---
title: "MAB-DQA — paper by Xiang et al. (2026) — summary and ideas"
description: "The authors show the flaw this creates. 42 references. Among them: 0."
authors: ["Yixin Xiang", "Yunshan Ma", "Xiaoyu Du", "Yibing Chen", "Yanxin Zhang", "Jinhui Tang"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1053/"
doi: "10.18653/v1/2026.acl-long.1053"
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/xiang-2026-mab-dqa-addressing-query-aspect/"
---
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# MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits

**Authors:** Yixin Xiang, Yunshan Ma, Xiaoyu Du, Yibing Chen, Yanxin Zhang and Jinhui Tang · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/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.acl-long.1053/) · [PDF](https://aclanthology.org/2026.acl-long.1053.pdf) · [DOI 10.18653/v1/2026.acl-long.1053](https://doi.org/10.18653/v1/2026.acl-long.1053) · [OpenAlex](https://openalex.org/W7166873441)

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

## Summary
Multimodal retrieval-augmented pipelines for Document Question Answering (DQA), such as Colpali and MoloRAG, rank page images by late-interaction scoring. This scoring sums each query token's best patch match and treats every token as equally important. The authors show the flaw this creates. Pages full of frequent but uninformative terms, such as a company name repeated throughout a financial report, can outscore the page that holds the real evidence. The problem is worse when only a few top pages are kept.

MAB-DQA addresses this in several steps:
- A VLM breaks the query into aspect-level subqueries and keeps the full query alongside them.
- Pages are organised into a query-aware hypergraph.
- Each subquery is treated as an arm of a multi-armed bandit.
- Quick VLM judgments on a few representative pages act as rewards, and Thompson Sampling shifts retrieval effort toward the aspects that prove useful.
- A reflective reasoning agent produces the final answer.

On four benchmarks, the system beats the strongest prior method by 10.38% in average answer accuracy. It also sets new retrieval results on MMLongBench and LongDocURL. This matters for long, layout-heavy documents such as reports and manuals.

## Key points
- The authors call the core failure "aspect retrieval degradation." In a Best Buy example from MMLongBench, an irrelevant page scored 1.449 under Colpali, while the true evidence page scored only 0.892.
- The pipeline has three parts: a page graph combined with subquery hyperedges, bandit-guided page selection over that hypergraph, and an answer agent that reflects on both the question and its answer.
- The bandit uses Thompson Sampling with a Beta-Bernoulli model of relevant/irrelevant VLM feedback. UCB and Epsilon-Greedy were not compared.
- Reported gains:
  - 5–18% average improvement over the state of the art in the abstract.
  - 10.38% average answer-accuracy gain in the main text.
  - Best retrieval at Top-K = 1, 3 and 5 on MMLongBench and LongDocURL.
- The experiments use documents of 40–500 pages. Results depend on VLM quality and on hyperparameters (α, β, λ) tuned by grid search. Code is published at github.com/ElephantOH/MAB-DQA.

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

## Institutions

- [University of Wisconsin–Madison](https://md.research.elamur.ai/free/en/universities/university-of-wisconsin-madison/) — University, Madison, United States · 54 works · 49,089 citations
- [Singapore Management University](https://md.research.elamur.ai/free/en/universities/singapore-management-university/) — University, Singapore, Singapore · 35 works · 2,457 citations
- [Nanjing University of Science and Technology](https://md.research.elamur.ai/free/en/universities/nanjing-university-of-science-and-technology/) — University, Nanjing, China · 19 works · 1,590 citations

Also on the byline: [Nanjing Forestry University](https://ror.org/03m96p165).

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## Cite

Cite as: Yixin Xiang, Yunshan Ma, Xiaoyu Du, Yibing Chen, Yanxin Zhang and Jinhui Tang. 2026. MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{xiang2026mab,
  title = {{MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits}},
  author = {Yixin Xiang, Yunshan Ma, Xiaoyu Du, Yibing Chen, Yanxin Zhang and Jinhui Tang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1053},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1053}
}
```

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*Updated 2 November 2026*