这篇论文测了语言模型会不会按给定的模态逻辑规则推理，DeepSeek V4 Flash开推理模式后从4.4%直接飙到88.1%，反差很大，可以看看。
研究构造了前提相同但框架或域条件不同的配对模态问题，自动推理验证了相反标签。在平衡核心测试中，直接提示下五款近期模型中有四款表现低于条件基线。启用推理模式后，DeepSeek V4 Flash在相同提示下从4.4%升至88.1%。结果表明模型遵循规定语义的能力高度依赖推理模式和模型身份。
Same Formulas, Different Semantics: Do Language Models Follow Modal Logic Specifications?
Reasoning about necessity and possibility depends on assumptions about accessibility between worlds and about which objects exist at each one. The same inference may therefore hold under one modal system and fail under another. Evaluating language models on such problems requires testing whether their judgments follow the stated semantics rather than a familiar logic. We construct paired modal problems with identical premises and conjecture but different frame or domain conditions; automated reasoning verifies opposite labels. A balanced core prevents the semantic condition alone from revealing the answer. On this core, four of five recent models perform below the condition-only baseline under direct prompting. Yet enabling reasoning mode raises DeepSeek V4 Flash from 4.4% to 88.1% on unchanged prompts. Following stipulated modal semantics thus depends strongly on inference mode as well as model identity. When frame conditions are omitted, models often agree but fit different familiar logics best. We release the formulas, oracle artifacts, countermodels, and responses.