MemEye：一种以视觉为中心的多模态代理记忆评估框架
MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory
AI总结：
MemEye框架从视觉证据的粒度和证据使用的复杂性两个维度评估多模态代理记忆，通过8个生活场景任务构建新基准，验证记忆方法在细粒度视觉细节保留和时间状态推理上的不足。
摘要：
长期代理记忆日益多模态，但现有评估 rarely 测试代理是否保留后续推理所需的视觉证据。先前工作表明，许多视觉基础问题可通过仅用描述或文本痕迹回答，允许在不保留细粒度视觉证据的情况下推断答案。同时，需要推理变化视觉状态的困难案例大多缺失。因此，我们引入MemEye框架，从两个维度评估记忆能力：一个测量决定性视觉证据的粒度（从场景级到像素级证据），另一个测量检索证据的使用方式（从单一证据到进化合成）。在此框架下，我们构建了涵盖8个生活场景任务的新基准，包含基于消融驱动的验证门控，用于评估可回答性、捷径抵抗性、视觉必要性和推理结构。通过在4个VLM基础架构上评估13种记忆方法，我们发现当前架构仍难以保留细粒度视觉细节并推理时间状态变化。我们的发现表明，长期多模态记忆依赖于证据路由、时间跟踪和细节提取。
英文摘要：
Long-term agent memory is increasingly multimodal, yet existing evaluations rarely test whether agents preserve the visual evidence needed for later reasoning. In prior work, many visually grounded questions can be answered using only captions or textual traces, allowing answers to be inferred without preserving the fine-grained visual evidence. Meanwhile, harder cases that require reasoning over changing visual states are largely absent. Therefore, we introduce MemEye, a framework that evaluates memory capabilities from two dimensions: one measures the granularity of decisive visual evidence (from scene-level to pixel-level evidence), and the other measures how retrieved evidence must be used (from single evidence to evolutionary synthesis). Under this framework, we construct a new benchmark across 8 life-scenario tasks, with ablation-driven validation gates for assessing answerability, shortcut resistance, visual necessity, and reasoning structure. By evaluating 13 memory methods across 4 VLM backbones, we show that current architectures still struggle to preserve fine-grained visual details and reason about state changes over time. Our findings show that long-term multimodal memory depends on evidence routing, temporal tracking, and detail extraction.
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