--- title: "Who cites Chain of Agents: Large Language — 62 citing papers" description: "62 papers of the Elamur Research library cite «Chain of Agents: Large Language Models Collaborating on Long-Context Tasks», found in their…" canonical: "https://research.elamur.ai/free/en/papers/zhang-2024-chain-agents-large-language-models/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhang-2024-chain-agents-large-language-models/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Chain of Agents: Large Language Models…](https://md.research.elamur.ai/free/en/papers/zhang-2024-chain-agents-large-language-models/) / cited by # Who cites «Chain of Agents: Large Language Models Collaborating on Long-Context Tasks» 62 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «Chain of Agents: Large Language Models Collaborating on Long-Context Tasks»](https://md.research.elamur.ai/free/en/papers/zhang-2024-chain-agents-large-language-models/) 1. [**A Review of Large Language Models and Autonomous Agents in Chemistry arXiv:2407.01603**Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White*2024 · 288 citations*](https://md.research.elamur.ai/free/en/papers/ramos-2024-review-large-language-models-autonomous/) 2. **[CompAct: Compressing Retrieved Documents Actively for Question Answering](https://md.research.elamur.ai/free/en/papers/yoon-2024-compact-compressing-retrieved-documents-actively/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chanwoong Yoon, Taewhoo Lee, Hyeon Hwang, et al.*2024 · 34 citations* 3. **[Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems](https://md.research.elamur.ai/free/en/papers/shen-2025-understanding-information-propagation-effects-communication/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xu Shen, Yixin Liu, Yiwei Dai, et al.*2025 · 15 citations* 4. **[ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA](https://md.research.elamur.ai/free/en/papers/xinjie-2025-reagent-reversible-multi-agent-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhao Xinjie, Fan Gao, Xingyu Song, et al.*2025 · 8 citations* 5. **[KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation](https://md.research.elamur.ai/free/en/papers/tatarinov-2026-kg-mulqa-framework-kg-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nikita Tatarinov, Vidhyakshaya Kannan, Haricharana Srinivasa, et al.*2026 · 1 citation* 6. **[Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration](https://md.research.elamur.ai/free/en/papers/liu-2026-scaling-external-knowledge-input-beyond/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zijun Liu, Zhennan Wan, Peng Li, et al.*2026 · 1 citation* 7. **[Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-agent-gwo-collaborative-agents-dynamic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xudong Wang, Chaoning Zhang, Chenghao Li, et al.*2026* 8. **[CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures](https://md.research.elamur.ai/free/en/papers/pandey-2026-core-measuring-multi-agent-llm/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Punya Syon Pandey, Yongjin Yang, Jiarui Liu, et al.*2026* 9. **[NeuralFSM: Adaptive Multi-Agent Coordination via Learning Finite-State Execution Policy](https://md.research.elamur.ai/free/en/papers/wang-2026-neuralfsm-adaptive-multi-agent-coordination/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiye Wang, Yu Wang, Jianbin Li, et al.*2026* 10. **[PACE: Predictive Adaptive Context Extraction for Long-Horizon LLM Agents](https://md.research.elamur.ai/free/en/papers/wei-2026-pace-predictive-adaptive-context-extraction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lei Wei, Xiao Peng, Tt, et al.*2026* 11. **[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* 12. **[TeachMaster: Generative Teaching via Code](https://md.research.elamur.ai/free/en/papers/wang-2026-teachmaster-generative-teaching-code/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuheng Wang, Runde Yang, Lin Wu, et al.*2026* 13. **[ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts](https://md.research.elamur.ai/free/en/papers/olabisi-2026-threadsumm-summarization-nested-discourse-threads/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Olubusayo Olabisi, Ekata Mitra, Ameeta Agrawal*2026* 49 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zhang-2024-chain-agents-large-language-models/) — Chain of Agents: Large Language Models Collaborating on Long-Context Tasks