OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing

arXiv:2609.32780 · cs.CV, cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Benchmark — 22× in this paper · explained in 2 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Decode — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Mixture of Experts — 2× in this paper · explained in 2 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
• LoRA: Low-Rank Adaptation of Large Language Models
• EvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models
• LLaVA-OneVision: Easy Visual Task Transfer
• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
• Evaluating Object Hallucination in Large Vision-Language Models
• Visual Instruction Tuning
• MMBench: Is Your Multi-modal Model an All-around Player?
• ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
• DocVQA: A Dataset for VQA on Document Images
• DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs
• Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding
• GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language Model
• MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
• MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI

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