DS SERVE: A Framework for Efficient and Scalable Neural Retrieval
#DS-Serve #Neural Retrieval #Information Retrieval #Large-scale datasets #Latency optimization #Memory efficiency #Retrieval-augmented generation #API endpoints
📌 Key Takeaways
- DS-Serve transforms massive text datasets (half a trillion tokens) into efficient neural retrieval systems
- The framework achieves low latency with modest memory requirements on a single node
- It supports flexible trade-offs between latency, accuracy, and result diversity
- DS-Serve offers both web interface and API endpoints for accessibility
- The framework has broad applications including retrieval-augmented generation and search agents
📖 Full Retelling
Researchers led by Jinjian Liu and including Yichuan Wang, Xinxi Lyu, Rulin Shao, Joseph E. Gonzalez, Matei Zaharia, and Sewon Min introduced DS-Serve, a groundbreaking framework for efficient neural retrieval, in a paper submitted to arXiv on December 17, 2025. The framework transforms massive text datasets containing half a trillion tokens into high-performance neural retrieval systems, offering both web interface and API endpoints with low latency and modest memory requirements on a single node. DS-Serve represents a significant advancement in information retrieval technology, particularly for handling extremely large-scale text datasets. The framework's key innovation lies in its ability to balance performance with resource efficiency, achieving low latency while maintaining modest memory overhead on standard hardware. This makes neural retrieval technology more accessible to organizations without massive computational resources. The framework also supports flexible inference-time trade-offs between latency, accuracy, and result diversity, allowing users to optimize performance based on specific application needs. The researchers anticipate that DS-Serve will have broad applications across multiple domains, including large-scale retrieval-augmented generation systems, training data attribution mechanisms, and advanced search agent development.
🏷️ Themes
Information Retrieval, Artificial Intelligence, Scalability
📚 Related People & Topics
Information retrieval
Finding information for an information need
Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be base...
Entity Intersection Graph
Connections for Information retrieval:
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Large language model
3 shared
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Artificial intelligence
2 shared
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Recommender system
2 shared
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Efficiency
1 shared
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Transparency
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Original Source
--> Computer Science > Information Retrieval arXiv:2602.22224 [Submitted on 17 Dec 2025] Title: DS SERVE: A Framework for Efficient and Scalable Neural Retrieval Authors: Jinjian Liu , Yichuan Wang , Xinxi Lyu , Rulin Shao , Joseph E. Gonzalez , Matei Zaharia , Sewon Min View a PDF of the paper titled DS SERVE: A Framework for Efficient and Scalable Neural Retrieval, by Jinjian Liu and 6 other authors View PDF HTML Abstract: We present DS-Serve, a framework that transforms large-scale text datasets, comprising half a trillion tokens, into a high-performance neural retrieval system. DS-Serve offers both a web interface and API endpoints, achieving low latency with modest memory overhead on a single node. The framework also supports inference-time trade-offs between latency, accuracy, and result diversity. We anticipate that DS-Serve will be broadly useful for a range of applications, including large-scale retrieval-augmented generation , training data attribution, training search agents, and beyond. Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2602.22224 [cs.IR] (or arXiv:2602.22224v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2602.22224 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yichuan Wang [ view email ] [v1] Wed, 17 Dec 2025 00:43:10 UTC (856 KB) Full-text links: Access Paper: View a PDF of the paper titled DS SERVE: A Framework for Efficient and Scalable Neural Retrieval, by Jinjian Liu and 6 other authors View PDF HTML TeX Source view license Current browse context: cs.IR < prev | next > new | recent | 2026-02 Change to browse by: cs cs.AI cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Con...
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