WM-VLM: Probing Internal World Models for Interleaved Visual-Textual Reasoning

arXiv:2609.34826 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Accuracy — 34× in this paper · explained in 1 episode
• World Model — 23× in this paper · explained in 7 episodes
• Cosine Similarity — 12× in this paper · explained in 3 episodes
• Embeddings — 10× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Two-stage training — 8× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 5× in this paper
• Benchmark — 4× in this paper · explained in 2 episodes
• Rectified Flow — 4× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Flow Matching — 2× in this paper · explained in 8 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Latent Reasoning — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Test-time Scaling — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Two-Stage Training Strategy — 2× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Latent Visual Reasoning — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Qwen2.5-VL Technical Report
• Chart-R1: Chain-of-Thought Supervision and Reinforcement for Advanced Chart Reasoner
• ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation
• Thinking with Generated Images
• Emerging Properties in Unified Multimodal Pretraining
• Seeing Through Their Eyes: Evaluating Visual Perspective Taking in Vision Language Models
• Bridging Interleaved Multi-Modal Reasoning as a Unified Decision Process
• DreamPlan: Efficient Reinforcement Fine-Tuning of Vision-Language Planners via Video World Models
• LatentUM: Unleashing the Potential of Interleaved Cross-Modal Reasoning via a Latent-Space Unified Model
• Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models
• BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning
• Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
• What's Holding Back Latent Visual Reasoning?
• Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models
• VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs
• When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning
• Visual Latents Know More Than They Say: Unsilencing Latent Reasoning in MLLMs
• Thinking with Imagination: Agentic Visual Spatial Reasoning with World Simulators

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