Complex Problem Solving in Large Language Models: A Statistical Control Survey and Diagnostic Framework
Complex problem solving (CPS) with large language models (LLMs) is often framed as a matter of stronger reasoning or longer generation. Yet early-step error amplification, prompt brittleness, and failures to revise incorrect commitments are difficult to explain by missing knowledge or expressive capacity alone. This survey interprets CPS as a sequential estimation-and-decision problem over a latent solution state. A controller maintains a belief about an unobserved solution trajectory, updates it as noisy intermediate evidence arrives, and decides whether to commit, verify, branch, roll back, or abstain to minimize expected loss. Reasoning supplies candidate transitions and interpretation...
| Authors | Jiazhang Cai, Tao Wang, Ruidong Zhang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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