Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics
Summary
This paper investigates whether enterprise agents need learned world models or can instead rely on runtime discovery of system configurations. It introduces 'enterprise discovery agents' and a benchmark called CascadeBench, demonstrating that reading runtime context yields better robustness against deployment shifts in dynamic enterprise environments.
View Cached Full Text
Cached at: 05/13/26, 04:10 AM
Paper page - Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics
Source: https://huggingface.co/papers/2605.12178 Authors:
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
Abstract
Enterprise discovery agents that read system configuration at runtime outperform traditional world models in configurable environments where dynamics change over time.
World modelsenable agents to anticipate the effects of their actions by internalizing environment dynamics. Inenterprise systems, however, these dynamics are often defined by tenant-specific business logic that varies across deployments and evolves over time, making models trained on historical transitions brittle underdeployment shift. We ask a question the world-models literature has not addressed: when the rules can be read at inference time, does an agent still need to learn them? We argue, and demonstrate empirically, that in settings wheretransition dynamicsare configurable and readable,runtime discoverycomplements offline training by grounding predictions in the active system instance. We proposeenterprise discovery agents, which recover relevanttransition dynamicsat runtime by reading the system’s configuration rather than relying solely on internalized representations. We introduceCascadeBench, a reasoning-focused benchmark for enterprise cascade prediction that adopts the evaluation methodology ofWorld of Workflowson diverse synthetic environments, and use it together with deployment-shift evaluation to show that offline-trainedworld modelscan perform well in-distribution but degrade as dynamics change, whereas discovery-based agents are more robust under shift by grounding their predictions in the current instance. Our findings suggest that, in configurable enterprise environments, agents should not rely solely on fixed internalized dynamics, but should incorporate mechanisms for discovering relevant transition logic at runtime.
View arXiv pageView PDFAdd to collection
Get this paper in your agent:
hf papers read 2605\.12178
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2605.12178 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2605.12178 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2605.12178 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Agent-World introduces a self-evolving training framework for general agent intelligence that autonomously discovers real-world environments and tasks via the Model Context Protocol, enabling continuous learning. Agent-World-8B and 14B models outperform strong proprietary models across 23 challenging agent benchmarks.
Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform
This paper argues that large language models struggle with causal reasoning and long-horizon planning due to a mismatch between sequence prediction and reasoning over latent environment dynamics, and introduces the Latent Dynamics Inference perspective along with the Flux environment to study these limitations.
Bigger context windows aren't solving the enterprise memory problem. Here's why
This article critiques the trend of ever-larger context windows in LLMs, arguing they don't solve enterprise knowledge problems due to retrieval degradation, data volume, and lack of structure. It advocates for knowledge modeling layers that map relationships and intent before retrieval.
Context Graphs for Proactive Enterprise Agents
This paper proposes Context Graphs, a live relational data structure for enterprise entities that enables proactive agents to surface relevant information before users query, formalizing components for delta detection, proactivity scoring, and LLM-powered surfacing.
Learning Safe Agent Behaviour from Human Preferences and Justifications via World Models
This paper introduces DROPJ, a human-centred method for safely training and deploying agent policies by learning a world model from real-world trajectories, then eliciting human preferences with justifications to train a reward model for model predictive control. Experiments show that using human-generated simulated trajectories and justifications improves safety and reduces computational cost.