Seeing and Solving Are Not Enough for Vision-Language Models

arXiv:2609.33694 · cs.CV · Submitted 2026-09-27 · Read on arXiv

cs.CV

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Accuracy — 26× in this paper · explained in 1 episode
• Fine-Tuning — 18× in this paper · explained in 5 episodes
• Consistency — 11× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• State Realization Tuning — 6× in this paper
• Grounding — 4× in this paper · explained in 3 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 2× in this paper
• Transactions on Machine Learning Research — 2× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Composition Failure Rate — 1× in this paper
• Composition Failure Share — 1× in this paper
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Meeting of the Association for Computational Linguistics — 1× in this paper
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Qwen2.5-VL Technical Report
• Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding
• DISSECT: Diagnosing Where Vision Ends and Language Priors Begin in Scientific VLMs
• Show Your Work: Scratchpads for Intermediate Computation with Language Models
• Beyond Bag-of-Words: Diagnosing Compositional Binding Failures in Vision-Language Models
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models