Papers with ToT
On the Empirical Complexity of Reasoning and Planning in LLMs (2024.findings-emnlp)
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| Challenge: | Evidence shows that the relative performance of CoT, ToT, and their variants may vary from task to task. |
| Approach: | They propose to use chain-of-thought (CoT), tree-of thought (ToT), and related techniques to solve complex reasoning tasks with Large Language Models. |
| Outcome: | The proposed methods outperform the linear structure of CoT on hard reasoning tasks. |
Benchmarking Uncertainty Metrics for LLM Target-Aware Search (2025.findings-emnlp)
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| Challenge: | Existing uncertainty metrics for LLM search methods do not capture the diverse types of uncertainty needed to guide different optimization goals. |
| Approach: | They propose a framework for uncertainty benchmarking that captures four different uncertainty types . the uncertainty types Answer, Correctness, Aleatoric, and Epistemic serve different optimization goals . |
| Outcome: | The proposed framework identifies four different uncertainty types . the uncertainty types serve different optimization goals in LLM search . |
Dynamic Parallel Tree Search for Efficient LLM Reasoning (2025.acl-long)
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Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, Xianglong Liu, Dacheng Tao
| Challenge: | Recent methods focus on search accuracy while overlooking computational efficiency. |
| Approach: | They propose a parallelism framework that dynamically optimizes reasoning path in inference. |
| Outcome: | The proposed framework improves efficiency by 2-4 on average while maintaining or even surpassing existing reasoning algorithms in accuracy. |
Defining and Evaluating Visual Language Models’ Basic Spatial Abilities: A Perspective from Psychometrics (2025.acl-long)
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| Challenge: | Existing studies assessing the spatial abilities of VLMs lack a solid theoretical foundation and lack measurable data. |
| Approach: | They propose a psychometric framework defining five basic spatial abilities in Visual Language Models. |
| Outcome: | The proposed framework defines five basic spatial abilities in Visual Language Models (VLMs) it provides a comprehensive evaluation benchmark and methodological perspective for embodied AI development . |
ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming (2025.emnlp-main)
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| Challenge: | Constraint programming (CP) is a powerful paradigm for solving constraint optimization problems. |
| Approach: | They propose to use an open-source LLM to generate formal modeling for COPs. |
| Outcome: | The proposed model outperforms the baselines on the new IndusCP benchmark by 2x. |
Tree of Problems: Improving structured problem solving with compositionality (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable performance across multipletasks through in-context learning. |
| Approach: | They propose a Tree of Problems (ToP) that is a simpler version of Tree of Thoughts (toT) they propose 'in-context learning' is the ability of Large Language Models (LLMs) to perform a task with the help of a few demonstrations within their context. |
| Outcome: | The proposed approach outperforms ToT and GoT and performs better on complex reasoning tasks. |
LogicTree: Structured Proof Exploration for Coherent and Rigorous Logical Reasoning with Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have remarkable multi-step reasoning capabilities, but they still face challenges in complex logical reasoning. |
| Approach: | They propose an algorithm-guided search framework that automates structured proof exploration and ensures logical coherence. |
| Outcome: | The proposed framework outperforms o3-mini and chain-of-thought with average gains of 23.6% and 12.5% on five datasets. |