On AIME24 and AIME25, temporal discounting alone raises average accuracy by 2.04 points over vanilla OPD; naive reward mixing adds only 0.05 point beyond discounting, while the full discounting-plus-mixing-plus-normalization configuration reaches 57.56 average accuracy, a 3.91-point gain. Mixing plus normalization without discounting gains 2.00 points, supporting complementary contributions.

On AIME24 and AIME25, temporal discounting alone raises average accuracy by 2.04 points over vanilla OPD; naive reward mixing adds only 0.05 point beyond discounting, while the full discounting-plus-mixing-plus-normalization configuration reaches 57.56 average accuracy, a 3.91-point gain. Mixing plus normalization without discounting gains 2.00 points, supporting complementary contributions. · CiteArk

测量结果

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保留同一结论下的完整结果组。已有评估排在前面；单项判断不替代整组结论。展开行查看条件、来源与历次证据。

每行是一项测量，分别显示论文报告、最近可用实测和累计证据状态。
指标与条件	论文报告	最近可用实测	证据判断
dataset: AIME24	56.46%	—	尚未评估
dataset: AIME25	50.83%	—	尚未评估
dataset: AIME24 and AIME25	53.65%	—	尚未评估
dataset: AIME24	58.29%	—	尚未评估
dataset: AIME25	53.08%	—	尚未评估
dataset: AIME24 and AIME25	55.69%	—	尚未评估
	2.04 percentage points	—	尚未评估
dataset: AIME24	58.81%	—	尚未评估
dataset: AIME25	52.67%	—	尚未评估
dataset: AIME24 and AIME25	55.74%	—	尚未评估
	2.09 percentage points	—	尚未评估
dataset: AIME24	57.55%	—	尚未评估

报告指标与观测值

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测量结果