Planning with Transformers: Chain of Computation and Structured Context Windows
Large Language Models (LLMs) have had a remarkable impact across many areas of machine learning. However, recent studies have shown that they struggle to reliably solve planning problems, particularly when compared with classical planners. Even with recent advances in reasoning-oriented models, the extent to which LLMs can genuinely perform planning remains an open question. At the same time, theoretical results have shown that transformers, the core architecture underlying modern LLMs, are Turing-complete. In this work, we investigate this apparent gap between the theoretical computational power of LLMs and their empirical planning performance. We propose Chain of Computation (COC), a co...
| Authors | Ehsan Futuhi, Nathan R. Sturtevant |
|---|---|
| Primary category | cs.LG |
| Published | 20 July 2026 |
| Last updated at source | 9 October 2026 |
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