AutoJudger : An Adaptive Evaluation Framework for Efficient Benchmarking of MLLMs
Xuanwen Ding, Chengjun Pan, Zejun Li et al.
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Preprint
Aug 2026
Thinking Hard, Not Smart: Reasoning Models Fail to Ration Test-Time Compute Across Questions
An exam-style evaluation framework is introduced for studying the global budget allocation of reasoning language models when multiple problems share an end-to-end cost or latency constraint, in which a model must distribute one shared token budget across questions with different difficulty and point values.
Chenrui Fan, Yize Cheng, Ming Li et al.
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