@inproceedings{wang-etal-2026-mmlongembed,
title = "{MML}ong{E}mbed: Benchmarking Multimodal Embedding Models in Long-Context Scenarios",
author = "Wang, Haitian and
Sun, Ruoxi and
Qiu, Quantong and
Li, Juntao and
Li, Junhui and
Chen, Hua and
Chang, Jinxiong and
Zhang, Min",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2586/",
pages = "58765--58785",
ISBN = "979-8-89176-459-0",
abstract = "Recent advancements have significantly expanded the theoretical context windows of Multimodal Embedding Models (MEMs). However, larger context windows do not necessarily translate into effective comprehension and representation of long-context multimodal inputs, which remains a critical bottleneck for real-world deployment. To address the lack of systematic evaluation in this setting, we introduce *MMLongEmbed*, the first comprehensive benchmark for evaluating MEMs in long-context scenarios. MMLongEmbed comprises four retrieval tasks spanning multiple context-length ranges, covering text, document, and video modalities. Through extensive evaluation of state-of-the-art models, we find that current architectures rely heavily on superficial feature matching and struggle to capture deep semantic and structural dependencies. We further observe that performance degradation varies systematically with context length and key information placement. Moreover, models exhibit substantially different robustness to redundant contextual information across modalities."
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%0 Conference Proceedings
%T MMLongEmbed: Benchmarking Multimodal Embedding Models in Long-Context Scenarios
%A Wang, Haitian
%A Sun, Ruoxi
%A Qiu, Quantong
%A Li, Juntao
%A Li, Junhui
%A Chen, Hua
%A Chang, Jinxiong
%A Zhang, Min
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-mmlongembed
%X Recent advancements have significantly expanded the theoretical context windows of Multimodal Embedding Models (MEMs). However, larger context windows do not necessarily translate into effective comprehension and representation of long-context multimodal inputs, which remains a critical bottleneck for real-world deployment. To address the lack of systematic evaluation in this setting, we introduce *MMLongEmbed*, the first comprehensive benchmark for evaluating MEMs in long-context scenarios. MMLongEmbed comprises four retrieval tasks spanning multiple context-length ranges, covering text, document, and video modalities. Through extensive evaluation of state-of-the-art models, we find that current architectures rely heavily on superficial feature matching and struggle to capture deep semantic and structural dependencies. We further observe that performance degradation varies systematically with context length and key information placement. Moreover, models exhibit substantially different robustness to redundant contextual information across modalities.
%U https://aclanthology.org/2026.emnlp-main.2586/
%P 58765-58785
Markdown (Informal)
[MMLongEmbed: Benchmarking Multimodal Embedding Models in Long-Context Scenarios](https://aclanthology.org/2026.emnlp-main.2586/) (Wang et al., EMNLP 2026)
ACL
- Haitian Wang, Ruoxi Sun, Quantong Qiu, Juntao Li, Junhui Li, Hua Chen, Jinxiong Chang, and Min Zhang. 2026. MMLongEmbed: Benchmarking Multimodal Embedding Models in Long-Context Scenarios. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 58765–58785, Budapest, Hungary. Association for Computational Linguistics.