# Integrating the Analytic Hierarchy Process with Large Language Models for Transparent Multi-Criteria Decision-Making

arXiv: https://arxiv.org/abs/2609.16779 · HTML: https://mlsanity.com/paper/2609.16779/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [AI in Investment Analysis: LLMs for Equity Stock Ratings](https://mlsanity.com/paper/2411.00856/) (2024)
- [STRUX: An LLM for Decision-Making with Structured Explanations](https://mlsanity.com/paper/2410.12583/) (2024)
- [AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses](https://mlsanity.com/paper/2410.01246/) (2024)

## Abstract (arXiv)

LLMs are increasingly employed in a wide range of decision-making tasks. However, the opacity of their internal reasoning makes it difficult to validate or interpret their outputs, and the need for interpretability becomes especially critical in high-stakes settings. This study examines the decision-making capabilities of LLMs through the Analytic Hierarchy Process (AHP), a classical and widely used multicriteria decision-making framework. We construct a new annotated benchmark based on AHP and propose the first end-to-end approach that enables LLMs to perform the complete AHP workflow. Experiments in real-world decision problems in the legal and higher-education ranking domains show that our method significantly improves alignment with expert judgments.