腾讯PatternEval：非思考模式更易致响应失败
Switching a multimodal model into non-thinking mode saves latency, but this Tenc…
Switching a multimodal model into non-thinking mode saves latency, but this Tencent paper finds it also makes user-visible response failures much more likely.
将多模态模型切换至非思考模式虽能节省延迟，但腾讯这篇论文发现，这也使得用户可见的响应失败概率大幅增加。
PatternEval scores the shape of an answer rather than its correctness, and finds that non-thinking inference breaks user-facing response quality far more often.
PatternEval 评估的是答案的形态而非正确性，并发现非思考推理破坏用户面对响应质量的情况远为频繁。
PatternEval is a 2,415-prompt multimodal benchmark that scores 4 of them: chain-of-thought leakage, repetition, logical contradiction, and performative reasoning.
PatternEval 是一个包含 2,415 个提示的多模态基准，对其中 4 项进行评分：思维链泄漏、重复、逻辑矛盾及表演性推理。
That catches quality problems a correctness check never sees.
这能捕捉到正确性检查永远无法发现的质量问题。
If you ship a fast mode, its answers need their own eval.
如果你推出快速模式，其答案需要专属的评估体系。
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