Multimodal recommenders combine collaborative behavior with visual and textual item evidence, whose usefulness varies across user-item interactions. Independently trained source-specific diagnostic probes partition held-out interactions into behavior-, appearance-, semantics-, and mixed-evidence regimes across five benchmarks, within which capacity-matched fixed fusion rules exhibit systematic regime-dependent performance crossovers. This diagnostic observation motivates MAGNET, a multimodal graph recommender with two core mechanisms. First, a calibrated expert bank organizes trainable experts by anchor source (behavior, appearance, or semantics) and fusion family (dominant, balanced, or complementary), while an interaction-conditioned router selects a sparse composition using all three evidence sources. Second, an entropy-triggered, coverage-aware progressive schedule decouples population-level routing-mass coverage from per-instance decisiveness, transitioning from broad routing exploration to confident specialization once sufficient coverage is sustained, while preserving coverage thereafter. MAGNET separately encodes the observed interaction graph and a filtered content-induced structural view, applying cross-view alignment after independent propagation. We evaluate MAGNET on four Amazon domains and the non-Amazon MicroLens-100K benchmark, including tail-item and low-history warm-start user evaluation alongside matched expert-design controls. Under the shared-feature, fixed-split protocol, MAGNET-DV exceeds the strongest protocol-compatible non-MAGNET baseline in every reported main-table cell, supported by seed-wise difference tests. Both the fixed and KL-anchored structured variants achieve higher five-seed mean NDCG@20 than matched homogeneous and free-mixture alternatives across all five datasets. MAGNET supports route-level diagnostics through explicit expert semantics.
MRUF: Multi-granularity Routing with Uncertainty-Aware Fusion for Robust Multimodal Sentiment Analysis
School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing（丝绸之路多语言认知计算国际联合研究实验室）；Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center（新疆多模态智能处理与信息安全工程技术研究中心）；Pengcheng Laboratory Xinjiang Network Node（鹏城实验室新疆网络节点）；Embodied Intelligence Joint Laboratory（具身智能联合实验室）
Modality-Native Routing in Agent-to-Agent Networks: A Multimodal A2A Protocol Extension
AI Architect, Author—Data Engineering for Multimodal AI (O’Reilly)（人工智能架构师，作者—多模态AI的数据工程（O’Reilly））；Stanford School of Engineering (April 2026)（斯坦福大学工程学院（2026年4月））
MMR-Bench: A Comprehensive Benchmark for Multimodal LLM Routing
School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）；National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家实验室，南京大学）
Vision-Assisted Foundation Model for Solving Multi-Task Vehicle Routing Problems
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；Institute of Marine Science and Technology, Shandong University（山东大学海洋科学与技术研究院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Centre for Frontier AI Research, Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所前沿人工智能研究中心）
Backbone-Conditional Behavior of Modality Gating in Multi-Modal Prostate MRI Segmentation: A 5-Fold Cross-Validation and Gate Mechanism Analysis
The Second Xiangya Hospital of Central South University（中南大学湘雅医学院第二医院）；The Third Xiangya Hospital of Central South University（中南大学湘雅医学院第三医院）；School of Life Sciences, Central South University（中南大学生命科学学院）；Hunan Provincial Key Laboratory of Medical Information Research (Central South University)（湖南省医学信息研究重点实验室（中南大学））；Hunan Provincial Clinical Medical Research Center for Cardiovascular Intelligent Medicine（湖南省心血管智能医学临床医学研究中心）
Wild-Drive: Off-Road Scene Captioning and Path Planning via Robust Multi-modal Routing and Efficient Large Language Model
School of Instrument Science and Engineering, Southeast University（东南大学仪器科学与工程学院）；Southeast University Nanjing Jiangbei New Area Innovation Research Institute（东南大学南京江滨新区创新研究院）；National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology（国防科技大学装备状态感知与智能支撑国家重点实验室）；School of Transportation, Shandong University of Science and Technology（山东科技大学交通学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing
School of Computer Science, Peking University（北京大学计算机科学学院）；MOE Key Laboratory of Computational Linguistics, Peking University（北京大学教育部计算语言学重点实验室）；National Key Laboratory for Multimedia Information Processing, Peking University（北京大学国家多媒体信息处理重点实验室）
Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；School of Software, Yunnan University（云南大学软件学院）；National University of Singapore（新加坡国立大学）；School of Engineering, Yunnan University（云南大学工程学院）
ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
NepAl Applied Mathematics and Informatics Institute（尼泊尔应用数学与信息学研究所）；Fogsphere (Redev.AI Ltd)（福格球（Redev.AI有限公司））；University of Lausanne（洛桑大学）；West Virginia University（西弗吉尼亚大学）；University of Verona（维罗纳大学）；University College London（伦敦大学学院）；University of Aberdeen（阿伯丁大学）
VLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing
University of Science and Technology of China（中国科学技术大学）；University of Manchester（曼彻斯特大学）；Beihang University（北航）；Fudan University（复旦大学）；University of New South Wales（新南威尔士大学）
CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
Technology, Innovation, Entrepreneurship Department（技术、创新与创业部门）；King Abdullah University of Science and Technology（卡塔尔科技大学）；School of Computing and Information Systems（计算与信息系统学院）；The University of Melbourne（墨尔本大学）；College of Computer Science and Technology（计算机科学与技术学院）；Jilin University（吉林大学）；School of Computing Technologies（计算技术学院）；RMIT University（皇家墨尔本理工大学）；College of Computing and Data Science（计算与数据科学学院）；Nanyang Technological University（南洋理工大学）
The University of Edinburgh（爱丁堡大学）；Imperial College, London（伦敦帝国学院）；University of Surrey（萨里大学）；Queen Mary University of London（伦敦玛丽女王大学）；Microsoft Research, Cambridge（微软研究院剑桥分部）
Generalist-Specialist Mixture-of-Experts for Rare Pathology Detection in Multimodal Imaging
Technical University of Munich (TUM)（慕尼黑工业大学）；TUM University Hospital（慕尼黑工业大学附属医院）；Hasso Plattner Institute for Digital Engineering（哈索·普拉特纳数字工程研究所）；University of Potsdam（波茨坦大学）；Imperial College London（伦敦帝国理工学院）
MMCTOP: A Multimodal Textualization and Mixture-of-Experts Framework for Clinical Trial Outcome Prediction
Nova School of Business and Economics（诺瓦商学院）；Hogarthian Technologies（霍加斯技术）；School of Medicine（医学院）；Oregon Health & Science University（俄勒冈健康与科学大学）；Cleveland Clinic（克利夫兰诊所）；IBM Research（IBM研究院）
