MoGround: Measuring and Mitigating Modality Distraction in Vision-Language Models

arXiv:2609.33431 · cs.LG · Submitted 2026-09-27 · Read on arXiv

cs.LG

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/LuckerZOfficiaL/Modality-Distraction

Terminology

• Grounding — 39× in this paper · explained in 3 episodes
• Accuracy — 36× in this paper · explained in 1 episode
• Robustness — 29× in this paper · explained in 5 episodes
• Benchmark — 23× in this paper · explained in 2 episodes
• Verification — 10× in this paper · explained in 2 episodes
• Fine-Tuning — 9× in this paper · explained in 5 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Steering — 8× in this paper · explained in 2 episodes
• Hallucination — 5× in this paper · explained in 3 episodes
• AUROC — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Benchmarking — 2× in this paper · explained in 3 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• LoRA Fine-Tuning — 2× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Activation Steering — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Sparse Autoencoders (SAEs) — 1× in this paper · explained in 3 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Qwen2.5-VL Technical Report
• Diagnosing and Mitigating Modality Interference in Multimodal Large Language Models
• TokenSwap: Benchmarking and Reducing the Modality Gap in Multimodal LLMs
• AMPS: Adaptive Modality Preference Steering via Functional Entropy
• LLaVA-OneVision: Easy Visual Task Transfer
• Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
• Do Vision-Language Models See or Guess? Measuring and Reducing Textual-Prior Reliance with a Phrasing-Controlled Benchmark
• Are Reasoning Vision-Language Models Robust to Semantic Visual Distractions?
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• Evaluating and Steering Modality Preferences in Multimodal Large Language Model
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks