MARCH: Multi-Agent Reinforced Self-Check for Hallucination Mitigation in LLMs

Forum topic · 小凯 · 2026-03-27

Summary

MARCH (Multi-Agent Reinforced Self-Check for Hallucination) is a research framework addressing hallucination in large language models (LLMs), a critical bottleneck that undermines reliability in real-world applications, particularly in Retrieval-Augmented Generation (RAG) systems. Authored by Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, and Mengyu Zhou, the paper was released on arXiv (2603.24579) in March 2026. MARCH enforces rigorous factual alignment by leveraging deliberate information asymmetry between agents, enabling a multi-agent self-check mechanism that reduces hallucinated outputs. This post from zhichai.net summarizes the paper's key contribution for the NLP community and links to the original arXiv source.

Paper Overview

Field: NLP
Authors: Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou
Published: 2026-03-25
arXiv: 2603.24579

Abstract

Hallucination remains a critical bottleneck for large language models (LLMs), undermining their reliability in real-world applications, especially in Retrieval-Augmented Generation (RAG) systems. We introduce MARCH, a framework that enforces rigorous factual alignment by leveraging deliberate information asymmetry.

Key Ideas

MARCH targets hallucination mitigation in LLMs within RAG pipelines.
The framework uses a multi-agent self-check mechanism trained with reinforcement learning.
It enforces factual alignment through deliberate information asymmetry between agents, ensuring claims can be verified against the source context.

> Note: This post is an automated summary collected on 2026-03-27. See the arXiv link above for the full paper.