15  References

15.1 Bibliography

[1]

INCOSE, INCOSE systems engineering handbook, 5th ed. Wiley, 2023.

[2]

Object Management Group, “OMG systems modeling language (SysML) v2.0 specification,” 2025. Available: https://www.omg.org/spec/SysML/2.0/

[3]

Object Management Group, “OMG kernel modeling language (KerML) 1.0 specification,” 2025. Available: https://www.omg.org/spec/KerML/1.0/

[4]

Object Management Group, “OMG systems modeling API and services 1.0,” 2025. Available: https://www.omg.org/spec/SystemsModelingAPI/1.0/

[5]

Anthropic, “Model context protocol specification.” 2024. Available: https://spec.modelcontextprotocol.io/

[6]

D. Horthy, “12-factor agents: Principles for building reliable LLM applications.” https://github.com/humanlayer/12-factor-agents, 2025.

[7]

Anthropic, “Building effective agents.” https://www.anthropic.com/research/building-effective-agents, 2024.

[8]

Y. Bouamra, B. Yun, A. Poisson, and F. Armetta, “SysTemp: A Multi-Agent System for Template-Based Generation of SysML v2.” 2025. Available: https://arxiv.org/abs/2506.21608

[9]

D. Jin, Z. Jin, L. Li, Z. Fang, J. Li, and X. Chen, “A System Model Generation Benchmark from Natural Language Requirements.” 2025. Available: https://arxiv.org/abs/2508.03215

[10]

S. Massey, “A discussion on accelerating hardware engineering through agile practices.” 2025. Available: https://resources.sysgit.io/a-discussion-on-accelerating-hardware-engineering-through-agile-practices/

[11]

Z. Li, S. Husung, and H. Wang, “LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2,” in 2025 IEEE international symposium on systems engineering (ISSE), 2025, pp. 1–8. doi: 10.1109/ISSE65546.2025.11369983.

[12]

M. A. Hendricks and A. Cicirello, “Text to Model via SysML: Automated Generation of Dynamical System Computational Models from Unstructured Natural Language Text via Enhanced System Modeling Language Diagrams,” arXiv preprint, 2025, Available: https://arxiv.org/abs/2507.06803

[13]

P. Darm, J. Xie, and A. Riccardi, “Inference-Time Intervention in Large Language Models for Reliable Requirement Verification,” arXiv preprint, 2025, Available: https://arxiv.org/abs/2503.14130

[14]

B. Jerry, L. Moreno, V. Francisco, and R. Hervas, “LLM-Driven Accessible Interface: A Model-Based Approach,” arXiv preprint, 2026, Available: https://arxiv.org/abs/2601.06616

[15]

S. Otten et al., “Generative AI in Systems Engineering: A Framework for Risk Assessment of LLMs,” arXiv preprint, 2026, Available: https://arxiv.org/abs/2602.04358

[16]

P. Giannouris and S. Ananiadou, “NOMAD: Multi-Agent LLM System for UML Class Diagram Generation from Natural Language Requirements,” arXiv preprint, 2025, Available: https://arxiv.org/abs/2511.22409

[17]

A. Ferrari, S. Abualhaija, and C. Arora, “Model Generation with LLMs: From Requirements to UML Sequence Diagrams,” arXiv preprint, 2024, Available: https://arxiv.org/abs/2404.06371

[18]

D. Rouabhia and I. Hadjadj, “Behavioral Augmentation of UML Class Diagrams: LLMs for Method Generation,” arXiv preprint, 2025, Available: https://arxiv.org/abs/2506.00788

[19]

W. Mao et al., “Data Dependency-Aware Code Generation from Enhanced UML Sequence Diagrams,” arXiv preprint, 2025, Available: https://arxiv.org/abs/2508.03379

[20]

A. Trendowicz et al., “DeepQuali: LLMs for Assessing Quality of User Stories,” arXiv preprint, 2026, Available: https://arxiv.org/abs/2602.08887

[21]

E. Bader, D. Vereno, and C. Neureiter, “Facilitating User-Centric Model-Based Systems Engineering Using Generative AI,” in Proceedings of the 12th international conference on model-based software and systems engineering (MODELSWARD 2024), SCITEPRESS, 2024.

[22]

E. S. Crabb and M. T. Jones, “Accelerating Model-Based Systems Engineering by Harnessing Generative AI,” in 2024 19th annual system of systems engineering conference (SoSE), IEEE, 2024.

[23]

S. O. Erikstad, “Multi-Agent LLMs and MBSE for Developing Design Optimization Models,” 2024, Available: https://www.researchgate.net/publication/380882908

[24]

S. Neema et al., “On the Evaluation of Engineering Artificial General Intelligence,” arXiv preprint, 2025, Available: https://arxiv.org/abs/2505.10653

[25]

D. Horthy, “Advanced context engineering for coding agents.” Accessed: Feb. 12, 2026. [Online]. Available: https://humanlayer.dev/blog/advanced-context-engineering

[26]

Y. Ji, “Context engineering for AI agents: Lessons from building manus.” Accessed: Feb. 12, 2026. [Online]. Available: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus

[27]

S. Rodda, Mcp-proxy: Aggregating MCP proxy with progressive tool disclosure. (2025). Available: https://github.com/IAMSamuelRodda/mcp-proxy

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C. Luddy, Xc-mcp: XCode CLI MCP server with progressive disclosure. (2025). Available: https://github.com/conorluddy/xc-mcp

[29]

Microsoft Research, LLMLingua: Prompt compression for LLMs. (2024). Available: https://github.com/microsoft/LLMLingua

[30]

Volcengine, OpenViking: Context database for AI agents. (2025). Available: https://github.com/volcengine/OpenViking

[31]

Anthropic, “How we built our multi-agent research system.” Accessed: Feb. 12, 2026. [Online]. Available: https://www.anthropic.com/engineering/multi-agent-research-system

[32]

R. Lopopolo, “Harness engineering: Leveraging Codex in an agent-first world.” Accessed: Feb. 17, 2026. [Online]. Available: https://openai.com/index/harness-engineering/