---
title: "Who cites Mixture-of-Agents Enhances — 90 citing papers"
description: "90 papers of the Elamur Research library cite «Mixture-of-Agents Enhances Large Language Model Capabilities», found in their…"
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---
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# Who cites «Mixture-of-Agents Enhances Large Language Model Capabilities»

90 papers of our library cite it (of the 149,439 whose bibliographies we read) · 26 with a page here, the most cited first · [← back to «Mixture-of-Agents Enhances Large Language Model Capabilities»](https://md.research.elamur.ai/free/en/papers/wang-2024-mixture-agents-enhances-large/)

1. [**Large Language Monkeys: Scaling Inference Compute with Repeated Sampling arXiv:2407.21787**Bradley Brown, Jordan Juravsky, Ryan Ehrlich, et al.*2024 · 270 citations*](https://md.research.elamur.ai/free/en/papers/brown-2024-large-language-monkeys-scaling-inference/)
2. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
3. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/)
4. **[AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration](https://md.research.elamur.ai/free/en/papers/wang-2025-agentdropout-dynamic-agent-elimination-token/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhexuan Wang, Yutong Wang, Xuebo Liu, et al.*2025 · 29 citations*
5. **[MemInsight: Autonomous Memory Augmentation for LLM Agents](https://md.research.elamur.ai/free/en/papers/salama-2025-meminsight-autonomous-memory-augmentation-llm/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rana Salama, Jason Cai, Michelle Yuan, et al.*2025 · 22 citations*
6. **[Nature-Inspired Population-Based Evolution of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-nature-inspired-population-based-evolution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiqun Zhang, Peng Ye, Xiaocui Yang, et al.*2026 · 8 citations*
7. **[EmoMAS: Emotion-Aware Multi-Agent System for High-Stakes Edge-Deployable Negotiation with Bayesian Orchestration](https://md.research.elamur.ai/free/en/papers/long-2026-emomas-emotion-aware-multi-agent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yunbo Long, Yuhan Liu, Liming Xu*2026 · 6 citations*
8. **[PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning](https://md.research.elamur.ai/free/en/papers/hu-2026-pacore-learning-scale-test-time/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingcheng Hu, Yinmin Zhang, Shijie Shang, et al.*2026 · 5 citations*
9. **[MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inference](https://md.research.elamur.ai/free/en/papers/zhang-2026-mapro-recasting-multi-agent-prompt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zheyuan Zhang, Lin Ge, Hongjiang Li, et al.*2026 · 3 citations*
10. **[Multi-LLM Collaborative Search for Complex Problem Solving](https://md.research.elamur.ai/free/en/papers/yang-2026-multi-llm-collaborative-search-complex/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sen Yang, Yafu Li, Wai Lam, et al.*2026 · 3 citations*
11. **[Self-Evolving Multi-Agent Systems via Textual Backpropagation](https://md.research.elamur.ai/free/en/papers/ma-2026-self-evolving-multi-agent-systems/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaowen Ma, Yunpu Ma, Chenyang Lin, et al.*2026 · 3 citations*
12. **[Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search](https://md.research.elamur.ai/free/en/papers/shi-2026-efficient-multi-agent-system-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wentao Shi, Zichun Yu, Fuli Feng, et al.*2026 · 2 citations*
13. **[Harnessing Consistency for Robust Test-Time LLM Ensemble](https://md.research.elamur.ai/free/en/papers/zeng-2026-harnessing-consistency-robust-test-time/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhichen Zeng, Qi Yu, Xiao Lin, et al.*2026 · 2 citations*
14. **[SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems](https://md.research.elamur.ai/free/en/papers/zhang-2026-silo-bench-scalable-environment-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuzhe Zhang, Feiran Liu, Yi Shan, et al.*2026 · 2 citations*
15. **[Building LLMs Like LEGO: Two-dimensional Architecture Reassembly of Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2026-building-llms-like-lego-two/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xingyu Wu, Yu Zhou, KC Tan*2026 · 1 citation*
16. **[ComfyUI-R1: Exploring Reasoning Models for Workflow Generation](https://md.research.elamur.ai/free/en/papers/xu-2026-comfyui-r1-exploring-reasoning-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhenran Xu, Yiyu Wang, Xue Yang, et al.*2026 · 1 citation*
17. **[A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement](https://md.research.elamur.ai/free/en/papers/tang-2026-scalable-multi-llm-collaboration-system/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shengji Tang, Jianjian Cao, Weihao Lin, et al.*2026*
18. **[Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding](https://md.research.elamur.ai/free/en/papers/yun-2026-distilling-long-cot-reasoning-through/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Taewon Yun, Jisu Shin, Jeonghwan Choi, et al.*2026*
19. **[Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design](https://md.research.elamur.ai/free/en/papers/zhou-2026-identifying-collective-intelligence-factor-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhilun Zhou, Zihan Liu, Jiahe Liu, et al.*2026*
20. **[Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-learning-refine-self-refinement-parallel/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qibin Wang, Pu Zhao, Shaohan Huang, et al.*2026*
21. **[Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling](https://md.research.elamur.ai/free/en/papers/wunderlich-2026-multi-agent-reasoning-improves-compute/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Florian Valentin Wunderlich, Lars Benedikt Kaesberg, Jan Philip Wahle, et al.*2026*
22. **[Multilingual KokoroChat: A Multi-LLM Ensemble Translation Method for Creating a Multilingual Counseling Dialogue Dataset](https://md.research.elamur.ai/free/en/papers/suzuki-2026-multilingual-kokorochat-multi-llm-ensemble/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ryoma Suzuki, Zhiyang Qi, Michimasa Inaba*2026*
23. **[NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering](https://md.research.elamur.ai/free/en/papers/shi-2026-ng-router-graph-supervised-multi/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Kaiwen Shi, Zheyuan Zhang, Zhengqing Yuan, et al.*2026*
24. **[PropGenie: A Multi-Agent Conversational Framework for Real Estate Assistance](https://md.research.elamur.ai/free/en/papers/shen-2026-propgenie-multi-agent-conversational-framework/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Chang Shen, Shaozu Yuan, Kuizong Wu, et al.*2026*
25. **[RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents](https://md.research.elamur.ai/free/en/papers/wang-2026-routemoa-dynamic-routing-without-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jize Wang, Han Wu, Zhiyuan You, et al.*2026*
26. **[Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection](https://md.research.elamur.ai/free/en/papers/niu-2026-routing-generated-data-annotation-free/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyi Niu, Justin Chen, Genta Indra Winata, et al.*2026*

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

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