# Continual Enterprise World Model Discovery in Dynamic Systems

arXiv: https://arxiv.org/abs/2609.19551 · HTML: https://mlsanity.com/paper/2609.19551/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 13 of 13)

- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?](https://mlsanity.com/paper/2403.07718/) (2024)
- [Memento: Fine-tuning LLM Agents without Fine-tuning LLMs](https://mlsanity.com/paper/2508.16153/) (2025)
- [CWM: An Open-Weights LLM for Research on Code Generation with World Models](https://mlsanity.com/paper/2510.02387/) (2025)
- [MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems](https://mlsanity.com/paper/2510.17281/) (2025)
- [On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models](https://mlsanity.com/paper/1511.09249/) (2015)
- [STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?](https://mlsanity.com/paper/2605.06527/) (2026)
- [EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings](https://mlsanity.com/paper/2603.13594/) (2026)
- [GTM: Simulating the World of Tools for AI Agents](https://mlsanity.com/paper/2512.04535/) (2025)
- [World of Workflows: A Benchmark for Bringing World Models to Enterprise Systems](https://mlsanity.com/paper/2601.22130/) (2026)
- [When Continual Learning Moves to Memory: A Study of Experience Reuse in LLM Agents](https://mlsanity.com/paper/2604.27003/) (2026)
- [EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments](https://mlsanity.com/paper/2606.13681/) (2026)
- [Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics](https://mlsanity.com/paper/2605.12178/) (2026)

## Abstract (arXiv)

In an enterprise system, updating one field can set another, create a record, or start an approval. These effects are produced by business rules that are not built into the platform but written by each organization and revised over time. An agent working in such a system cannot predict the result of its own actions without knowing these rules. We study continual enterprise world model discovery, where an agent starts without knowledge of these business rules and discovers them by interacting with records and observing the outcomes. From those observations it builds a world model, which it revises as the rules change. To evaluate this, we introduce EnterpriseWorldShift, built on a live ServiceNow environment with nine tables, 25 hidden rules and 600 evaluation actions. It presents four versions of the same enterprise world, with the tables and records held fixed while a rule is modified, then added, then removed, so that discovery, revision, extension and retirement are each tested in turn. Our Continual Discovery Agent (CDA) builds such a model and carries it from one world to the next. It predicts the effects of the hidden rules more accurately than looking them up for each question, the approach taken by prior work, by up to 8.98 IoU points, and it answers from its own model without querying the running system.