Decomposing and Steering Functional Metacognition in Large Language Models
Large language models (LLMs) increasingly exhibit behaviors suggesting awareness of their evaluation context, often adapting their reasoning strategies in benchmark settings. Prior work has shown that such evaluation awareness can distort performance measurements; however, it remains unclear whether this phenomenon reflects a single behavioral artifact or a deeper internal structure within the model. We propose that LLMs maintain a decomposable space of functional metacognitive states: internal variables encoding factors such as evaluation awareness, self-assessed capability, perceived risk, computational effort allocation, audience expertise adaptation, and intentionality. Through residu...
| Authors | Yanshi Li, Xueru Bai, Shuman Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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