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

arXiv: https://arxiv.org/abs/2606.15943 · HTML: https://mlsanity.com/paper/2606.15943/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 14 Jun 2026.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning](https://mlsanity.com/paper/2507.19457/) (2025)
- [Causal Abstractions of Neural Networks](https://mlsanity.com/paper/2106.02997/) (2021)
- [Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning](https://mlsanity.com/paper/2205.09712/) (2022)
- [Out-of-Distribution Detection and Selective Generation for Conditional Language Models](https://mlsanity.com/paper/2209.15558/) (2022)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [Abstracting Causal Models](https://mlsanity.com/paper/1812.03789/) (2018)
- [On Decoding Strategies for Neural Text Generators](https://mlsanity.com/paper/2203.15721/) (2022)
- [Automatic Root Cause Analysis via Large Language Models for Cloud Incidents](https://mlsanity.com/paper/2305.15778/) (2023)
- [Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts](https://mlsanity.com/paper/2409.12447/) (2024)
- [SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation](https://mlsanity.com/paper/2005.09296/) (2020)
- [Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text](https://mlsanity.com/paper/2211.11300/) (2022)
- [RBT4DNN: Requirements-based Testing of Neural Networks](https://mlsanity.com/paper/2504.02737/) (2025)
- [Towards a Probabilistic Framework for Analyzing and Improving LLM-Enabled Software](https://mlsanity.com/paper/2501.06370/) (2025)

## Abstract (arXiv)

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.