RAG-Stack: Co-Optimizing RAG Serving Performance and Quality | Paper Citation Record

Observation 3ea6001d-7144-43f2-9487-e470c4b6501b · outbound

This paper cites Algorithmicsuperintelligence/Openevolve.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Algorithmicsuperintelligence/Openevolve

Reference 1

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Observation 08540976-e172-40ba-8445-44448188cfe3 · outbound

This paper cites FlagOpen/FlagEmbedding.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality FlagOpen/FlagEmbedding

Reference 2

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Observation 225d76cb-d726-470d-b071-8af8d9921adc · outbound

This paper cites Marker-Inc-Korea/AutoRAG.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Marker-Inc-Korea/AutoRAG

Reference 3

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Observation 06b21274-cc6f-4ce5-8ea1-076c22dd26aa · outbound

This paper cites Unexpected Improvements to Expected Improvement for Bayesian Optimization.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unexpected Improvements to Expected Improvement for Bayesian Optimization

Reference 4

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

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Observation 41d5a24c-cad6-44e8-9f70-02aa21388be6 · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 6

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 7

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Observation 43aacc9d-a03f-405d-b666-149b44fb79e9 · outbound

This paper cites Qwen Technical Report.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Qwen Technical Report

Reference 8

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Observation a1c9236c-1d16-4ad7-9fbf-be6123b08e87 · outbound

This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Reference 9

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 10

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Observation c4b1a9e1-72b9-4d48-981d-c4f5cd7b2c87 · outbound

This paper cites Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models

Reference 11

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Observation 3493f766-dd6d-47ba-b394-5ea19fac79a9 · outbound

This paper cites Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems

Reference 12

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 13

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 14

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 15

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 16

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This paper cites Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement

Reference 17

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 18

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This paper cites The Faiss library.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality The Faiss library

Reference 19

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 20

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Observation 2b872cc6-33d0-414e-b816-26dcf358ecb7 · outbound

This paper cites ELI5: Long Form Question Answering.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality ELI5: Long Form Question Answering

Reference 21

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Observation 8fca0e04-26b6-42b3-b84d-de1dab123170 · outbound

This paper cites AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation

Reference 22

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Observation 55d7d336-966e-4184-9551-b696d204cee0 · outbound

This paper cites Precise Zero-Shot Dense Retrieval without Relevance Labels.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Precise Zero-Shot Dense Retrieval without Relevance Labels

Reference 23

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Observation 944f20cd-f4ca-4849-ae05-43b1434a2db1 · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 24

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Observation a4820a3a-363a-46f7-a626-b06809a0b99b · outbound

This paper cites The CMA Evolution Strategy: A Tutorial.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality The CMA Evolution Strategy: A Tutorial

Reference 25

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Observation 736d81f2-aaf5-4acc-83d5-acc22127cca2 · outbound

This paper cites PipeDream: Fast and Efficient Pipeline Parallel DNN Training.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality PipeDream: Fast and Efficient Pipeline Parallel DNN Training

Reference 26

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This paper cites GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Reference 27

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 28

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 29

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This paper cites RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Serving.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Serving

Reference 30

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This paper cites Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models

Reference 31

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Reference 32

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 33

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 34

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Reference 35

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

Reference 36

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Reference 37

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 38

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 39

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Efficient Memory Management for Large Language Model Serving with PagedAttention

Reference 40

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 41

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Reference 42

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 43

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 44

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 45

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality In 2017 International Joint Conference on Neural Networks (IJCNN)

Reference 46

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Observation c6ddf4b6-bcf8-49de-a39d-2f9260413408 · outbound

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Reference 48

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Large Language Models to Enhance Bayesian Optimization

Reference 49

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Reference 50

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Observation 4dcd3747-1cb6-4631-b1c1-7b829b28cca0 · outbound

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs

Reference 51

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality AlphaEvolve: A coding agent for scientific and algorithmic discovery

Reference 52

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Unresolved cited work

Reference 53

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Observation 6e7c7898-2dbd-4775-b864-661d5a381d14 · outbound

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Vicky Zhao, Lili Qiu, and Dongmei Zhang

Reference 54

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations

Reference 55

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Observation 1a9e9527-3ee9-4486-9570-9a2b3547b899 · outbound

This paper cites Long$^2$RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Long$^2$RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:13:19.741970Z

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Observation cab65b78-31b0-45eb-a2ad-f73a560edb7a · outbound

This paper cites Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs

Reference 57

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Observation 7c4fd55a-c9c3-427a-b3b5-c72054db4f79 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 58

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Observation 7355d19a-3526-4562-8e2f-ee1f91497717 · outbound

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 59

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Reference 64

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Observation 59a92239-d6c4-4251-b986-2f1193258918 · outbound

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Reference 78

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Observation d772df4c-2271-4ab9-9662-c02eacb96ad9 · outbound

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality Multi-fidelity Bayesian Optimisation with Continuous Approximations

Reference 2017

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RAG-Stack: Co-Optimizing RAG Serving Performance and Quality InFindings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.)

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Reference 2025

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