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MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory

A framework for evaluating multimodal agent memory capabilities.

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MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory

By Minghao Guo, Qingyue Jiao, Zeru Shi, Yihao Quan, Boxuan Zhang, Danrui Li, Liwei Che, Wujiang XuarXiv
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The authors introduce MemEye, a visual-centric evaluation framework for multimodal agent memory. They propose two dimensions to measure memory capabilities: the granularity of decisive visual evidence and how retrieved evidence must be used.

The framework is applied to 13 memory methods across 4 VLM backbones, revealing that current architectures struggle to preserve fine-grained visual details.

Abstract

The authors introduce MemEye, a visual-centric evaluation framework for multimodal agent memory. They propose two dimensions to measure memory capabilities: the granularity of decisive visual evidence and how retrieved evidence must be used. The framework is applied to 13 memory methods across 4 VLM backbones, revealing that current architectures struggle to preserve fine-grained visual details.

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multimodal memory evaluationvisual-centric frameworkagent memory capabilitiesevaluation metricsmemory architecturesAI AgentsAgent MemoryLarge Language ModelsRetrieval & RAG
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