arXiv AI

Beyond Modality Harmony: Orthogonal Purification and Topology-Guided MoE for Conflict-Aware Multimodal Recommendation

The paper introduces OrthoRec, a multimodal recommender system that challenges the common "modality harmony" assumption by addressing conflicts between multimodal features and collaborative patterns. It employs Collaborative‑Guided Orthogonal Purification (CGOP) to separate useful multimodal signals from noisy orthogonal components, and a Topology‑Aware Routing Mixture‑of‑Experts (TAR‑MoE) to adaptively integrate purified modalities based on collaborative topology. Experiments on Amazon datasets demonstrate that OrthoRec outperforms recent baselines and remains robust to modality noise and item sparsity.

arXiv Machine Learning

Sep 7

MURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning

MURAL is a multimodal recommendation framework that replaces static similarity graphs with a dynamic topology discovery process. It uses an Adaptive Edge Learner to find latent item-item correlations efficiently and an Uncertainty-Aware Fusion module to down‑weight noisy modality signals based on aleatoric uncertainty. The model also incorporates a contrastive teacher‑student alignment to stabilize training and has been shown to outperform state‑of‑the‑art baselines on large‑scale TikTok and Amazon datasets, providing both higher accuracy and interpretability.

By Ahmad Mousavi (Department of Mathematics,Statistics American University), Majid Alikhani (Independent Researcher), Yeon-Chang Lee (Department of Computer Science,Engineering Ulsan National Institute of Science,Technology), Roberto Corizzo (Department of Computer Science American University), Yeganeh Abdollahinejad (Department of Biosystems,Agricultural Engineering Michigan State University)

rag multimodal benchmarks safety

arXiv AI

Aug 13

VLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation

arXiv:2603. 17450v2 Announce Type: replace-cross Abstract: Sequential Recommendation (SR) in multimodal settings typically relies on small frozen pretrained encoders, which limits semantic capacity and prevents Collaborative Filtering (CF) signals from being fully integrated into item representations.

By Junyoung Kim, Woojoo Kim, Wonbin Kweon, Jaehyung Lim, Dongha Kim, Hwanjo Yu

llms rag fine-tuning multimodal

arXiv AI

Jul 7

EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation

arXiv:2508. 16170v2 Announce Type: replace-cross Abstract: MultiModal Recommendation (MMR) systems have emerged as a promising solution for improving recommendation quality by leveraging rich item-side modality information, prompting a surge of diverse methods.

By Xiaoxiong Zhang, Xin Zhou, Zhiwei Zeng, Yongjie Wang, Zhiqi Shen

llms efficiency multimodal safety

arXiv Machine Learning

Aug 17

MedMix: Specialization-Consistent Federated Sparse MoEs under Modality Heterogeneity

arXiv:2608. 13911v1 Announce Type: new Abstract: Federated multimodal medical AI faces modality heterogeneity at both the client and sample levels: clients may systematically lack access to specific modality types, while individual records within the same client may contain different partial modality subsets.

By Adiba Orzikulova, Dong Min Kim, Jaehong Yoon, Sung-Ju Lee

multimodal safety

arXiv Machine Learning

Jul 20

Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework

arXiv:2607. 15687v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offering richer semantic signals than single-modality graphs.

By Xunkai Li, Guohao Fu, Yuming Ai, Zhengyu Wu, Hongchao Qin, Rong-Hua Li, Guoren Wang

fine-tuning multimodal safety

arXiv Machine Learning

Jun 15

Context-aware Modality-Topology Co-Alignment for Multimodal Attributed Graphs

arXiv:2606. 14172v1 Announce Type: new Abstract: Multimodal Attributed Graphs (MAGs) model real-world entities by coupling graph topology with heterogeneous attributes such as text and images.

By Sirui Zhang, Xu Wang, Zhengyu Wu, Xunkai Li, Hongchao Qin

multimodal safety

arXiv Machine Learning

Jun 17

SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs

arXiv:2606. 12867v2 Announce Type: replace Abstract: Multimodal-attributed graphs (MAGs) couple graph topology with node semantics from text, images, and other modalities.

By Zhengyu Wu, Xu Wang, Hongchao Qin, Xunkai Li, Guang Zeng, Rong-Hua Li, Guoren Wang

multimodal benchmarks safety

arXiv AI

Aug 5

Adaptive Modality Reliability Diagnosis and Restoration for Robust Multimodal Intent Recognition

arXiv:2608. 03475v1 Announce Type: cross Abstract: Multimodal intent recognition combines linguistic, acoustic, and visual evidence, but individual modalities may be noisy, missing, semantically conflicting, or disproportionately dominant.

By Suraj Kumar, Mohnish Raj, Soumi Chattopadhayay, Chandranath Adak, Ayan Dutta

multimodal benchmarks

arXiv AI

Aug 7

TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning

arXiv:2604. 02183v3 Announce Type: replace Abstract: Multimodal recommendation systems (MRS) jointly model user-item interaction graphs and rich item content, but this tight coupling makes user data difficult to remove once learned.

By Zhanting Zhou, KaHou Tam, Ziqiang Zheng, Zeyu Ma, Yang Yang

multimodal

arXiv AI

Jun 8

Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models

arXiv:2602. 07026v3 Announce Type: replace-cross Abstract: Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the Modality Gap, remains: embeddings of distinct modalities expressing identical semantics occupy systematically offset regions.

By Xiaomin Yu, Yi Xin, Yuhui Zhang, Wenjie Zhang, Chonghan Liu, Hanzhen Zhao, Chen Liu, Xiaoxing Hu, Ziyue Qiao, Hao Tang, Xiaobin Hu, Chengwei Qin, Hui Xiong, Yu Qiao, Shuicheng Yan

llms rag fine-tuning multimodal safety

arXiv Machine Learning

Jun 30

Symbiotic-MoE: Unlocking the Synergy between Generation and Understanding

arXiv:2604. 07753v2 Announce Type: replace-cross Abstract: Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient conflicts.

By Xiangyue Liu, Zijian Zhang, Miles Yang, Zhao Zhong, Liefeng Bo, Ping Tan

llms diffusion multimodal benchmarks

arXiv Machine Learning

Jun 4

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

arXiv:2606. 05109v1 Announce Type: new Abstract: To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information.

By Vasiliki Rizou, Pascal Frossard, Dorina Thanou

rag multimodal benchmarks safety

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