STATe-of-Thoughts: Structured Action Templates for Tree-of-Thoughts
Inference-Time-Compute (ITC) methods like Best-of-N and Tree-of-Thoughts are meant to produce output candidates that are both high-quality and diverse, but their use of high-temperature sampling often fails to achieve meaningful output diversity. Moreover, existing ITC methods offer limited control over how to perform reasoning, which in turn limits their explainability. We present STATe-of-Thoughts (STATe), an interpretable ITC method that searches over high-level reasoning patterns. STATe replaces stochastic sampling with discrete and interpretable textual interventions: a controller selects actions encoding high-level reasoning choices, a generator produces reasoning steps conditioned on those choices, and an evaluator scores candidates to guide search. This structured approach yields three main advantages. First, action-guided textual interventions produce greater response diversity than temperature-based sampling. Second, in a case study on argument generation, STATe's explicit action sequences capture interpretable features that are highly predictive of output quality. Third, estimating the association between performance and action choices allows us to identify promising yet unexplored regions of the action space and steer generation directly toward them. Together, these results establish STATe as a practical framework for generating high-quality, diverse, and interpretable text. Our framework is available at https://github.com/zbambergerNLP/state-of-thoughts.
6994205e50fb2c0be4783df6
Zachary Bamberger
6994205e50fb2c0be4783df7
Till R. Saenger
claimed_verified
2026-02-17T15:08:48.423Z
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/avatars/e08cee5504dc55e4f7fa0ed5227f16d1.svg
Till Saenger
TillRS
user
TillRS
6994205e50fb2c0be4783df8
Gilad Morad
claimed_verified
2026-02-18T09:07:14.117Z
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/avatars/f948d9cce1670136b21715b5bcc6deb7.svg
Gilad Morad
gilad124
user
gilad124
6994205e50fb2c0be4783df9
Ofra Amir
6994205e50fb2c0be4783dfa
Brandon M. Stewart
6994205e50fb2c0be4783dfb
Amir Feder
STATe-of-Thoughts: Structured Action Templates for Tree-of-Thoughts
Inference-Time-Compute (ITC) methods like Best-of-N and Tree-of-Thoughts are meant to produce output candidates that are both high-quality and diverse, but their use of high-temperature sampling often fails to achieve meaningful output diversity. Moreover, existing ITC methods offer limited control over how to perform reasoning, which in turn limits their explainability. We present STATe-of-Thoughts (STATe), an interpretable ITC method that searches over high-level reasoning patterns. STATe replaces stochastic sampling with discrete and interpretable textual interventions: a controller selects actions encoding high-level reasoning choices, a generator produces reasoning steps conditioned on those choices, and an evaluator scores candidates to guide search. This structured approach yields three main advantages. First, action-guided textual interventions produce greater response diversity than temperature-based sampling. Second, in a case study on argument generation, STATe's explicit action sequences capture interpretable features that are highly predictive of output quality. Third, estimating the association between performance and action choices allows us to identify promising yet unexplored regions of the action space and steer generation directly toward them. Together, these results establish STATe as a practical framework for generating high-quality, diverse, and interpretable text. Our framework is available at https://github.com/zbambergerNLP/state-of-thoughts.
STATe presents an interpretable inference-time compute method that uses discrete textual interventions to generate diverse, high-quality, and explainable text by searching over reasoning patterns rather than relying on stochastic sampling.
Best-of-N Tree-of-Thoughts high-temperature sampling inference-time compute textual interventions action-guided generation reasoning patterns argument generation action sequences performance estimation