Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning
Large language models (LLMs), especially reasoning models, generate extended chain-of-thought (CoT) reasoning that often contains explicit deliberation over future outcomes. Yet whether this deliberation constitutes genuine planning, how it is structured, and what aspects of it drive performance remain poorly understood. In this work, we introduce a new method to characterize LLM planning by extracting and quantifying search trees from reasoning traces in the four-in-a-row board game. By fitting computational models on the extracted search trees, we characterize how plans are structured and how they influence move decisions. We find that LLMs' search is shallower than humans', and that pe...
| Authors | Sixing Chen, Ji-An Li, Saner Cakir et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 May 2026 |
| Last updated at source | 22 May 2026 |
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