Weihai Lu Peking University, Zhejun Zhao Baidu, Yanshu Li, Huan He Amazon
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.
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cs.CL · cs.MA · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Xi Xiao University of Alabama at Birmingham, Chenrui Ma, Yunbei Zhang Tulane University, Chen Liu Yale University, Zhuxuanzi Wang Cornell University, Yanshu Li, Lin Zhao Northeastern University, Guosheng Hu University of Bristol 等 10 位作者
Low-Rank Adaptation (LoRA) has become a cornerstone of parameter-efficient fine-tuning (PEFT). Yet, its efficacy is hampered by two fundamental limitations: \textit{semantic drift}, arising from treating all update directions with equal importance, and \textit{structural incoherence}, due to adapting layers independently, resulting in uncoordinated and suboptimal updates. To address these issues, we propose \textbf{StructLoRA}, a framework that tackles both limitations through a principled dual-component design: (1) an Information Bottleneck-guided filter that prunes task-irrelevant directions to mitigate semantic drift, and (2) a lightweight, training-only graph-based coordinator that enforces inter-layer consistency to resolve structural incoherence. Extensive experiments across large language models, vision language models, and vision models (including LLaMA, LLaVA, and ViT) demonstrate that \textbf{StructLoRA} consistently establishes a new state of the art, outperforming not only vanilla LoRA but also advanced dynamic rank allocation and sparsity-based methods. Notably, the gains are particularly pronounced in challenging low-rank and low-data regimes. Crucially, since the proposed modules operate only during training, \textbf{StructLoRA} improves performance with \textbf{zero additional inference cost}, shifting the focus of PEFT from mere parameter compression to a more holistic optimization of information quality and structural integrity.
尚无 AI 解读，展示原文摘要
cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表