---
title: "One Retrieval to Cover Them All — paper by Ratnakar et al. (2026)"
description: "The authors frame this as a session problem and propose two metrics: the share of a session's documents returned by a… 14 references."
authors: ["Shivam Ratnakar", "Yixuan Zhu", "Cecilia Cheng", "Chaya Vijayakumar"]
year: 2026
venue: "Proceedings of the 4th Workshop on Towards Knowledgeable Foundation Models (KnowFM 2026)"
source: "https://aclanthology.org/2026.knowfm-1.14/"
doi: "10.18653/v1/2026.knowfm-1.14"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ratnakar-2026-one-retrieval-cover-them-all/"
---
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# One Retrieval to Cover Them All: Co-occurrence-Aware Knowledge Base Reorganization for Session-Level RAG

**Authors:** [Shivam Ratnakar](https://md.research.elamur.ai/free/en/authors/shivam-ratnakar/), Yixuan Zhu, Cecilia Cheng and Chaya Vijayakumar · **Year:** 2026 · **Venue:** [Proceedings of the 4th Workshop on Towards Knowledgeable Foundation Models (KnowFM 2026)](https://md.research.elamur.ai/free/en/venues/knowfm/) ([Workshop on Towards Knowledgeable Foundation Models 2026](https://md.research.elamur.ai/free/en/venues/knowfm/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.knowfm-1.14/) · [PDF](https://aclanthology.org/2026.knowfm-1.14.pdf) · [DOI 10.18653/v1/2026.knowfm-1.14](https://doi.org/10.18653/v1/2026.knowfm-1.14) · [OpenAlex](https://openalex.org/W7166823779)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Retrieval-augmented generation is usually tuned to answer one question at a time. Enterprise support users, however, tend to arrive with a whole episode of connected needs, and the documents relevant to that episode often sit far apart in embedding space. The authors frame this as a session problem and propose two metrics: the share of a session's documents returned by a single retrieval call, and the number of calls needed to reach a target coverage τ.

Their method reorganizes the knowledge base offline. Document IDs are treated as tokens, and sequences of documents needed together are used to train a Word2Vec CBOW model. These sequences come from expert-labeled article groups, random walks with jumps over a similarity graph, and synthetic QA trajectories. The resulting vectors are then grouped with agglomerative clustering. At query time, a few top hits are expanded with the other members of their clusters, and the combined pool is re-ranked.

On WixQA, a single call now covers 58% of a session instead of 41%. Fewer follow-up calls are needed, and the effective KB becomes much smaller. The authors argue that enterprise RAG should be judged by session coverage, not single-query recall.

## Key points
- Evaluated on WixQA (6,221 enterprise support articles). Session coverage at k=8 rises from 41% to 58%, a gain of +17 points (95% CI: [14.1, 20.4]). Gains of +17 to 21 points hold across four embedding models and in all six functional domains.
- Retrieval calls needed to reach 70% coverage drop by 34%. Clustering into ⌈N/5⌉ groups shrinks the effective KB to about 20% of its original size.
- Co-occurrence embeddings are 100-dimensional, trained with window 2 and 10 negative samples. Ground-truth groups are upsampled 10× and all sequences are augmented 3×. Online, the method takes the top-3 vanilla hits, adds roughly 12 cluster candidates, and re-ranks to a final top-8.
- Trade-off: Hits@8 falls slightly, from 96.0% to 93.0%. On HotpotQA, where documents are not reused across queries, there is no benefit (−1.4%).
- Limitations:
  - Co-occurrence signals are largely synthetic, since no real navigation logs were used.
  - Sessions are derived from the clusters themselves.
  - The quality of downstream generated answers is not measured.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Shivam Ratnakar, Yixuan Zhu, Cecilia Cheng and Chaya Vijayakumar. 2026. One Retrieval to Cover Them All: Co-occurrence-Aware Knowledge Base Reorganization for Session-Level RAG. Proceedings of the 4th Workshop on Towards Knowledgeable Foundation Models (KnowFM 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ratnakar2026one,
  title = {{One Retrieval to Cover Them All: Co-occurrence-Aware Knowledge Base Reorganization for Session-Level RAG}},
  author = {Shivam Ratnakar, Yixuan Zhu, Cecilia Cheng and Chaya Vijayakumar},
  booktitle = {Proceedings of the 4th Workshop on Towards Knowledgeable Foundation Models (KnowFM 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.knowfm-1.14},
  url = {https://doi.org/10.18653/v1/2026.knowfm-1.14}
}
```

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## References

14 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*