# Agentic Generation and Evolution of Knowledge Models

arXiv: https://arxiv.org/abs/2606.03662 · HTML: https://mlsanity.com/paper/2606.03662/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 8 of 8)

- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [EvoSpex: An Evolutionary Algorithm for Learning Postconditions](https://mlsanity.com/paper/2102.13569/) (2021)
- [Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach](https://mlsanity.com/paper/2311.07377/) (2023)
- [Model-Driven Development of Complex Software: A Research Roadmap](https://mlsanity.com/paper/1409.6620/) (2014)
- [Leveraging LLMs to support co-evolution between definitions and instances of textual DSLs](https://mlsanity.com/paper/2512.06836/) (2025)
- [GAssert: A Fully Automated Tool to Improve Assertion Oracles](https://mlsanity.com/paper/2103.02901/) (2021)
- [Evolve the Model Universe of a System Universe](https://mlsanity.com/paper/2309.13342/) (2023)
- [Software Engineering for Collective Cyber-Physical Ecosystems](https://mlsanity.com/paper/2406.04780/) (2024)

## Abstract (arXiv)

Complex software systems such as autonomous vehicles, robotics increasingly interact with dynamic physical, cyber, and social environments. Reasoning about their behavior, maintaining them under continuous change, and evolving them safely require trustworthy knowledge about the system, its assumptions, and its operating context. Knowledge models (KMs) provide a practical basis for such reasoning, but they may themselves become incomplete, inconsistent, or outdated as systems evolve. This paper presents TrustModel, a vision for the agentic generation and evolution of living KMs. TrustModel comprises three agentic subsystems: Modeling, for constructing and updating KMs; Conformance, for assessing their alignment with the system and its environment; and Evolution, for generating guidance to keep KMs synchronized with emerging changes. We demonstrate how TrustModel can be instantiated for model-based testing and discuss its potential for supporting other MDE activities, such as requirements and assumption monitoring, architectural drift tracking, and change impact assessment. Overall, TrustModel positions living KMs as a foundation for dependable engineering of continuously evolving software systems.