Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling

arXiv:2608.04554 · cs.CL, cs.CV · Submitted 2026-08-05 · Read on arXiv

Han Chen, Ming Li, Hong Jiao, Tianyi Zhou

cs.CL, cs.CV

Submitted: 2026-08-05

Code: https://github.com/MingLiiii/Visual_Item_Difficulty

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Late Fusion — 9× in this paper · explained in 2 episodes
• XGBoost — 9× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Calibration — 6× in this paper · explained in 9 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Vision-language models — 4× in this paper · explained in 2 episodes
• Cognitive Load — 3× in this paper · explained in 2 episodes
• Evaluation Protocol — 3× in this paper · explained in 2 episodes
• Papers — 3× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 3× in this paper · explained in 7 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Language Models (LLMs) — 2× in this paper · explained in 2 episodes
• Reasoning Traces — 2× in this paper · explained in 4 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Transfer Learning — 2× in this paper · explained in 16 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 8 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 9 episodes
• Parameterization — 1× in this paper · explained in 2 episodes
• Test-Time Scaling — 1× in this paper · explained in 4 episodes
• Verifier — 1× in this paper · explained in 2 episodes
• Weight Decay — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
• The Llama 3 Herd of Models
• Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction
• DINOv2: Learning Robust Visual Features without Supervision
• Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review
• Qwen2.5 Technical Report
• PaliGemma 2: A Family of Versatile VLMs for Transfer
• Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge
• Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency
• Qwen3 Technical Report
• Towards Valid Student Simulation with Large Language Models
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

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