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Filtering out high-frequency tokens from LLM embeddings can significantly enhance their semantic quality and downstream performance.
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.