# Layer-of-Thoughts Prompting (LoT): Leveraging LLM-Based Retrieval with Constraint Hierarchies

arXiv: https://arxiv.org/abs/2410.12153 · HTML: https://mlsanity.com/paper/2410.12153/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 1 of its 33 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 1 of 1)

- [Demystifying Chains, Trees, and Graphs of Thoughts](https://mlsanity.com/paper/2401.14295/) (2024)

## Built on by (most cited first, 6 of 6)

- [Latent Collaboration in Multi-Agent Systems](https://mlsanity.com/paper/2511.20639/) (2025)
- [Eliza: A Web3 friendly AI Agent Operating System](https://mlsanity.com/paper/2501.06781/) (2025)
- [Toward Robust Legal Text Formalization into Defeasible Deontic Logic using LLMs](https://mlsanity.com/paper/2506.08899/) (2025)
- [AutoStreamPipe: LLM Assisted Automatic Generation of Data Stream Processing Pipelines](https://mlsanity.com/paper/2510.23408/) (2025)
- [ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual Attribution](https://mlsanity.com/paper/2502.00989/) (2025)
- [Hierarchical Chain-of-Thought: Enhancing LLM Reasoning Performance and Efficiency](https://mlsanity.com/paper/2604.00130/) (2026)

## Abstract (arXiv)

This paper presents a novel approach termed Layer-of-Thoughts Prompting (LoT), which utilizes constraint hierarchies to filter and refine candidate responses to a given query. By integrating these constraints, our method enables a structured retrieval process that enhances explainability and automation. Existing methods have explored various prompting techniques but often present overly generalized frameworks without delving into the nuances of prompts in multi-turn interactions. Our work addresses this gap by focusing on the hierarchical relationships among prompts. We demonstrate that the efficacy of thought hierarchy plays a critical role in developing efficient and interpretable retrieval algorithms. Leveraging Large Language Models (LLMs), LoT significantly improves the accuracy and comprehensibility of information retrieval tasks.