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Foundation models are poised to revolutionize multi-agent systems by enabling semantic-level reasoning and flexible coordination that surpasses the limitations of classical approaches.
A unified benchmark reveals the trade-offs between pixel-wise accuracy and perceptual realism in state-of-the-art image super-resolution techniques.
Current image restoration models still fail to strike the right balance between noise reduction, detail fidelity, and accurate color in real-world, low-light portrait scenarios, highlighting a critical gap this challenge aims to close.
