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UniBlendNet achieves state-of-the-art ambient lighting normalization by adaptively correcting for spatially-varying illumination, leading to visually superior and more stable image restoration.
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.
Humans are surprisingly vulnerable to deception by compromised LLM agents, with less than 10% detecting attacks even in high-stakes scenarios.
