17 citations · 51 across the 15 of their papers we have counts for
15 papers
Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences
Stas Bekman, Samyam Rajbhandari, Michael Wyatt +5
Long sequences are critical for applications like RAG, long document summarization, multi-modality, etc., and modern LLMs, like Llama 4 Scout, support max sequence length of up to…
MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility
Yexiao He, Ang Li, Boyi Liu +2
Healthcare decision-making represents one of the most challenging domains for Artificial Intelligence (AI), requiring the integration of diverse knowledge sources, complex reasonin…
ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering
Ruofan Wu, Youngwon Lee, Fan Shu +5
Retrieval-Augmented Generation (RAG) systems are increasingly diverse, yet many suffer from monolithic designs that tightly couple core functions like query reformulation, retrieva…
CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation
Youngwon Lee, Seung-won Hwang, Daniel Campos +3
With the adoption of retrieval-augmented generation (RAG), large language models (LLMs) are expected to ground their generation to the retrieved contexts. Yet, this is hindered by…
Inference Scaling for Bridging Retrieval and Augmented Generation
Youngwon Lee, Seung-won Hwang, Daniel Campos +3
Retrieval-augmented generation (RAG) has emerged as a popular approach to steering the output of a large language model (LLM) by incorporating retrieved contexts as inputs. However…
AI and Memory Wall
Amir Gholami, Zhewei Yao, Sehoon Kim +3
The availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serv…