@inproceedings{li-etal-2026-march,
title = "{MARCH}: Multi-Agent Reinforced Check for Hallucination",
author = "Li, Zhuo and
Zhang, Yupeng and
Cheng, Pengyu and
Song, Jiajun and
Zhou, Mengyu and
Li, Hao and
Hu, Shujie and
Qin, Yu and
Erchao.zec and
Jiang, Xiaoxi and
Guanjunjiang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1828/",
pages = "39389--39415",
ISBN = "979-8-89176-390-6",
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. While existing hallucination detection methods employ LLM-as-a-judge to verify LLM outputs against retrieved evidence, they suffer from inherent *confirmation bias*, where the verifier inadvertently reproduces the errors of the original generation. To address this, we introduce **M**ulti-**A**gent **R**einforced self-**C**heck for **H**allucination (MARCH), a framework that enforces rigorous factual alignment by leveraging deliberate *information asymmetry*. MARCH orchestrates a collaborative pipeline of three specialized agents: a Solver, a Proposer, and a Checker. The Solver generates an initial RAG response, which the Proposer decomposes into claim-level verifiable atomic propositions. Crucially, the Checker validates these propositions against retrieved evidence in isolation, deprived of the Solver{'}s original output. This well-crafted information asymmetry scheme breaks the cycle of self-confirmation bias. By training this pipeline with multi-agent reinforcement learning (MARL), we enable the agents to co-evolve and optimize factual adherence. Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucination rates. Notably, an 8B-parameter LLM equipped with MARCH achieves performance competitive with powerful closed-source models. MARCH paves a scalable path for factual self-improvement of LLMs through co-evolution. The code is at https://github.com/Qwen-Applications/MARCH."
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<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. While existing hallucination detection methods employ LLM-as-a-judge to verify LLM outputs against retrieved evidence, they suffer from inherent *confirmation bias*, where the verifier inadvertently reproduces the errors of the original generation. To address this, we introduce **M**ulti-**A**gent **R**einforced self-**C**heck for **H**allucination (MARCH), a framework that enforces rigorous factual alignment by leveraging deliberate *information asymmetry*. MARCH orchestrates a collaborative pipeline of three specialized agents: a Solver, a Proposer, and a Checker. The Solver generates an initial RAG response, which the Proposer decomposes into claim-level verifiable atomic propositions. Crucially, the Checker validates these propositions against retrieved evidence in isolation, deprived of the Solver’s original output. This well-crafted information asymmetry scheme breaks the cycle of self-confirmation bias. By training this pipeline with multi-agent reinforcement learning (MARL), we enable the agents to co-evolve and optimize factual adherence. Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucination rates. Notably, an 8B-parameter LLM equipped with MARCH achieves performance competitive with powerful closed-source models. MARCH paves a scalable path for factual self-improvement of LLMs through co-evolution. The code is at https://github.com/Qwen-Applications/MARCH.</abstract>
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%0 Conference Proceedings
%T MARCH: Multi-Agent Reinforced Check for Hallucination
%A Li, Zhuo
%A Zhang, Yupeng
%A Cheng, Pengyu
%A Song, Jiajun
%A Zhou, Mengyu
%A Li, Hao
%A Hu, Shujie
%A Qin, Yu
%A Jiang, Xiaoxi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%A Erchao.zec
%A Guanjunjiang
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-etal-2026-march
%X Hallucination remains a critical bottleneck for large language models (LLMs), undermining their reliability in real-world applications, especially in Retrieval-Augmented Generation (RAG) systems. While existing hallucination detection methods employ LLM-as-a-judge to verify LLM outputs against retrieved evidence, they suffer from inherent *confirmation bias*, where the verifier inadvertently reproduces the errors of the original generation. To address this, we introduce **M**ulti-**A**gent **R**einforced self-**C**heck for **H**allucination (MARCH), a framework that enforces rigorous factual alignment by leveraging deliberate *information asymmetry*. MARCH orchestrates a collaborative pipeline of three specialized agents: a Solver, a Proposer, and a Checker. The Solver generates an initial RAG response, which the Proposer decomposes into claim-level verifiable atomic propositions. Crucially, the Checker validates these propositions against retrieved evidence in isolation, deprived of the Solver’s original output. This well-crafted information asymmetry scheme breaks the cycle of self-confirmation bias. By training this pipeline with multi-agent reinforcement learning (MARL), we enable the agents to co-evolve and optimize factual adherence. Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucination rates. Notably, an 8B-parameter LLM equipped with MARCH achieves performance competitive with powerful closed-source models. MARCH paves a scalable path for factual self-improvement of LLMs through co-evolution. The code is at https://github.com/Qwen-Applications/MARCH.
%U https://aclanthology.org/2026.acl-long.1828/
%P 39389-39415
Markdown (Informal)
[MARCH: Multi-Agent Reinforced Check for Hallucination](https://aclanthology.org/2026.acl-long.1828/) (Li et al., ACL 2026)
ACL
- Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie Hu, Yu Qin, Erchao.zec, Xiaoxi Jiang, and Guanjunjiang. 2026. MARCH: Multi-Agent Reinforced Check for Hallucination. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 39389–39415, San Diego, California, United States. Association for Computational Linguistics.