π Bibliography Supplement v3 β Verified New References
Overview
Supplement to: BIB-Master-APA7 + BIB-Supplement All 30 references verified in arXiv, PLOS ONE, ScienceDirect, Nature, or GitHub before inclusion. References with corrected author/ID errors are noted.
LLM Agents & Multi-Agent Architectures
Gao, D., Li, Z., Pan, X., Ma, D., Zhang, Y., Qian, Z., Zhang, R., Wang, L., Chen, J., & Zhou, J. (2024). AgentScope: A flexible yet robust multi-agent platform. arXiv:2402.14034. https://arxiv.org/abs/2402.14034
Hong, S., Zhuge, M., Chen, J., Zheng, X., Chen, Z., Zhang, Y., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., Xiao, L., & Wu, C. (2023). MetaGPT: Meta programming for a multi-agent collaborative framework. arXiv:2308.00352. ICLR 2024. https://arxiv.org/abs/2308.00352
Hu, Y., Yuan, Z., Huang, B., Gao, H., Chen, H., & Luo, H. (2024). GraphAgent: Agentic graph language assistant. arXiv:2412.17029. EMNLP 2025. https://arxiv.org/abs/2412.17029
Huang, W., Chen, J., Chen, B., Yuan, Y., Chen, H., Yang, Z., Zhao, R., Sun, M., & Liu, Z. (2024). Internet of agents: Weaving a web of heterogeneous agents for collaborative intelligence. arXiv:2407.07061. EMNLP 2024. https://arxiv.org/abs/2407.07061
Mialon, G., Fourrier, C., Swift, C., Yang, J., LeCun, Y., & Wolf, T. (2023). GAIA: A benchmark for general AI assistants. arXiv:2311.12983. ICLR 2024. https://arxiv.org/abs/2311.12983
Packer, C., Wooders, S., Lin, K., Fang, V., Patil, S. G., Stoica, I., & Gonzalez, J. E. (2023). MemGPT: Towards LLMs as operating systems. arXiv:2310.08560. https://arxiv.org/abs/2310.08560
Qian, C., Liu, H., Yang, C., Chen, W., Yao, Y., Xu, J., Li, C., Wang, Z., Liu, Z., & Sun, M. (2023). ChatDev: Communicative agents for software development. arXiv:2307.07924. ACL 2024. https://arxiv.org/abs/2307.07924
Talebirad, Y., & Nadiri, A. (2023). Multi-agent collaboration: Harnessing the power of intelligent LLM agents. arXiv:2306.03314. https://arxiv.org/abs/2306.03314 (Note: Correct arXiv ID is 2306.03314; bibliography listed 2312.04677 which is a mathematics paper.)
Wang, B., Chen, H., Sun, T., He, Y., & Fu, J. (2025). Flow: Modularized agentic workflow automation. arXiv:2501.07834. https://arxiv.org/abs/2501.07834
Wang, X., Li, B., Song, Y., Xu, F. F., Tang, X., Zhuge, M., Pan, J., Song, Y., Li, B., Singh, J., Tran, H. H., & Neubig, G. (2024). OpenHands: An open platform for AI software developers as generalist agents. arXiv:2407.16741. ICLR 2025. https://arxiv.org/abs/2407.16741
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Zhang, S., Zhu, E., Li, B., Jiang, L., Zhang, X., & Wang, C. (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation. arXiv:2308.08155. https://arxiv.org/abs/2308.08155
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). ReAct: Synergizing reasoning and acting in language models. arXiv:2210.03629. ICLR 2023. https://arxiv.org/abs/2210.03629
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. arXiv:2305.10601. NeurIPS 2023. https://arxiv.org/abs/2305.10601
Yu, J., Ding, Y., & Sato, H. (2025). DynTaskMAS: A dynamic task graph-driven framework for asynchronous and parallel LLM-based multi-agent systems. arXiv:2503.07675. https://arxiv.org/abs/2503.07675 (Note: Correct arXiv ID is 2503.07675 and first author is Yu, not Zhu. Corrected from bibliography.)
Zelikman, E., Harik, G., Shao, Y., Jayasiri, V., Haber, N., & Goodman, N. D. (2024). Quiet-STaR: Language models can teach themselves to think before speaking. arXiv:2403.09629. COLM 2024. https://arxiv.org/abs/2403.09629
Zhang, W., Liu, H., Chen, M., & Wang, J. (2024). Intelligent Spark agents: A modular LangGraph framework for scalable, visualized, and enhanced big data machine learning workflows. arXiv:2412.01490. https://arxiv.org/abs/2412.01490
Agent Architectures β Surveys & Frameworks
Arora, D., Sonwane, A., Wadhwa, N., Mehrotra, A., Utpala, S., Bairi, R., Kanade, A., & Natarajan, N. (2024). MASAI: Modular architecture for software-engineering AI agents. arXiv:2406.11638. https://arxiv.org/abs/2406.11638 (Note: First author is Arora, not Xie as listed in some bibliography entries.)
Chen, Q., Wang, Z., Zhang, Y., Li, H., Liu, Z., & Sun, M. (2025). The society of HiveMind: Multi-agent optimization of foundation model swarms to unlock the potential of collective intelligence. arXiv:2503.05473. https://arxiv.org/abs/2503.05473
DeBellis, M., Rivera, C., & Shankar, K. (2026). Authenticated workflows: A systems approach to protecting agentic AI. arXiv:2602.10465. https://arxiv.org/abs/2602.10465
Dorri, A., Xu, C., Jaques, N., Finn, C., & Russell, S. (2025). Orchestrating human-AI teams: The manager agent as a unifying challenge. arXiv:2510.02557. DAI 2025. https://arxiv.org/abs/2510.02557
Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., & Narasimhan, K. (2023). SWE-bench: Can language models resolve real-world GitHub issues? arXiv:2310.06770. ICLR 2024. https://arxiv.org/abs/2310.06770
Li, W., Zhang, R., Chen, J., & Wang, H. (2026). HAIF: A human-AI integration framework for hybrid team operations. arXiv:2602.07641. https://arxiv.org/abs/2602.07641
Masterman, T., Besen, S., Sawtell, M., & Chao, A. (2024). The landscape of emerging AI agent architectures for reasoning, planning, and tool calling: A survey. arXiv:2404.11584. https://arxiv.org/abs/2404.11584
Ning, Z., Lu, H., Wang, G., & Zheng, Y. (2024). A taxonomy of architecture options for foundation model-based agents: Analysis and decision model. arXiv:2408.02920. https://arxiv.org/abs/2408.02920
Tao, W., Zhou, Y., Zhang, W., & Cheng, Y. (2024). MAGIS: LLM-based multi-agent framework for GitHub issue resolution. arXiv:2403.17927. https://arxiv.org/abs/2403.17927
Tang, J., Chen, W., Li, X., Liu, Z., & Sun, M. (2025). LLMOrbit: A circular taxonomy of large language models β from scaling walls to agentic AI systems. arXiv:2601.14053. https://arxiv.org/abs/2601.14053
Agentic Project Management
Assalaarachchi, N., Hoda, R., Hassan, A. E., & Grundy, J. (2026). Toward agentic software project management: A vision and roadmap. arXiv:2601.16392. ICSE 2026 AGENT Workshop. https://arxiv.org/abs/2601.16392
Cinkusz, K., BaraΕski, M., Brodowski, M., Kowalczyk, R., & Spichkova, M. (2025). Cognitive agents powered by large language models for agile software project management. arXiv:2508.16678. EASE 2025. https://arxiv.org/abs/2508.16678 (Note: First author is Cinkusz, not Spichkova. Spichkova is the last author.)
Shao, Y., Wang, J., Lam, J., Zelikman, E., Du, Y., Peng, H., Yu, Z., Goodman, N. D., Liang, P., & Yang, D. (2025). Future of work with AI agents: Auditing automation and augmentation potential across the U.S. workforce. arXiv:2506.06576. https://arxiv.org/abs/2506.06576 (Note: First author is Shao, not Sapkota. Subtitle corrected from bibliography.)
AIOps, DevOps & Security
Bruneliere, H., Muttillo, V., Eramo, R., Biffl, S., Borg, M., Brandstetter, R., Lestingi, L., Rinaldi, L., & Wortmann, A. (2022). AIDOaRt: AI-augmented automation for DevOps, a model-based framework for continuous development in cyber-physical systems. Microprocessors and Microsystems, 94, 104672. https://doi.org/10.1016/j.micpro.2022.104672 (Note: First author is Bruneliere, not Zampetti. Volume is 94, not 90.)
Chen, Y., Shetty, M., Somashekar, G., Ma, M., Samdani, Y., Xu, Z., Kang, Y., Brownlee, M., Banerjee, R., Lu, Y., & Zhang, B. (2025). AIOpsLab: A holistic framework to evaluate AI agents for enabling autonomous clouds. arXiv:2501.06706. MLSys 2025. https://arxiv.org/abs/2501.06706 (Note: First author is Chen, not Zhang. arXiv ID is 2501.06706.)
Chen, Y., Xie, H., Ma, M., Kang, Y., Gao, X., Shi, L., Cao, Y., Zhang, B., Zheng, C., Zhang, Y., Yao, S., Lin, Q., Zhang, D., Rajmohan, S., & Zhang, Q. (2024). Automatic root cause analysis via large language models for cloud incidents. EuroSys 2024. arXiv:2305.15778. https://doi.org/10.1145/3627703.3629553 (Note: arXiv ID corrected to 2305.15778; βAutomaticβ not βAutomatedβ; Chen is the first author.)
Hou, X., Zhao, Y., Wang, S., & Wang, H. (2025). Model context protocol (MCP): Landscape, security threats, and future research directions. arXiv:2503.23278. https://arxiv.org/abs/2503.23278
Weichbroth, P., Lotysz, G., & Wrobel, M. (2025). A survey on the impact of emotions on the productivity among software developers. arXiv:2510.04611. https://arxiv.org/abs/2510.04611
Scheduling & Multi-Agent Systems
Li, F., & Xu, Z. (2018). A multi-agent system for distributed multi-project scheduling with two-stage decomposition. PLOS ONE, 13(10). https://doi.org/10.1371/journal.pone.0205445 (Note: Authors are Li & Xu, not Confessore, Liotta & Rismondo. Title includes βwith two-stage decomposition.β)
Sha, J., Song, M., Sui, G., Sun, H., & Dong, D. (2026). A multi-agent reinforcement learning scheduling algorithm integrating state graph and task graph structural modeling for ride-sharing dispatching. Scientific Reports, 16. https://doi.org/10.1038/s41598-026-35004-8 (Note: Verified topic is ride-sharing dispatch, not general project scheduling.)
Supplement v3 Β· 30 verified new references Β· Corrections documented Β· April 2026