---
title: "Who cites FACT-AUDIT: An Adaptive — 11 citing papers"
description: "11 papers of the Elamur Research library cite «FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of…"
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---
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# Who cites «FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models»

11 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models»](https://md.research.elamur.ai/free/en/papers/lin-2025-fact-audit-adaptive-multi-agent/)

1. **[From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms](https://md.research.elamur.ai/free/en/papers/luo-2026-storage-experience-survey-evolution-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinghao Luo, Yuchen Tian, Chuxue Cao, et al.*2026 · 3 citations*
2. **[A Self-Reflective LLM-based Architecture for Semi-Open Event Extraction](https://md.research.elamur.ai/free/en/papers/tanev-2026-self-reflective-llm-based-architecture/) [Workshop on Event Extraction and Understanding: Challenges and Applications](https://md.research.elamur.ai/free/en/venues/eeuca/)**Hristo Tanev, Michel de Bollivier, Bertrand De Longueville*2026 · 1 citation*
3. **[Taming “Zombie” Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution](https://md.research.elamur.ai/free/en/papers/zhang-2026-taming-zombie-agents-markov-state/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Taolin Zhang, Pukun Zhao, Qizhou Chen, et al.*2026*

8 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/lin-2025-fact-audit-adaptive-multi-agent/) — FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models