@inproceedings{zhou-etal-2026-awm,
title = "{AWM}: Answerable Working Memory for Long-Document {VQA} Agents",
author = "Zhou, Dongzhuoran and
Zhu, Yuqicheng and
Liu, Yule and
Yang, Zhen and
Lu, Rui and
Dong, Yuxiao and
Tang, Jie and
Kharlamov, Evgeny",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1915/",
pages = "40824--40839",
ISBN = "979-8-89176-462-0",
abstract = "Long-document visual question answering increasingly relies on VLM agents that retrieve candidate pages, inspect page images, write findings to working memory, and synthesize answers. Working memory should carry answer-supporting evidence across page inspections for later grounded answering, yet existing evaluation mainly checks final-answer correctness and evidence-page access. This creates a memory-quality blind spot: an agent may reach the right page and answer correctly while leaving behind memory too generic or incomplete to support answering once page context is removed. We introduce \textit{memory-only answerability}, a diagnostic that asks whether a reader can answer from the question and terminal working memory alone. Building on this diagnostic, \textit{Answerable Working Memory} (AWM) treats terminal working memory as an answerable evidence artifact, and AWM-GRPO incorporates this signal into the GRPO reward while preserving final-answer priority. Under GRPO, this reward assigns higher advantages to answer-correct trajectories whose terminal working memory remains answerable. On \textsc{MMLongBench-Doc}, even when gold evidence pages are provided, 42.5{\%} of correct answers still cannot be answered from terminal working memory alone. AWM-GRPO improves final-answer accuracy over the RAG baseline by 8.1 and 11.9 points on \textsc{MMLongBench-Doc} and \textsc{LongDocURL} and reduces the memory-missing-correct rate by 2.7 points over answer-only GRPO."
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<abstract>Long-document visual question answering increasingly relies on VLM agents that retrieve candidate pages, inspect page images, write findings to working memory, and synthesize answers. Working memory should carry answer-supporting evidence across page inspections for later grounded answering, yet existing evaluation mainly checks final-answer correctness and evidence-page access. This creates a memory-quality blind spot: an agent may reach the right page and answer correctly while leaving behind memory too generic or incomplete to support answering once page context is removed. We introduce memory-only answerability, a diagnostic that asks whether a reader can answer from the question and terminal working memory alone. Building on this diagnostic, Answerable Working Memory (AWM) treats terminal working memory as an answerable evidence artifact, and AWM-GRPO incorporates this signal into the GRPO reward while preserving final-answer priority. Under GRPO, this reward assigns higher advantages to answer-correct trajectories whose terminal working memory remains answerable. On MMLongBench-Doc, even when gold evidence pages are provided, 42.5% of correct answers still cannot be answered from terminal working memory alone. AWM-GRPO improves final-answer accuracy over the RAG baseline by 8.1 and 11.9 points on MMLongBench-Doc and LongDocURL and reduces the memory-missing-correct rate by 2.7 points over answer-only GRPO.</abstract>
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%0 Conference Proceedings
%T AWM: Answerable Working Memory for Long-Document VQA Agents
%A Zhou, Dongzhuoran
%A Zhu, Yuqicheng
%A Liu, Yule
%A Yang, Zhen
%A Lu, Rui
%A Dong, Yuxiao
%A Tang, Jie
%A Kharlamov, Evgeny
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhou-etal-2026-awm
%X Long-document visual question answering increasingly relies on VLM agents that retrieve candidate pages, inspect page images, write findings to working memory, and synthesize answers. Working memory should carry answer-supporting evidence across page inspections for later grounded answering, yet existing evaluation mainly checks final-answer correctness and evidence-page access. This creates a memory-quality blind spot: an agent may reach the right page and answer correctly while leaving behind memory too generic or incomplete to support answering once page context is removed. We introduce memory-only answerability, a diagnostic that asks whether a reader can answer from the question and terminal working memory alone. Building on this diagnostic, Answerable Working Memory (AWM) treats terminal working memory as an answerable evidence artifact, and AWM-GRPO incorporates this signal into the GRPO reward while preserving final-answer priority. Under GRPO, this reward assigns higher advantages to answer-correct trajectories whose terminal working memory remains answerable. On MMLongBench-Doc, even when gold evidence pages are provided, 42.5% of correct answers still cannot be answered from terminal working memory alone. AWM-GRPO improves final-answer accuracy over the RAG baseline by 8.1 and 11.9 points on MMLongBench-Doc and LongDocURL and reduces the memory-missing-correct rate by 2.7 points over answer-only GRPO.
%U https://aclanthology.org/2026.findings-emnlp.1915/
%P 40824-40839
Markdown (Informal)
[AWM: Answerable Working Memory for Long-Document VQA Agents](https://aclanthology.org/2026.findings-emnlp.1915/) (Zhou et al., Findings 2026)
ACL
- Dongzhuoran Zhou, Yuqicheng Zhu, Yule Liu, Zhen Yang, Rui Lu, Yuxiao Dong, Jie Tang, and Evgeny Kharlamov. 2026. AWM: Answerable Working Memory for Long-Document VQA Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 40824–40839, Budapest, Hungary. Association for Computational Linguistics.