@inproceedings{lin-etal-2026-test,
title = "Test-Time Scaling for Scientific Equation Discovery",
author = "Lin, Haowei and
Lim, Hubert and
Wang, Xiangyu and
Huang, Letian and
He, Di",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.354/",
pages = "8122--8143",
ISBN = "979-8-89176-459-0",
abstract = "Test-time scaling (TTS) improves language model reasoning by allocating additional test-time compute, but prior work mainly studies closed-ended tasks such as math and coding. We study TTS for automated equation discovery, an open-ended setting where models search over candidate equations and rely on observed datapoints for feedback. We formulate LLM-driven equation discovery as an iterative search process that unifies Best-of-N, sequential refinement, tree search, and evolution-style methods under a common compute-allocation view. To isolate allocation effects from prompt engineering and other heuristics, we compare minimal parallel controllers under fixed budgets. On LLM-SRBench equation-discovery tasks, we find that search width is the dominant allocation parameter: the best width in our sweep generally increases with the compute budget, while the population{--}branching split and controller choice matter less. Appropriate width selection also improves wall-clock efficiency by increasing parallelism. These results suggest that, given an informative verifier, controlling exploration and exploitation is central to scaling LLM-based equation discovery."
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%0 Conference Proceedings
%T Test-Time Scaling for Scientific Equation Discovery
%A Lin, Haowei
%A Lim, Hubert
%A Wang, Xiangyu
%A Huang, Letian
%A He, Di
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lin-etal-2026-test
%X Test-time scaling (TTS) improves language model reasoning by allocating additional test-time compute, but prior work mainly studies closed-ended tasks such as math and coding. We study TTS for automated equation discovery, an open-ended setting where models search over candidate equations and rely on observed datapoints for feedback. We formulate LLM-driven equation discovery as an iterative search process that unifies Best-of-N, sequential refinement, tree search, and evolution-style methods under a common compute-allocation view. To isolate allocation effects from prompt engineering and other heuristics, we compare minimal parallel controllers under fixed budgets. On LLM-SRBench equation-discovery tasks, we find that search width is the dominant allocation parameter: the best width in our sweep generally increases with the compute budget, while the population–branching split and controller choice matter less. Appropriate width selection also improves wall-clock efficiency by increasing parallelism. These results suggest that, given an informative verifier, controlling exploration and exploitation is central to scaling LLM-based equation discovery.
%U https://aclanthology.org/2026.emnlp-main.354/
%P 8122-8143
Markdown (Informal)
[Test-Time Scaling for Scientific Equation Discovery](https://aclanthology.org/2026.emnlp-main.354/) (Lin et al., EMNLP 2026)
ACL
- Haowei Lin, Hubert Lim, Xiangyu Wang, Letian Huang, and Di He. 2026. Test-Time Scaling for Scientific Equation Discovery. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 8122–8143, Budapest, Hungary. Association for Computational Linguistics.