Modality-Guided Mixture of Structured Experts with Entropy-Triggered Routing for Multimodal Recommendation

arXiv:2602.20723 · cs.AI · Submitted 2026-02-24 · Read on arXiv

cs.AI

Submitted: 2026-02-24

Updated: 2026-09-14

Comments: 36 pages, 9 figures. Code and reproducibility configurations: https://github.com/jidaivita/MAGNET

Code: https://github.com/jidaivita/MAGNET

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Regularization — 32× in this paper · explained in 3 episodes
• Embeddings — 22× in this paper · explained in 2 episodes
• Alignment — 15× in this paper · explained in 1 episode
• Aggregation — 10× in this paper · explained in 1 episode
• Robustness — 10× in this paper · explained in 5 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Interpretability — 9× in this paper · explained in 15 episodes
• Sparsity — 8× in this paper · explained in 4 episodes
• Core — 7× in this paper · explained in 2 episodes
• Multimodal Fusion — 7× in this paper · explained in 3 episodes
• Representation Learning — 7× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Contrastive Learning — 6× in this paper · explained in 5 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Mixture of Experts — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Mixture-of-Experts (MoE) — 4× in this paper · explained in 7 episodes
• Self-Supervised Learning — 4× in this paper · explained in 8 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Bayesian Personalized Ranking — 3× in this paper
• Disentangled Representation Learning — 3× in this paper
• Transformer — 3× in this paper · explained in 2 episodes
• Actor — 2× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Adaptive Graph — 2× in this paper · explained in 1 episode
• Adaptivity — 2× in this paper · explained in 1 episode
• Anchor Source — 2× in this paper · explained in 1 episode
• Distribution Shift — 2× in this paper · explained in 6 episodes
• GNNs — 2× in this paper · explained in 2 episodes
• Graph Construction — 2× in this paper · explained in 3 episodes
• Mixture of Experts (MoE) — 2× in this paper · explained in 5 episodes
• Multi-Task Learning — 2× in this paper
• Two-stage training — 2× in this paper · explained in 2 episodes
• Adaptive Multimodal Fusion — 1× in this paper · explained in 1 episode
• Agentic AI — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Entanglement — 1× in this paper · explained in 4 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• Hypergraph — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Multimodal Integration — 1× in this paper · explained in 2 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Progressive Layered Extraction — 1× in this paper
• Pruning — 1× in this paper · explained in 4 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Exploration vs Exploitation: Rethinking RLVR through Clipping, Entropy, and Spurious Reward
• Reasoning with Exploration: An Entropy Perspective
• Agentic Entropy-Balanced Policy Optimization
• Agentic Reinforced Policy Optimization
• Proximal Policy Optimization Algorithms
• A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions
• ST-MoE: Designing Stable and Transferable Sparse Expert Models

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection