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Compressing long chain-of-thought (CoT) from large language models (LLMs) is an emerging strategy to improve the reasoning efficiency of LLMs.
A mean value theorem in geometry of numbers
Carl Ludwig Siegel · 1945
Earlier work this paper cites.
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