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Attention-supervised finetuning reveals that model architecture significantly impacts the plausibility of explanations in media bias detection, challenging assumptions about scale and performance.
Achieving nearly three times faster inference in speech synthesis without sacrificing speaker similarity could redefine efficiency benchmarks in the field.
Ditch brittle token rankings: OccamToken uses register-anchored relative evidence testing to prune visual tokens in VLMs, achieving extreme compression (down to 1.4%!) without retraining or significant accuracy loss.
