Graphical-Probabilistic Modeling of Generative Flows in LLM-Native Software Systems

• URL: http://arxiv.org/abs/2606.15943v1
• Date: Sun, 14 Jun 2026 17:52:47 GMT
• Title: Graphical-Probabilistic Modeling of Generative Flows in LLM-Native Software Systems
• Abstract summary: We propose methods for documenting generative flows and for stating properties of LLM-based software designs.<n>Our initial approach is based on probabilistic models, tailored to capture phenomena characteristic of LLM-native systems.<n>This framework aims to provide a foundation for principled reasoning about generative interactions and system-level properties in LLM-centric software architectures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Engineering LLM-native software remains a challenging and immature field. Current practice is largely exploratory, relying on experimentation and heuristic techniques such as prompting and context engineering. These, however, are low-level and lack the principled structure needed to support design-level reasoning or analysis. In contrast, traditional software engineering leverages modularity and abstraction to communicate and analyze system behavior. To bring similar rigor to LLM-native development, we propose methods for documenting generative flows and for stating properties of LLM-based software designs. Such methods must account for the stochastic, prompt-dependent behavior of large language models while remaining expressive enough to capture emergent phenomena. Our initial approach is based on graphical probabilistic models, tailored to capture phenomena characteristic of LLM-native systems. This framework -- what we term Generation Networks -- aims to provide a foundation for principled reasoning about generative interactions and system-level properties in LLM-centric software architectures.

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