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Dynamic adjustment of reasoning paths based on continuous difficulty estimates can save up to 76% in token consumption without losing accuracy in complex reasoning tasks.
Fine-grained image retrieval gets a boost: DQE-CIR learns to focus on distinctive visual features conditioned on modification text, outperforming standard contrastive learning.
