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By leveraging semantic redundancy, POOL can slash confidence-estimation costs by up to 76% without sacrificing accuracy.
Retrieval-augmented reasoning can boost LRM accuracy by up to 60% during test-time scaling, transforming how models handle complex problem-solving.
