# iToT: An Interactive System for Customized Tree-of-Thought Generation

arXiv: https://arxiv.org/abs/2409.00413 · HTML: https://mlsanity.com/paper/2409.00413/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning.
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 31 Aug 2024.

## Placed in

- LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/)

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Active Retrieval Augmented Generation](https://mlsanity.com/paper/2305.06983/) (2023)
- [Challenges and Applications of Large Language Models](https://mlsanity.com/paper/2307.10169/) (2023)
- [Internet-augmented language models through few-shot prompting for open-domain question answering](https://mlsanity.com/paper/2203.05115/) (2022)
- [Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango](https://mlsanity.com/paper/2209.07686/) (2022)
- [Unleashing the potential of prompt engineering for large language models](https://mlsanity.com/paper/2310.14735/) (2023)
- [Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models](https://mlsanity.com/paper/2404.03622/) (2024)
- [Low-code LLM: Graphical User Interface over Large Language Models](https://mlsanity.com/paper/2304.08103/) (2023)

## Built on by (most cited first, 2 of 2)

- [Co-CoT: A Prompt-Based Framework for Collaborative Chain-of-Thought Reasoning](https://mlsanity.com/paper/2504.17091/) (2025)
- [Domain-Specialized Tree of Thought through Plug-and-Play Predictors](https://mlsanity.com/paper/2603.20267/) (2026)

## Abstract (arXiv)

As language models have become increasingly successful at a wide array of tasks, different prompt engineering methods have been developed alongside them in order to adapt these models to new tasks. One of them is Tree-of-Thoughts (ToT), a prompting strategy and framework for language model inference and problem-solving. It allows the model to explore multiple solution paths and select the best course of action, producing a tree-like structure of intermediate steps (i.e., thoughts). This method was shown to be effective for several problem types. However, the official implementation has a high barrier to usage as it requires setup overhead and incorporates task-specific problem templates which are difficult to generalize to new problem types. It also does not allow user interaction to improve or suggest new thoughts. We introduce iToT (interactive Tree-of-Thoughts), a generalized and interactive Tree of Thought prompting system. iToT allows users to explore each step of the model's problem-solving process as well as to correct and extend the model's thoughts. iToT revolves around a visual interface that facilitates simple and generic ToT usage and transparentizes the problem-solving process to users. This facilitates a better understanding of which thoughts and considerations lead to the model's final decision. Through three case studies, we demonstrate the usefulness of iToT in different human-LLM co-writing tasks.