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    <abstract>We introduce EMemBench, a programmatic benchmark generator for evaluating long-term episodic memory of agents through interactive games. Rather than using a fixed set of questions, EMemBench generates questions from environment-grounded trajectories, covering both text-only and visual game environments. Each template computes verifiable ground truth from underlying game signals, with controlled answerability and balanced coverage over memory skills: single/multi-hop recall, induction, temporal, spatial, logical, and adversarial. We evaluate memory agents with strong LMs/VLMs as backbones, using in-context prompting as baselines. Across 15 text games and multiple visual seeds, results are far from saturated: induction and spatial reasoning are persistent bottlenecks, especially in visual settings. Persistent memory yields clear gains for open backbones on text games, but improvements are less consistent for VLM agents, suggesting that visually grounded episodic memory remains an open challenge. A human study further contextualizes the difficulty and interpretability of EMemBench.</abstract>
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%0 Conference Proceedings
%T EMemBench: Interactive Benchmarking of Episodic Memory for VLM Agents
%A Li, Xinze
%A Zhu, Ziyue
%A Liu, Siyuan
%A Ma, Yubo
%A Zang, Yuhang
%A Cao, Yixin
%A Sun, Aixin
%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 li-etal-2026-emembench
%X We introduce EMemBench, a programmatic benchmark generator for evaluating long-term episodic memory of agents through interactive games. Rather than using a fixed set of questions, EMemBench generates questions from environment-grounded trajectories, covering both text-only and visual game environments. Each template computes verifiable ground truth from underlying game signals, with controlled answerability and balanced coverage over memory skills: single/multi-hop recall, induction, temporal, spatial, logical, and adversarial. We evaluate memory agents with strong LMs/VLMs as backbones, using in-context prompting as baselines. Across 15 text games and multiple visual seeds, results are far from saturated: induction and spatial reasoning are persistent bottlenecks, especially in visual settings. Persistent memory yields clear gains for open backbones on text games, but improvements are less consistent for VLM agents, suggesting that visually grounded episodic memory remains an open challenge. A human study further contextualizes the difficulty and interpretability of EMemBench.
%U https://aclanthology.org/2026.findings-emnlp.905/
%P 19163-19192

Markdown (Informal)

[EMemBench: Interactive Benchmarking of Episodic Memory for VLM Agents](https://aclanthology.org/2026.findings-emnlp.905/) (Li et al., Findings 2026)

• EMemBench: Interactive Benchmarking of Episodic Memory for VLM Agents (Li et al., Findings 2026)

ACL

• Xinze Li, Ziyue Zhu, Siyuan Liu, Yubo Ma, Yuhang Zang, Yixin Cao, and Aixin Sun. 2026. EMemBench: Interactive Benchmarking of Episodic Memory for VLM Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 19163–19192, Budapest, Hungary. Association for Computational Linguistics.