Domain-Specialized Tree of Thought through Plug-and-Play Predictors

• URL: http://arxiv.org/abs/2603.20267v1
• Date: Sat, 14 Mar 2026 10:22:01 GMT
• Title: Domain-Specialized Tree of Thought through Plug-and-Play Predictors
• Abstract summary: We introduce DST, an adaptable, plug-and-play predictor that serves as a lightweight, supervised to guide the ToT search process.<n>Our predictor enables dynamic, context-aware pruning, allowing the search to proceed with near-greedy efficiency on simpler reasoning steps.<n>Our work effectively resolves the accuracy-efficiency trade-off in tree-based reasoning, transforming ToT into a scalable and practical paradigm for complex problem-solving in LLMs.
• Score: 32.191360225502386
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While Large Language Models (LLMs) have advanced complex reasoning, prominent methods like the Tree of Thoughts (ToT) framework face a critical trade-off between exploration depth and computational efficiency. Existing ToT implementations often rely on heavyweight LLM-based self-evaluation or rigid heuristics for branch pruning, making them prohibitively expensive and inflexible for broad application. To address this, we introduce DST, an adaptable, plug-and-play predictor that serves as a lightweight, supervised heuristic to guide the ToT search process. Our predictor enables dynamic, context-aware pruning, allowing the search to proceed with near-greedy efficiency on simpler reasoning steps while adaptively expanding the search beam only when encountering uncertainty or task complexity. We evaluate our approach on a diverse suite of benchmarks spanning mathematical reasoning, general reasoning, and complex logical reasoning. Experimental results demonstrate that our method achieves accuracy competitive with or superior to strong baselines, including standard ToT, while reducing computational overhead by 26-75%. Our work effectively resolves the accuracy-efficiency trade-off in tree-based reasoning, transforming ToT from a resource-intensive technique into a scalable and practical paradigm for complex problem-solving in LLMs.

Related papers

• Advantage-Guided Gate: Reshaping Open-Ended Reasoning for Vision-Based Spatial Intelligence [70.8951332710039]
  Multimodal large language models (MLLMs) have demonstrated significant potential in complex spatial scene understanding and reasoning tasks.<n>However, their open-ended reasoning process is prone to decision errors and error accumulation, leading to instability in answer quality.<n>We propose an advantage-guided gating framework that dynamically intervenes in and corrects deviations during the reasoning process.
  arXiv  Detail & Related papers  (2026-08-08T07:46:04Z)
• CoT-Seg: Rethinking Segmentation with Chain-of-Thought Reasoning and Self-Correction [50.67483317563736]
  This paper aims to explore a system that can think step-by-step, look up information if needed, generate results, self-evaluate its own results, and refine the results.<n>We introduce CoT-Seg, a training-free framework that rethinks reasoning segmentation by combining chain-of-thought reasoning with self-correction.
  arXiv  Detail & Related papers  (2026-01-24T11:41:54Z)
• Reinforced Efficient Reasoning via Semantically Diverse Exploration [73.41112984160992]
  Reinforcement learning with verifiable rewards (RLVR) has proven effective in enhancing the reasoning of large language models (LLMs)<n>We propose reinforced efficient reasoning via semantically diverse explorations, i.e., ROSE, for LLMs.<n>Our method incorporates a semantic-entropy-based branching strategy and an $varepsilon$-exploration mechanism.
  arXiv  Detail & Related papers  (2026-01-08T15:56:44Z)
• Adaptive Test-Time Reasoning via Reward-Guided Dual-Phase Search [62.1546099504045]
  We propose a dual-phase test-time scaling framework that separates reasoning into planning and execution.<n>Specifically, we decompose reasoning trajectories and develop reward models for each phase, enabling the search to explore and prune plans and executions separately.<n> Experiments on both mathematical reasoning and code generation benchmarks demonstrate that our approach consistently improves accuracy while reducing computation redundant.
  arXiv  Detail & Related papers  (2025-09-29T19:27:23Z)
• Compressing Chain-of-Thought in LLMs via Step Entropy [12.576398947428988]
  Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting excel at complex reasoning but generate thought processes with considerable redundancy, leading to increased inference costs and reduced efficiency.<n>We introduce a novel CoT compression framework based on step entropy, a metric that quantifies the informational contribution of individual reasoning steps to identify redundancy.
  arXiv  Detail & Related papers  (2025-08-05T11:48:18Z)
• PixelThink: Towards Efficient Chain-of-Pixel Reasoning [70.32510083790069]
  PixelThink is a simple yet effective scheme that integrates externally estimated task difficulty and internally measured model uncertainty.<n>It learns to compress reasoning length in accordance with scene complexity and predictive confidence.<n> Experimental results demonstrate that the proposed approach improves both reasoning efficiency and overall segmentation performance.
  arXiv  Detail & Related papers  (2025-05-29T17:55:49Z)
• ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving [4.987786842464663]
  Tree-of-thoughts (ToT) offers a conceptually more advanced approach by modeling reasoning as an exploration within a tree structure.<n>ToTRL is designed to guide LLMs in developing the parallel ToT strategy based on the sequential CoT strategy.<n>Our ToTQwen3-8B model, trained with ToTRL, achieves significant improvement in performance and reasoning efficiency on complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-05-19T05:18:58Z)
• Dynamic Parallel Tree Search for Efficient LLM Reasoning [102.16694475391665]
  Tree of Thoughts (ToT) enhances Large Language Model (LLM) reasoning by structuring problem-solving as a spanning tree.<n>We propose Dynamic Parallel Tree Search (DPTS), a novel parallelism framework that aims to dynamically optimize the reasoning path in inference.<n> Experiments on Qwen-2.5 and Llama-3 with Math500 and GSM8K datasets show that DPTS significantly improves efficiency by 2-4x on average.
  arXiv  Detail & Related papers  (2025-02-22T14:13:37Z)
• Policy Guided Tree Search for Enhanced LLM Reasoning [3.090041654375235]
  Policy-Guided Tree Search (PGTS) is a framework that combines reinforcement learning with structured tree exploration to efficiently navigate reasoning paths.<n>Our key innovation is a learned policy that dynamically decides between expanding, branching, backtracking, or terminating exploration, eliminating the need for manuals or exhaustive search.
  arXiv  Detail & Related papers  (2025-02-04T22:08:20Z)
• Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning [40.069109287947875]
  We propose a novel reasoning framework called Forest-of-Thought (FoT)<n>FoT integrates multiple reasoning trees to leverage collective decision-making for solving complex logical problems.<n>FoT employs sparse activation strategies to select the most relevant reasoning paths, improving both efficiency and accuracy.
  arXiv  Detail & Related papers  (2024-12-12T09:01:18Z)
• Supervised Chain of Thought [5.389461633686935]
  Chain of Thought (CoT) prompting offers a promising approach to solving complex reasoning tasks. One-prompt-for-all approach poses significant challenges for models to generate the correct reasoning steps. We show how task-specific supervision is essential for navigating the prompt space accurately and achieving optimal performance.
  arXiv  Detail & Related papers  (2024-10-18T06:25:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.