MoLE: Mixture of Latent Experts for Complementary Visual Reasoning

arXiv:2610.01917 · cs.CV, cs.AI, cs.CL · Submitted 2026-10-01 · Read on arXiv

cs.CV, cs.AI, cs.CL

Submitted: 2026-10-01

Updated: 2026-10-01

Project page: https://sharegpt4o.github.io

Terminology

• Benchmark — 41× in this paper · explained in 2 episodes
• Latent Visual Reasoning — 18× in this paper · explained in 2 episodes
• Latent Representation — 14× in this paper · explained in 2 episodes
• Ablation — 12× in this paper · explained in 1 episode
• Accuracy — 12× in this paper · explained in 1 episode
• Latent Representations — 12× in this paper · explained in 3 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Latent Reasoning — 6× in this paper · explained in 2 episodes
• Expert-Specific Value Adapter — 5× in this paper
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Two-stage training — 5× in this paper · explained in 2 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Mixture of Latent Experts — 4× in this paper
• Hallucination — 3× in this paper · explained in 3 episodes
• Multimodal Reasoning — 3× in this paper · explained in 5 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Chain-of-Thought (CoT) Prompting — 1× in this paper · explained in 3 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Hamming Distance — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Two-Stage Training Strategy — 1× in this paper · explained in 3 episodes

Sources

• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
• UniVLR: Unifying Text and Vision in Visual Latent Reasoning for Multimodal LLMs
• ST-MoE: Designing Stable and Transferable Sparse Expert Models

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