{"total":88,"rows":[{"id":"2409.02060","title":"OLMoE: Open Mixture-of-Experts Language Models","created":"2024-09-03","cited_by_count":301,"area":"moe"},{"id":"2401.15947","title":"MoE-LLaVA: Mixture of Experts for Large Vision-Language Models","created":"2024-01-29","cited_by_count":300,"area":"mllm"},{"id":"2407.06204","title":"A Survey on Mixture of Experts in Large Language Models","created":"2024-06-26","cited_by_count":237,"area":"moe"},{"id":"2411.04996","title":"Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models","created":"2024-11-07","cited_by_count":163,"area":null},{"id":"2305.18295","title":"RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths","created":"2023-05-29","cited_by_count":142,"area":"diffusion"},{"id":"2410.05993","title":"Aria: An Open Multimodal Native Mixture-of-Experts Model","created":"2024-10-08","cited_by_count":138,"area":"moe"},{"id":"2412.15188","title":"LMFusion: Adapting Pretrained Language Models for Multimodal Generation","created":"2024-12-19","cited_by_count":120,"area":null},{"id":"2312.12379","title":"Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning","created":"2023-12-19","cited_by_count":106,"area":"mllm"},{"id":"2410.08202","title":"Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training","created":"2024-10-10","cited_by_count":82,"area":"mllm"},{"id":"2411.17040","title":"Multimodal Alignment and Fusion: A Survey","created":"2024-11-26","cited_by_count":74,"area":null},{"id":"2402.03226","title":"FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion","created":"2024-02-05","cited_by_count":68,"area":"moe"},{"id":"2305.14705","title":"Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models","created":"2023-05-24","cited_by_count":65,"area":"moe"},{"id":"2407.21770","title":"MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts","created":"2024-07-31","cited_by_count":62,"area":"moe"},{"id":"2410.12013","title":"MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router","created":"2024-10-15","cited_by_count":62,"area":"moe"},{"id":"2412.02104","title":"Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey","created":"2024-12-03","cited_by_count":52,"area":"mllm"},{"id":"2504.07951","title":"Scaling Laws for Native Multimodal Models","created":"2025-04-10","cited_by_count":50,"area":null},{"id":"2310.20208","title":"ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection","created":"2023-10-31","cited_by_count":48,"area":null},{"id":"2312.08985","title":"OMG: Towards Open-vocabulary Motion Generation via Mixture of Controllers","created":"2023-12-14","cited_by_count":47,"area":null},{"id":"2404.01260","title":"Bridging Remote Sensors with Multisensor Geospatial Foundation Models","created":"2024-04-01","cited_by_count":46,"area":null},{"id":"2408.15881","title":"LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation","created":"2024-08-28","cited_by_count":45,"area":"mllm"},{"id":"2402.19348","title":"Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook","created":"2024-02-29","cited_by_count":33,"area":null},{"id":"2404.05019","title":"Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts","created":"2024-04-07","cited_by_count":32,"area":"moe"},{"id":"2408.10159","title":"Customizing Language Models with Instance-wise LoRA for Sequential Recommendation","created":"2024-08-19","cited_by_count":32,"area":"moe"},{"id":"2401.08383","title":"Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference","created":"2024-01-16","cited_by_count":28,"area":"moe"},{"id":"2404.08452","title":"MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection","created":"2024-04-12","cited_by_count":27,"area":"moe"},{"id":"2308.11971","title":"EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE","created":"2023-08-23","cited_by_count":25,"area":"mllm"},{"id":"2311.02684","title":"Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE","created":"2023-11-05","cited_by_count":25,"area":"moe"},{"id":"2305.06324","title":"Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception","created":"2023-05-10","cited_by_count":19,"area":"moe"},{"id":"2411.13949","title":"SMoLoRA: Exploring and Defying Dual Catastrophic Forgetting in Continual Visual Instruction Tuning","created":"2024-11-21","cited_by_count":19,"area":"mllm"},{"id":"2504.09724","title":"A Survey on Efficient Vision-Language Models","created":"2025-04-13","cited_by_count":19,"area":"mllm"},{"id":"2310.10908","title":"Unlocking Emergent Modularity in Large Language Models","created":"2023-10-17","cited_by_count":17,"area":null},{"id":"2310.07188","title":"Adaptive Gating in Mixture-of-Experts based Language Models","created":"2023-10-11","cited_by_count":16,"area":"moe"},{"id":"2410.02935","title":"On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions","created":"2024-10-03","cited_by_count":16,"area":"moe"},{"id":"2412.05552","title":"SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts","created":"2024-12-07","cited_by_count":16,"area":"moe"},{"id":"2407.14093","title":"Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models","created":"2024-07-19","cited_by_count":14,"area":"mllm"},{"id":"2409.02231","title":"SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration","created":"2024-09-03","cited_by_count":14,"area":"alignment"},{"id":"2412.20157","title":"UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity","created":"2024-12-28","cited_by_count":14,"area":"moe"},{"id":"2309.03904","title":"Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis","created":"2023-09-07","cited_by_count":13,"area":"moe"},{"id":"2409.05929","title":"M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Embedding Predictive Architecture","created":"2024-09-09","cited_by_count":13,"area":"moe"},{"id":"2504.20996","title":"X-Fusion: Introducing New Modality to Frozen Large Language Models","created":"2025-04-29","cited_by_count":13,"area":null},{"id":"2507.12566","title":"Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models","created":"2025-07-16","cited_by_count":13,"area":"mllm"},{"id":"2405.17082","title":"Ensembling Diffusion Models via Adaptive Feature Aggregation","created":"2024-05-27","cited_by_count":12,"area":"diffusion"},{"id":"2402.06570","title":"Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control","created":"2024-02-09","cited_by_count":11,"area":null},{"id":"2406.19905","title":"Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language Model","created":"2024-06-28","cited_by_count":11,"area":"mllm"},{"id":"2501.04322","title":"Eve: Efficient Multimodal Vision Language Models with Elastic Visual Experts","created":"2025-01-08","cited_by_count":11,"area":"mllm"},{"id":"2309.14327","title":"DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention","created":"2023-09-25","cited_by_count":10,"area":null},{"id":"2312.02520","title":"Towards More Unified In-context Visual Understanding","created":"2023-12-05","cited_by_count":10,"area":null},{"id":"2405.00557","title":"Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment","created":"2024-05-01","cited_by_count":10,"area":"lr"},{"id":"2507.17436","title":"Dynamic-DINO: Fine-Grained Mixture of Experts Tuning for Real-time Open-Vocabulary Object Detection","created":"2025-07-23","cited_by_count":8,"area":"mllm"},{"id":"2403.08281","title":"Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models","created":"2024-03-13","cited_by_count":7,"area":null},{"id":"2409.01990","title":"Designing Large Foundation Models for Efficient Training and Inference: A Survey","created":"2024-09-03","cited_by_count":7,"area":null},{"id":"2501.16295","title":"Mixture-of-Mamba: Enhancing Multi-Modal State-Space Models with Modality-Aware Sparsity","created":"2025-01-27","cited_by_count":7,"area":"ssm"},{"id":"2411.00918","title":"LIBMoE: A Library for comprehensive benchmarking Mixture of Experts in Large Language Models","created":"2024-11-01","cited_by_count":6,"area":"moe"},{"id":"2512.03125","title":"Mitigating Intra- and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models","created":"2025-12-02","cited_by_count":6,"area":null},{"id":"2501.18936","title":"Revisit Visual Prompt Tuning: The Expressiveness of Prompt Experts","created":"2025-01-31","cited_by_count":5,"area":"moe"},{"id":"2508.18753","title":"CrossHOI-Bench: A Unified Benchmark for HOI Evaluation across Vision-Language Models and HOI-Specific Methods","created":"2025-08-26","cited_by_count":5,"area":"mllm"},{"id":"2411.19402","title":"On the Role of Discrete Representation in Sparse Mixture of Experts","created":"2024-11-28","cited_by_count":4,"area":"moe"},{"id":"2502.10447","title":"MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition","created":"2025-02-11","cited_by_count":4,"area":"moe"},{"id":"2505.21079","title":"Uni3D-MoE: Scalable Multimodal 3D Scene Understanding via Mixture of Experts","created":"2025-05-27","cited_by_count":4,"area":"mllm"},{"id":"2508.09779","title":"MoIIE: Mixture of Intra- and Inter-Modality Experts for Large Vision Language Models","created":"2025-08-13","cited_by_count":4,"area":"mllm"},{"id":"2506.06406","title":"SMAR: Soft Modality-Aware Routing Strategy for MoE-based Multimodal Large Language Models Preserving Language Capabilities","created":"2025-06-06","cited_by_count":3,"area":"mllm"},{"id":"2508.09204","title":"MoQE: Improve Quantization Model performance via Mixture of Quantization Experts","created":"2025-08-09","cited_by_count":3,"area":"moe"},{"id":"2509.12715","title":"AsyMoE: Leveraging Modal Asymmetry for Enhanced Expert Specialization in Large Vision-Language Models","created":"2025-09-16","cited_by_count":3,"area":"mllm"},{"id":"2405.16874","title":"CoCoGesture: Toward Coherent Co-speech 3D Gesture Generation in the Wild","created":"2024-05-27","cited_by_count":2,"area":"diffusion"},{"id":"2503.21227","title":"LLaVA-CMoE: Towards Continual Mixture of Experts for Large Vision-Language Models","created":"2025-03-27","cited_by_count":2,"area":"mllm"},{"id":"2505.22360","title":"Identity-Preserving Text-to-Image Generation via Dual-Level Feature Decoupling and Expert-Guided Fusion","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2508.11326","title":"MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts","created":"2025-08-15","cited_by_count":2,"area":"moe"},{"id":"2310.03108","title":"Reinforcement Learning-based Mixture of Vision Transformers for Video Violence Recognition","created":"2023-10-04","cited_by_count":1,"area":"moe"},{"id":"2312.12458","title":"When Parameter-efficient Tuning Meets General-purpose Vision-language Models","created":"2023-12-16","cited_by_count":1,"area":"mllm"},{"id":"2503.22996","title":"Rethinking Sparse Mixture of Experts from a Unified Perspective","created":"2025-03-29","cited_by_count":1,"area":"moe"},{"id":"2506.21864","title":"DeepOmni: Towards Seamless and Smart Speech Interaction with Adaptive Modality-Specific MoE","created":"2025-06-27","cited_by_count":1,"area":"mllm"},{"id":"2510.05125","title":"Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendation","created":"2025-09-30","cited_by_count":1,"area":"lr"},{"id":"2601.21547","title":"Multi-Modal Time Series Prediction via Mixture of Modulated Experts","created":"2026-01-29","cited_by_count":1,"area":"moe"},{"id":"2605.30992","title":"Eigenvectors of Experts are Training-free Non-collapsing Routers","created":"2026-05-29","cited_by_count":1,"area":"moe"},{"id":"2405.18669","title":"Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities","created":"2024-05-29","cited_by_count":0,"area":null},{"id":"2504.06618","title":"Human-like compositional learning of visually-grounded concepts using synthetic environments","created":"2025-04-09","cited_by_count":0,"area":"lr"},{"id":"2508.19498","title":"UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models","created":"2025-08-27","cited_by_count":0,"area":null},{"id":"2602.10585","title":"Neural Additive Experts: Context-Gated Experts for Controllable Model Additivity","created":"2026-02-11","cited_by_count":0,"area":null},{"id":"2603.27965","title":"ExFusion: Efficient Transformer Training via Multi-Experts Fusion","created":"2026-03-30","cited_by_count":0,"area":"moe"},{"id":"2604.05267","title":"Do Domain-specific Experts exist in MoE-based LLMs?","created":"2026-04-07","cited_by_count":0,"area":"moe"},{"id":"2605.03348","title":"Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts","created":"2026-05-05","cited_by_count":0,"area":"moe"},{"id":"2605.20610","title":"Beyond Routing: Characterising Expert Tuning and Representation in Vision Mixture-of-Experts","created":"2026-05-20","cited_by_count":0,"area":"moe"},{"id":"2606.00275","title":"Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Models","created":"2026-05-29","cited_by_count":0,"area":"mllm"},{"id":"2607.00293","title":"Rosetta: Composable Native Multimodal Pretraining","created":"2026-07-01","cited_by_count":0,"area":"moe"},{"id":"2607.00457","title":"Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments","created":"2026-07-01","cited_by_count":0,"area":"wm"},{"id":"2607.10796","title":"Mixture of Cognitive Experts in Large Vision-Language Models","created":"2026-07-12","cited_by_count":0,"area":"lr"},{"id":"2607.29463","title":"Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification","created":"2026-07-31","cited_by_count":0,"area":"moe"},{"id":"2608.09907","title":"DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning","created":"2026-08-10","cited_by_count":0,"area":"mllm"}],"next_cursor":null}