most citedHarmonia: End-to-End RAG Serving Optimization

1 citations · 1 across the 4 of their papers we have counts for

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cs.DC2026

Sangam: Efficiently Serving Diffusion LLMs with the AR Stack

Nitin Kedia, Saurabh Agarwal, Myungjin Lee +1

Diffusion language models (dLLMs) generate text by iteratively denoising a masked response and can commit multiple output positions per model invocation. Their bidirectional attent…

cs.DC2025★ 1 cited

Harmonia: End-to-End RAG Serving Optimization

Saurabh Agarwal, Bodun Hu, Luis Pabon +3

Retrieval-Augmented Generation (RAG) improves the reliability of large language models by integrating external knowledge, but serving RAG pipelines efficiently is challenging becau…