---
title: "JURIS: Bringing the Jury System to Multi-Agent Summarization"
authors:
  - "Ziling Li"
  - "Junwei Zhang"
  - "Yixuan Yang"
  - "Yuqiang Han"
  - "Xiaolin Li"
doi: "10.24963/ijcai.2026/23"
url: "https://ijcai.org/proceedings/2026/23"
pdf: "https://ijcai.org/proceedings/2026/0023.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "199-207"
track: "Main Track"
keywords:
  - "Agent-based and Multi-agent Systems: Other"
  - "Agent-based and Multi-agent Systems: Normative systems"
  - "Agent-based and Multi-agent Systems: Multi-agent planning"
---

# JURIS: Bringing the Jury System to Multi-Agent Summarization

Ziling Li, Junwei Zhang, Yixuan Yang, Yuqiang Han, Xiaolin Li

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 199-207. DOI: [10.24963/ijcai.2026/23](https://doi.org/10.24963/ijcai.2026/23)

## Abstract

Agents implemented using Large Language Models (LLMs) offer novel solutions for tasks such as document summarization. However, single-agent systems, limited by their own knowledge base, often exhibit information omissions or inconsistencies with the facts, while multi-agent systems lacking effective organization can fall into the trap of social cognitive defects such as flattery and premature consensus. Inspired by the U.S. jury system, we propose JURIS, a judicial-decision-inspired multi-agent collaborative framework. Specifically, the document summarization process simulates a lawsuit scenario: multiple generator agents condense the target text into multiple sentences from different perspectives, much like lawyers on both sides providing different evidence; multiple decision-making agents consider the overall picture based on defense and voting mechanisms to select candidate sentences, much like a jury adopting different evidence; finally, a chief editor agent compiles a summary of all selected sentences, much like a secretary summarizing a plan and a judge giving a final verdict. Extensive experiments on in-domain news and cross-domain benchmarks demonstrate that JURIS consistently and significantly outperforms strong single-agent and multi-agent baselines across automatic evaluation metrics, multi-dimensional quality assessments, and human judgments, validating the effectiveness of decision-driven structured multi-agent collaboration for document summarization.