheng He +5

Adaptive Retrieval-Augmented Generation aims to mitigate the interference of extraneous noise by dynamically determining the necessity of retrieving supplementary passages. However…