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ThinkJev is a training-free method that builds fast (System 1) and slow (System 2) answering paths from a single hybrid-thinking open model, reading calibrated option probabilities directly or after variable-length thought, so it only pays for thinking where it statistically guarantees a target error rate.
This paper introduces PatternEval, a diagnostic benchmark for evaluating response-pattern failures in hybrid-thinking multimodal large language models, and proposes PatternRL for aligning these patterns through reinforcement learning with specific penalties.