# MemoBench：动态变化环境中的世界建模基准测试

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-25 08:00
- AIHOT 分数：42
- AIHOT 链接：https://aihot.virxact.com/items/cmqzhlkh701qusltjxyr2kt0g
- 原文链接：https://arxiv.org/abs/2606.27537

## AI 摘要

MemoBench 是一个针对视频生成模型在动态变化环境中的记忆一致性诊断基准，围绕“消失-重现”范式设计：目标物体经历物理过程后从画面中消失，模型需在其重新出现时正确恢复更新后的状态。基准包含 360 段真实与合成场景的真值片段，结合自动化指标与基于 VQA 的评估，覆盖四个诊断支柱。对八款当前最优模型的评测揭示了消失-重现模式下记忆一致性面临的关键难题与开放挑战。

## 正文

Video generation models aspire to simulate dynamic environments, and several benchmarks now evaluate memory consistency across frames. However, most assess consistency only while the target remains in view, and the few that force objects out of view evaluate static scenes where nothing changes during occlusion. To bridge this gap, we introduce MemoBench, a diagnostic benchmark built around the disappear-and-reappear paradigm in dynamically changing environments: a target object undergoes a physical process, disappears from view, and must be correctly recovered in its updated state upon reappearance. We curate 360 ground-truth clips spanning synthetic and real-world scenes, and design an evaluation suite combining automated metrics with VQA-based assessment across four diagnostic pillars. Evaluation of eight state-of-the-art models reveals key insights and open challenges regarding memory consistency under the disappear-and-reappear paradigm.