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[2602.14265] STATe-of-Thoughts: Structured Action Templates for Tree-of-Thoughts
Summary
The paper presents STATe-of-Thoughts, a new method for improving output diversity and interpretability in inference-time compute methods, enhancing reasoning control in AI-generated text.
Why It Matters
This research addresses limitations in existing inference-time compute methods by introducing a structured approach to reasoning in AI, which enhances output diversity and interpretability. This is crucial for applications in natural language processing where explainability is increasingly important.
Key Takeaways
- STATe replaces stochastic sampling with structured textual interventions for better output diversity.
- The method allows for greater control over reasoning processes, improving explainability.
- Case studies show that explicit action sequences are predictive of output quality.
Computer Science > Computation and Language arXiv:2602.14265 (cs) [Submitted on 15 Feb 2026] Title:STATe-of-Thoughts: Structured Action Templates for Tree-of-Thoughts Authors:Zachary Bamberger, Till R. Saenger, Gilad Morad, Ofra Amir, Brandon M. Stewart, Amir Feder View a PDF of the paper titled STATe-of-Thoughts: Structured Action Templates for Tree-of-Thoughts, by Zachary Bamberger and Till R. Saenger and Gilad Morad and Ofra Amir and Brandon M. Stewart and Amir Feder View PDF Abstract: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...
