Unverified50% confidenceFactExact time
The way thinking tokens are counted (whether truncated reasoning is included, whether the tokenizer is unified, whether comparisons are made at the same sampling temperature) affects the measured compression ratio, and under different definitions the compression ratio may differ by tens of percentage points.
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Long-term
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10/23/2026
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UnverifiedToken compression involves trade-offs: some seemingly redundant contextual information may be critical for model reasoning, and aggressive compression may affect output quality in scenarios such as code generation and complex reasoning.71% similarUnverifiedTo evaluate whether compression is beneficial or whether switching to a smaller model makes sense, one cannot look only at the drop in Token count per request, but must fix the source input and success criteria while comparing the final task result with the total usage of the entire task (including additional work such as summary generation).70% similarUnverifiedBased on the abstract information, questions such as the performance of compact Tokens under extremely sparse views or large-scale scenes, the trade-off curve between Token count and reconstruction quality, and the specific metric comparisons on generative tasks still await further understanding.70% similarUnverified降低努力程度可以通过限制生成的thinking tokens数量、调整beam search宽度、降低采样温度或通过系统级参数限制单次请求最大计算预算等方式实现69% similarUnverifiedCompressing token count and improving instruction quality are two orthogonal goals that need to be addressed separately.68% similar
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