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arXiv · 2511.14772

Test-time Scaling of LLMs: A Survey from A Subproblem Structure Perspective

Zhuoyi Yang·Xu Guo·Tong Zhang·Huijuan Xu·Boyang Li
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Abstract

With this paper, we survey techniques for improving the predictive accuracy of pretrained large language models by allocating additional compute at inference time. In categorizing test-time scaling methods, we place special emphasis on how a problem is decomposed into subproblems and on the topological organization of these subproblems whether sequential, parallel, or tree-structured. This perspective allows us to unify diverse approaches such as Chain-of-Thought, Branch-Solve-Merge, and Tree-of-Thought under a common lens. We further synthesize existing analyses of these techniques, highlighting their respective strengths and weaknesses, and conclude by outlining promising directions for future research

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Zhuoyi Yang, Xu Guo, Tong Zhang, Huijuan Xu, Boyang Li. 2025-11-01. Test-time Scaling of LLMs: A Survey from A Subproblem Structure Perspective. https://arxiv.org/abs/2511.14772

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