arXiv — NLP / Computation & Language · · 3 min read

AWM: Answerable Working Memory for Long-Document VQA Agents

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Computer Science > Computation and Language

arXiv:2608.25618 (cs)
[Submitted on 26 Aug 2026]

Title:AWM: Answerable Working Memory for Long-Document VQA Agents

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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 \emph{memory-only answerability}, a diagnostic that asks whether a reader can answer from the question and terminal working memory alone. Building on this diagnostic, \emph{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.
Comments: EMNLP 2026 Findings. 16 pages, 4 figures, 9 tables
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2608.25618 [cs.CL]
  (or arXiv:2608.25618v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.25618
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Dongzhuoran Zhou [view email]
[v1] Wed, 26 Aug 2026 10:35:25 UTC (317 KB)
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