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cs.CL · 2608.25618 · 2026-08-26

AWM: Answerable Working Memory for Long-Document VQA Agents

Dongzhuoran Zhou, Yuqicheng Zhu, Yule Liu, Zhen Yang et al.
Agent记忆工作记忆VQA
💬 agent可能翻对了页、答对了问题,但留下的工作记忆本身太笼统或不完整,脱离原始页面就无法支撑答案——即使给了黄金证据页,42.5%的正确答案其实无法只靠终态工作记忆回答;把这个"记忆可回答性"信号纳入GRPO奖励后准确率提升8-12个点。

🎯 背景

长文档视觉问答越来越依赖VLM agent检索候选页、查看页面图像、把发现写入工作记忆并综合出答案。工作记忆理应携带能支撑后续基于证据回答的信息,但现有评测主要只检查最终答案正确性和证据页访问情况,留下一个"记忆质量盲区":agent可能到达正确页面并答对,却留下过于笼统或不完整、脱离页面上下文后无法支撑回答的记忆。

🔬 方法

作者提出"仅凭记忆可回答性"这一诊断指标,检验读者能否仅凭问题和终态工作记忆作答。在此基础上,Answerable Working Memory(AWM)把终态工作记忆当作可回答的证据artifact,AWM-GRPO在保留最终答案优先级的同时把这一信号纳入GRPO奖励,为答案正确且终态工作记忆保持可回答的轨迹分配更高优势值。

📊 结果

在MMLongBench-Doc上,即使提供了黄金证据页,42.5%的正确答案仍无法仅凭终态工作记忆回答;AWM-GRPO相对RAG基线在MMLongBench-Doc和LongDocURL上分别提升8.1和11.9个百分点的最终答案准确率,并相对仅用答案信号的GRPO把"记忆缺失但答案正确"的比例降低2.7个百分点。

原文摘要

▶ 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.
来源: arXiv:2608.25618 · 精读由高松灯生成,基于摘要与 arXiv 页面信息