MR-IQA-2: Faithful Image Quality Reflection via Fine-Grained Credit Assignment

MR‑IQA‑2 introduces an actor‑editor‑judge framework that separates reasoning from rating in image quality assessment. The actor generates quality reasoning, the editor modifies the image based on identified factors, and a frozen judge compares the original and edited images to provide reflective supervision. Fine‑grained credit assignment allows distinct supervision signals for reasoning and rating, achieving competitive human‑aligned ratings while offering richer, faithful visual understanding.

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• llms
• multimodal
• benchmarks
• safety