🔗 Who cites «Why Do Multi-Agent LLM Systems Fail?»

71 papers of our library cite it (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · ← back to «Why Do Multi-Agent LLM Systems Fail?»

1. UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated SupervisionAnnual Meeting of the Association for Computational Linguistics (ACL)Zhen Fang, Ruiyan Han, XinYu Sun, et al.2026 · 11 citations
2. Mem²Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience DistillationAnnual Meeting of the Association for Computational Linguistics (ACL)Zihao Cheng, Zeming Liu, Yingyu Shan, et al.2026 · 5 citations
3. BILLY: Steering Large Language Models via Merging Persona Vectors for Creative GenerationConference of the European Chapter of the Association for Computational Linguistics (EACL)Tsung-Min Pai, Jui-I Wang, Li-Chun Lu, et al.2026 · 4 citations
4. J4R: Learning to Judge with Equivalent Initial State Group Relative Policy OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Austin Xu, Yilun Zhou, Xuan-Phi Nguyen, et al.2026 · 3 citations
5. Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution ReconstructionFindings of the Association for Computational LinguisticsXu Shen, Qi Zhang, Song Wang, et al.2026 · 3 citations
6. ComfyUI-R1: Exploring Reasoning Models for Workflow GenerationFindings of the Association for Computational LinguisticsZhenran Xu, Yiyu Wang, Xue Yang, et al.2026 · 1 citation
7. Fine-Grained Detection of Context-Grounded Hallucinations Using LLMsFindings of the Association for Computational LinguisticsYehonatan Peisakhovsky, Zorik Gekhman, Yosi Mass, et al.2026 · 1 citation
8. From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language ModelsFindings of the Association for Computational LinguisticsJiaxin Zhang, Wendi Cui, Zhuohang Li, et al.2026 · 1 citation
9. RAFFLES: Reasoning-based Attribution of Faults for LLM SystemsConference of the European Chapter of the Association for Computational Linguistics (EACL)Chenyang Zhu, Spencer Hong, Jingyu Wu, et al.2026 · 1 citation
10. TAGS: A Test-Time Generalist–Specialist Framework with Retrieval-Augmented Reasoning and VerificationFindings of the Association for Computational LinguisticsJianghao Wu, Feilong Tang, Yulong Li, et al.2026 · 1 citation
11. Verification-Aware Planning for Multi-Agent SystemsConference of the European Chapter of the Association for Computational Linguistics (EACL)Tianyang Xu, Dan Zhang, Kushan Mitra, et al.2026 · 1 citation
12. Action Boundary Blindness: When LLM Agents Cannot Tell Where One Action Ends and Another BeginsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhangyi Wang, Bingnan Yu, Jiexiang Xu, et al.2026
13. Agentic CLEAR: Automating Multi-Level Evaluation of LLM AgentsAnnual Meeting of the Association for Computational Linguistics (ACL)Asaf Yehudai, Lilach Eden, Michal Shmueli-Scheuer2026
14. BayesFlow: A Probability Inference Framework for Meta-Agent Assisted Workflow GenerationFindings of the Association for Computational LinguisticsBo Yuan, Yun Zhou, Zhichao Xu, et al.2026
15. CIA: Inferring the Communication Topology from LLM-based Multi-Agent SystemsAnnual Meeting of the Association for Computational Linguistics (ACL)Yongxuan Wu, Xixun Lin, He Zhang, et al.2026
16. FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use EnvironmentsFindings of the Association for Computational LinguisticsAmir Saeidi, Venkatesh Mishra, Souradeep Mukhopadhyay, et al.2026
17. LUMINA: Long-horizon Understanding for Multi-turn Interactive AgentsFindings of the Association for Computational LinguisticsAmin Rakhsha, Thomas Hehn, Pietro Mazzaglia, et al.2026
18. Superficial Success vs. Internal Breakdown: An Empirical Study of Generalization in Adaptive Multi-Agent SystemsFindings of the Association for Computational LinguisticsNamyeong So, Seokgyu Jang, Taeuk Kim2026
19. Taming “Zombie” Agents: A Markov State-Aware Framework for Resilient Multi-Agent EvolutionAnnual Meeting of the Association for Computational Linguistics (ACL)Taolin Zhang, Pukun Zhao, Qizhou Chen, et al.2026
20. Thesis Proposal: When Does an Agent Know It Is Lost? Confidence Trajectory Analysis for Tool-Using LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhenjiang Mao2026

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

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