@inproceedings{wu-etal-2026-lame,
title = "{L}a{ME}: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck",
author = "Wu, Peixi and
Yang, Biao and
Ma, Feipeng and
Chai, Bosong and
Lin, Bo and
Yuan, Wei and
Yang, Fan and
Gao, Tingting and
Li, Hebei and
Sun, Xiaoyan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1008/",
pages = "21317--21329",
ISBN = "979-8-89176-462-0",
abstract = "Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. Despite the strong performance across both general and complex tasks, this paradigm suffers from two core limitations: (i) autoregressive CoT reasoning incurs high computational cost, making it impractical for low-latency retrieval; and (ii) embedding performance is heavily coupled with CoT annotation quality, making large-scale training unreliable. These raise fundamental questions:Is textual CoT the optimal form of reasoning for embedding, and can effective embedding reasoning be accomplished in latent space? To this end, we propose LaME (Latent Reasoning Multimodal Embedding), which formulates embedding-oriented latent reasoning as a weakly supervised information bottleneck. LaME employs K learnable reason tokens as a fixed-capacity bottleneck, completing all reasoning within a single forward pass. The two weak supervision signals structurally decouple contrastive from autoregressive objectives and eliminate dependence on CoT annotations, while a two-stage training pipeline ensures stable convergence. Experiments on MMEB-v2 and MRMR show that LaME achieves competitive performance, surpassing some explicit CoT-based models, while delivering 60{\texttimes} faster inference than explicit CoT methods and 2{\texttimes} faster than latent baselines with throughput comparable to discriminative embedding models. The code is available at https://github.com/PeppaWu/LaME."
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<abstract>Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. Despite the strong performance across both general and complex tasks, this paradigm suffers from two core limitations: (i) autoregressive CoT reasoning incurs high computational cost, making it impractical for low-latency retrieval; and (ii) embedding performance is heavily coupled with CoT annotation quality, making large-scale training unreliable. These raise fundamental questions:Is textual CoT the optimal form of reasoning for embedding, and can effective embedding reasoning be accomplished in latent space? To this end, we propose LaME (Latent Reasoning Multimodal Embedding), which formulates embedding-oriented latent reasoning as a weakly supervised information bottleneck. LaME employs K learnable reason tokens as a fixed-capacity bottleneck, completing all reasoning within a single forward pass. The two weak supervision signals structurally decouple contrastive from autoregressive objectives and eliminate dependence on CoT annotations, while a two-stage training pipeline ensures stable convergence. Experiments on MMEB-v2 and MRMR show that LaME achieves competitive performance, surpassing some explicit CoT-based models, while delivering 60× faster inference than explicit CoT methods and 2× faster than latent baselines with throughput comparable to discriminative embedding models. The code is available at https://github.com/PeppaWu/LaME.</abstract>
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%0 Conference Proceedings
%T LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck
%A Wu, Peixi
%A Yang, Biao
%A Ma, Feipeng
%A Chai, Bosong
%A Lin, Bo
%A Yuan, Wei
%A Yang, Fan
%A Gao, Tingting
%A Li, Hebei
%A Sun, Xiaoyan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wu-etal-2026-lame
%X Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. Despite the strong performance across both general and complex tasks, this paradigm suffers from two core limitations: (i) autoregressive CoT reasoning incurs high computational cost, making it impractical for low-latency retrieval; and (ii) embedding performance is heavily coupled with CoT annotation quality, making large-scale training unreliable. These raise fundamental questions:Is textual CoT the optimal form of reasoning for embedding, and can effective embedding reasoning be accomplished in latent space? To this end, we propose LaME (Latent Reasoning Multimodal Embedding), which formulates embedding-oriented latent reasoning as a weakly supervised information bottleneck. LaME employs K learnable reason tokens as a fixed-capacity bottleneck, completing all reasoning within a single forward pass. The two weak supervision signals structurally decouple contrastive from autoregressive objectives and eliminate dependence on CoT annotations, while a two-stage training pipeline ensures stable convergence. Experiments on MMEB-v2 and MRMR show that LaME achieves competitive performance, surpassing some explicit CoT-based models, while delivering 60× faster inference than explicit CoT methods and 2× faster than latent baselines with throughput comparable to discriminative embedding models. The code is available at https://github.com/PeppaWu/LaME.
%U https://aclanthology.org/2026.findings-emnlp.1008/
%P 21317-21329
Markdown (Informal)
[LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck](https://aclanthology.org/2026.findings-emnlp.1008/) (Wu et al., Findings 2026)
ACL
- Peixi Wu, Biao Yang, Feipeng Ma, Bosong Chai, Bo Lin, Wei Yuan, Fan Yang, Tingting Gao, Hebei Li, and Xiaoyan Sun. 2026. LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 21317–21329, Budapest, Hungary. Association for Computational Linguistics.