Ruizhi Qian, Lei Shi, Tianyu Hu, Bo Yan, Song Wang, Yu Tian · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26793 · cited by 0 works (OpenAlex) · open-access PDF
理智模型往往会遇到问题而没有注意到它们,即使在症状在他们自己的文本中是可见的。在人类中,元认知将监控与控制分开:快速、直观的信号保持观察,而缓慢,有意的推理发生,并有助于决定是否继续,改变方向,或停止。系统一(S1)决策模型,如Jev,是这个角色的自然候选人:他们在一个快速调用中返回对文本的自然语言问题进行校准的概率:但它们只被用作推理之前的门户。我们引入SOMA(系统一(系统一)元认知意识),其中S1模型持续监控系统二(S2)推理模型,并在需要时干预。SOMA有三层:一个场景层在一个自然语言中每一个
Machine translation (Meta M2M100); the English original follows.
Reasoning models often run into problems without noticing them, even when symptoms are visible in their own text. In humans, metacognition separates monitoring from control: fast, intuitive signals keep watch while slow, deliberate reasoning unfolds and help decide whether to continue, change course, or stop. System One (S1) decision models, such as Jev, are a natural candidate for this role: they return a calibrated probability for a natural-language question about a text in one fast call, yet they are used only as gates before reasoning. We introduce SOMA (System One Metacognitive Awareness), in which an S1 model continuously monitors a System Two (S2) reasoning model and intervenes when needed. SOMA has three layers: a scenario layer states each objective in natural language with predefined actions, an awareness layer lets S1 judge whether the described behavior appears, and a control layer accumulates these judgments, first reminds S2, and takes over only if the behavior persists. Sharing one S1 model and one controller across tasks, without training, SOMA addresses two different failures on two reasoning models: on overthinking, it cuts reasoning tokens by up to 36\% while improving average accuracy, and on unanswerable questions, it raises abstention from 70.5\% to 87.2\%.
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Topics: Computer science, Metacognition, Soma, Control (management), Natural (archaeology), Artificial intelligence
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