Evaluating the Evaluators: Diagnosing Large Multimodal Models for AI-Generated Image Assessment

arXiv:2609.37576 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/Kwai-Kolors/Kolors

Terminology

• Benchmark — 47× in this paper · explained in 2 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Alignment — 14× in this paper · explained in 1 episode
• Fidelity — 12× in this paper · explained in 2 episodes
• Benchmarking — 7× in this paper · explained in 3 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Grounding — 7× in this paper · explained in 3 episodes
• Semantic Understanding — 7× in this paper · explained in 2 episodes
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Semantic Alignment — 3× in this paper · explained in 3 episodes
• Visual Grounding — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Evaluation Dimensions — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Transactions on Pattern Analysis and Machine Intelligence — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• Microsoft COCO Captions: Data Collection and Evaluation Server
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
• Exploring the Naturalness of AI-Generated Images
• Emerging Properties in Unified Multimodal Pretraining
• FLUX-Reason-6M & PRISM-Bench: A Million-Scale Text-to-Image Reasoning Dataset and Comprehensive Benchmark
• Seedream 3.0 Technical Report
• The Llama 3 Herd of Models
• AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception
• Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning
• Evaluating Text-to-Visual Generation with Image-to-Text Generation
• Improving Video Generation with Human Feedback
• Step1X-Edit: A Practical Framework for General Image Editing
• DeepSeek-VL: Towards Real-World Vision-Language Understanding
• Ovis2.5 Technical Report
• Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models
• GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
• WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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