@inproceedings{gao-etal-2026-skim,
title = "Skim and Skip: Hierarchical Adaptive Inference for Efficient Multimodal Retrieval",
author = "Gao, Meng and
Zhang, Yizhen and
Ding, Yang and
Dai, Ziqi and
Zhang, Shuoshuo and
Wang, Junjie and
Wu, Taiqiang and
Shi, Chufan and
Ji, Lei and
Jiao, Jian and
Zhang, Linfeng and
Gong, Yeyun and
Yang, Yujiu",
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.1927/",
pages = "43873--43890",
ISBN = "979-8-89176-459-0",
abstract = "Universal multimodal retrieval (UMR) increasingly adopts multimodal large language models (MLLMs) as unified embedding backbones, but their strong retrieval performance comes at substantial inference cost. Existing methods typically rely on uniformly dense inference, where all input tokens are processed through the entire model and matched using the final-layer [EOS] representation. However, this paradigm overlooks two key forms of heterogeneity in multimodal retrieval: token contributions to the final retrieval embedding are highly uneven, and different queries require markedly different amounts of inference depth. To address this, we propose **Skim and Skip (SAS)**, a hierarchical adaptive inference framework for efficient multimodal retrieval. SAS first performs token-level evidence selection to preserve only the input information most relevant to the final retrieval embedding, and then performs depth-adaptive inference to determine whether the current representation is already sufficient for reliable matching. Experiments on 12 MMEB retrieval tasks show that SAS retains about 99{\%} of the dense baseline{'}s average retrieval performance while reducing LLM-side FLOPs by up to **66.3{\%}** and achieving up to **1.64{\texttimes}** end-to-end speedup. Our code is available at [https://github.com/GaoMengGladys/SAS](https://github.com/GaoMengGladys/SAS)."
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<abstract>Universal multimodal retrieval (UMR) increasingly adopts multimodal large language models (MLLMs) as unified embedding backbones, but their strong retrieval performance comes at substantial inference cost. Existing methods typically rely on uniformly dense inference, where all input tokens are processed through the entire model and matched using the final-layer [EOS] representation. However, this paradigm overlooks two key forms of heterogeneity in multimodal retrieval: token contributions to the final retrieval embedding are highly uneven, and different queries require markedly different amounts of inference depth. To address this, we propose **Skim and Skip (SAS)**, a hierarchical adaptive inference framework for efficient multimodal retrieval. SAS first performs token-level evidence selection to preserve only the input information most relevant to the final retrieval embedding, and then performs depth-adaptive inference to determine whether the current representation is already sufficient for reliable matching. Experiments on 12 MMEB retrieval tasks show that SAS retains about 99% of the dense baseline’s average retrieval performance while reducing LLM-side FLOPs by up to **66.3%** and achieving up to **1.64×** end-to-end speedup. Our code is available at [https://github.com/GaoMengGladys/SAS](https://github.com/GaoMengGladys/SAS).</abstract>
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%0 Conference Proceedings
%T Skim and Skip: Hierarchical Adaptive Inference for Efficient Multimodal Retrieval
%A Gao, Meng
%A Zhang, Yizhen
%A Ding, Yang
%A Dai, Ziqi
%A Zhang, Shuoshuo
%A Wang, Junjie
%A Wu, Taiqiang
%A Shi, Chufan
%A Ji, Lei
%A Jiao, Jian
%A Zhang, Linfeng
%A Gong, Yeyun
%A Yang, Yujiu
%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 gao-etal-2026-skim
%X Universal multimodal retrieval (UMR) increasingly adopts multimodal large language models (MLLMs) as unified embedding backbones, but their strong retrieval performance comes at substantial inference cost. Existing methods typically rely on uniformly dense inference, where all input tokens are processed through the entire model and matched using the final-layer [EOS] representation. However, this paradigm overlooks two key forms of heterogeneity in multimodal retrieval: token contributions to the final retrieval embedding are highly uneven, and different queries require markedly different amounts of inference depth. To address this, we propose **Skim and Skip (SAS)**, a hierarchical adaptive inference framework for efficient multimodal retrieval. SAS first performs token-level evidence selection to preserve only the input information most relevant to the final retrieval embedding, and then performs depth-adaptive inference to determine whether the current representation is already sufficient for reliable matching. Experiments on 12 MMEB retrieval tasks show that SAS retains about 99% of the dense baseline’s average retrieval performance while reducing LLM-side FLOPs by up to **66.3%** and achieving up to **1.64×** end-to-end speedup. Our code is available at [https://github.com/GaoMengGladys/SAS](https://github.com/GaoMengGladys/SAS).
%U https://aclanthology.org/2026.emnlp-main.1927/
%P 43873-43890
Markdown (Informal)
[Skim and Skip: Hierarchical Adaptive Inference for Efficient Multimodal Retrieval](https://aclanthology.org/2026.emnlp-main.1927/) (Gao et al., EMNLP 2026)
ACL
- Meng Gao, Yizhen Zhang, Yang Ding, Ziqi Dai, Shuoshuo Zhang, Junjie Wang, Taiqiang Wu, Chufan Shi, Lei Ji, Jian Jiao, Linfeng Zhang, Yeyun Gong, and Yujiu Yang. 2026. Skim and Skip: Hierarchical Adaptive Inference for Efficient Multimodal Retrieval. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 43873–43890, Budapest, Hungary. Association for Computational Linguistics.