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        <namePart type="given">Weihai</namePart>
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    <abstract>Multimodal Stance Detection (MSD) is crucial for understanding public discourse, yet effectively fusing text and image, especially with conflicting signals, remains challenging. Existing methods often face difficulties with contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility. To address these, we propose Retrieval-Augmented Multi-modal Multi-agent Stance Detection (MM-StanceDet), a novel multi-agent framework integrating Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, a Reasoning-Enhanced Debate stage for exploring perspectives, and Self-Reflection for robust adjudication. Extensive experiments on five datasets demonstrate MM-StanceDet significantly outperforms state-of-the-art baselines, validating the efficacy of its multi-agent architecture and structured reasoning stages in addressing complex multimodal stance challenges.</abstract>
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%0 Conference Proceedings
%T MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection
%A Lu, Weihai
%A Zhao, Zhejun
%A Li, Yanshu
%A He, Huan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F lu-etal-2026-mm
%X Multimodal Stance Detection (MSD) is crucial for understanding public discourse, yet effectively fusing text and image, especially with conflicting signals, remains challenging. Existing methods often face difficulties with contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility. To address these, we propose Retrieval-Augmented Multi-modal Multi-agent Stance Detection (MM-StanceDet), a novel multi-agent framework integrating Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, a Reasoning-Enhanced Debate stage for exploring perspectives, and Self-Reflection for robust adjudication. Extensive experiments on five datasets demonstrate MM-StanceDet significantly outperforms state-of-the-art baselines, validating the efficacy of its multi-agent architecture and structured reasoning stages in addressing complex multimodal stance challenges.
%U https://aclanthology.org/2026.acl-long.1450/
%P 31449-31464

Markdown (Informal)

[MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection](https://aclanthology.org/2026.acl-long.1450/) (Lu et al., ACL 2026)

• MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection (Lu et al., ACL 2026)

ACL

• Weihai Lu, Zhejun Zhao, Yanshu Li, and Huan He. 2026. MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 31449–31464, San Diego, California, United States. Association for Computational Linguistics.