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Current image quality metrics struggle to articulate *why* one high-quality image is better than another, but this challenge shows MLLMs are closing the gap by providing expert-level explanations.
A new dataset, SeIQA, offers a benchmark to evaluate how humans perceive semantic loss in degraded images, pushing beyond traditional quality metrics.
Untangling LLM control into explicit decision-making layers slashes futile actions and boosts task success, revealing failure modes you can actually debug.
