# AI-Generated Compromises for Coalition Formation

arXiv: https://arxiv.org/abs/2506.06837 · HTML: https://mlsanity.com/paper/2506.06837/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Fine-tuning language models to find agreement among humans with diverse preferences](https://mlsanity.com/paper/2211.15006/) (2022)
- [LLM Voting: Human Choices and AI Collective Decision Making](https://mlsanity.com/paper/2402.01766/) (2024)
- [Generative Social Choice](https://mlsanity.com/paper/2309.01291/) (2023)
- [A Compare-Aggregate Model for Matching Text Sequences](https://mlsanity.com/paper/1611.01747/) (2016)
- [What Do Multiwinner Voting Rules Do? An Experiment Over the Two-Dimensional Euclidean Domain](https://mlsanity.com/paper/1901.09217/) (2019)
- [ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"](https://mlsanity.com/paper/1902.03616/) (2019)
- [Aggregation over Metric Spaces: Proposing and Voting in Elections, Budgeting, and Legislation](https://mlsanity.com/paper/1806.06277/) (2018)
- [United for Change: Deliberative Coalition Formation to Change the Status Quo](https://mlsanity.com/paper/2001.08031/) (2020)
- [Natural language processing for word sense disambiguation and information extraction](https://mlsanity.com/paper/2004.02256/) (2020)
- [Foundations for Grassroots Democratic Metaverse](https://mlsanity.com/paper/2203.04090/) (2022)
- [Iterative Deliberation via Metric Aggregation](https://mlsanity.com/paper/2108.00314/) (2021)

## Built on by (most cited first, 3 of 3)

- [Coalition Formation in LLM Agent Networks: Stability Analysis and Convergence Guarantees](https://mlsanity.com/paper/2604.14386/) (2026)
- [Constitutional Governance in Metric Spaces](https://mlsanity.com/paper/2605.13362/) (2026)
- [Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents](https://mlsanity.com/paper/2607.15095/) (2026)

## Abstract (arXiv)

The challenge of finding compromises between agent proposals is fundamental to AI subfields such as argumentation, mediation, and negotiation. Building on this tradition, Elkind et al. (2021) introduced a process for coalition formation that seeks majority-supported proposals preferable to the status quo, using a metric space where each agent has an ideal point. A crucial step in this process involves identifying compromise proposals around which agent coalitions can unite. How to effectively find such compromise proposals remains an open question. We address this gap by formalizing a model that incorporates agent bounded rationality and uncertainty, and by developing AI methods to generate compromise proposals. We focus on the domain of collaborative document writing, such as the democratic drafting of a community constitution. Our approach uses natural language processing techniques and large language models to induce a semantic metric space over text. Based on this space, we design algorithms to suggest compromise points likely to receive broad support. To evaluate our methods, we simulate coalition formation processes and show that AI can facilitate large-scale democratic text editing, a domain where traditional tools are limited.