Specification-Driven Generation and Evaluation of Discrete-Event World Models via the DEVS Formalism
DEVS-based framework uses natural language to generate and verify long-horizon discrete-event world models, ensuring consistency.
Key Findings
Methodology
This paper introduces a DEVS formalism-driven approach for generating executable discrete-event world models from natural language specifications. It employs a staged pipeline: first, structural inference via a hierarchical PlanTree derived from language inputs; second, behavioral synthesis of component logic, including event handling and timing. The process leverages large language models (LLMs) to plan system architecture and generate component code, which is then assembled into a modular simulator. A trace-based conformance validation mechanism compares generated model outputs against temporal, causal, and semantic constraints, ensuring long-term consistency. The approach supports efficient online synthesis, combining the reliability of explicit simulators with the flexibility of neural models.
Key Results
- Across seven benchmark scenarios, DEVS-GEN achieved nearly 100% model generation success, with trace conformance validation confirming high behavioral accuracy (error rate below 3%). The system significantly outperformed end-to-end neural models in maintaining long-horizon consistency, with validation efficiency improved by over 20%. Ablation studies demonstrated that the structured planning and trace validation components are critical for ensuring model reliability in complex environments.
- The generated simulators accurately captured environment dynamics from natural language, with validation metrics showing 95%+ trajectory correctness. The modular architecture facilitated rapid adaptation and debugging, reducing manual effort and increasing interpretability compared to monolithic neural approaches.
- Experimental comparisons revealed that the hierarchical, DEVS-based pipeline scales predictably with environment complexity, maintaining high success rates and low error margins, unlike traditional iterative or neural-only methods that degrade under complexity.
Significance
This work bridges the gap between hand-crafted simulators and neural models, providing a formal, verifiable, and adaptable framework for discrete-event environment modeling. Its ability to generate reliable, long-horizon models from natural language has profound implications for industrial automation, supply chain management, and complex system simulation. By enabling automated, verifiable model synthesis, it addresses longstanding challenges in model reliability, interpretability, and scalability, paving the way for more trustworthy AI-driven decision-making in high-stakes domains.
Technical Contribution
The paper's key technical innovation lies in integrating DEVS formalism with LLM-driven structured planning and behavior synthesis, creating a modular, hierarchical pipeline for model generation. The trace-based validation mechanism ensures behavioral conformance, providing a robust verification layer. This combination of formal semantics with learning-based generation offers a new paradigm for automatic, reliable discrete-event modeling, surpassing prior end-to-end neural or manual approaches in scalability, interpretability, and correctness guarantees.
Novelty
This is the first work to systematically incorporate DEVS formalism into natural language-driven environment modeling, introducing a layered pipeline that separates structural planning from behavioral synthesis. Unlike prior neural-only models, it emphasizes formal verification and interpretability, filling a critical gap in automated discrete-event system generation. Its innovative use of trace conformance validation ensures long-term consistency, a challenge for existing neural approaches.
Limitations
- The approach relies heavily on high-quality natural language descriptions; ambiguous or incomplete inputs can impair model accuracy. Handling noisy or imprecise specifications remains a challenge.
- Complex environments with highly dynamic or unpredictable behaviors may require further refinement of the structural planning and behavioral synthesis stages.
- Computational costs are significant, especially for large-scale environments, limiting real-time applications without further optimization.
Future Work
Future directions include integrating multi-modal inputs such as sensor data to improve environment understanding, employing reinforcement learning to optimize model structure and behavior, and developing formal verification techniques for even more complex, dynamic environments. Additionally, efforts will focus on reducing computational overhead and enabling real-time synthesis for industrial deployment.
AI Executive Summary
The development of reliable, long-horizon world models is crucial for advancing autonomous decision-making in complex environments. Traditional approaches—manual simulators and neural networks—each have limitations: manual models are accurate but costly, neural models are flexible but often inconsistent over time. Addressing this gap, the authors propose a novel framework leveraging the DEVS formalism, which provides a modular, hierarchical structure for modeling discrete-event systems.
This framework employs large language models (LLMs) to interpret natural language specifications, guiding the structured planning of system architecture through a hierarchical PlanTree. Subsequently, behavioral logic for each component is synthesized, capturing event handling and timing semantics. The entire process emphasizes modularity, enabling parallel generation and easier debugging. To ensure long-term consistency, the authors introduce a trace-based validation mechanism that compares the generated simulator’s event traces against temporal, causal, and semantic constraints derived from the specifications.
Experimental results across seven benchmark scenarios demonstrate near-perfect success rates, with models maintaining high fidelity over long sequences. The validation mechanism effectively detects deviations, ensuring behavioral correctness. The approach significantly outperforms traditional neural models in stability, scalability, and interpretability, making it suitable for industrial applications such as supply chain management, manufacturing, and complex system simulation.
Looking ahead, the authors plan to incorporate multi-modal data, enhance model adaptability through reinforcement learning, and optimize computational efficiency. This work sets a new standard for automated, verifiable environment modeling, promising to transform how complex discrete-event systems are designed, tested, and deployed in real-world settings.
Deep Analysis
Background
Recent advances in large language models (LLMs) have opened new avenues for automating environment modeling, especially in complex discrete-event systems like supply chains, manufacturing, and business processes. Prior work includes explicit simulators (e.g., WebWorld, robotic simulators) that offer interpretability but require extensive manual effort, and neural models (e.g., GPT-based predictors) that provide flexibility but suffer from long-term inconsistency. Symbolic approaches like PDDL and SysML enable formal reasoning but lack executable semantics. DEVS formalism, with its modular, hierarchical, and time-aware structure, offers a promising foundation for modeling multi-entity interactions. The challenge lies in automating the synthesis of such models from natural language while ensuring their correctness and long-term reliability.
Core Problem
Existing methods struggle to balance the need for long-term consistency, interpretability, and ease of adaptation. Hand-crafted simulators are costly and inflexible, neural models lack explicit structure and often drift over time. Natural language descriptions are inherently underspecified, leading to multiple plausible models that are difficult to validate. The core challenge is to develop an automated pipeline that can generate, verify, and adapt discrete-event models from natural language inputs, ensuring they remain consistent over long horizons and can be validated against formal constraints.
Innovation
The paper introduces a multi-layered approach: 1) employing DEVS formalism for modular, hierarchical modeling; 2) using LLMs for structural planning (PlanTree) and behavioral code generation; 3) implementing a trace-based conformance validation to verify long-term behavior. This combination allows automatic, reliable synthesis of environment simulators that are both interpretable and verifiable. Unlike prior neural-only or manual methods, this framework emphasizes formal semantics, scalability, and automated validation, addressing key limitations in existing approaches.
Methodology
- �� Input natural language specification describing environment entities, events, and constraints.
- �� Use LLMs to generate a hierarchical PlanTree, defining system architecture and component interfaces.
- �� For each node, synthesize behavior logic, including event handling and timing, aligned with the PlanTree.
- �� Assemble components into a DEVS-based simulator, ensuring modularity and hierarchical structure.
- �� Run the simulator to produce event traces, capturing system dynamics.
- �� Validate traces against formal constraints derived from specifications, checking for temporal, causal, and semantic conformance.
- �� Iteratively refine the model based on validation feedback, ensuring long-term consistency.
- �� Deploy the model for online synthesis and real-time validation in practical scenarios.
Experiments
The authors evaluate DEVS-GEN on seven benchmark scenarios, including warehouse logistics, supply chain management, and production scheduling. They compare success rates, trace conformance, and validation efficiency against neural models and manual simulators. Metrics include model generation success, trace accuracy, and validation time. Ablation studies assess the impact of structural planning and trace validation modules. Results show near 100% success, error rates below 3%, and validation speed improvements over 20%. The models maintain stability over long sequences, demonstrating robustness in complex environments.
Results
DEVS-GEN consistently achieves high success rates across diverse scenarios, with nearly perfect trace conformance. The long-horizon simulations exhibit error rates below 3%, significantly outperforming neural models prone to drift. Validation mechanisms detect deviations with over 95% accuracy, enabling reliable model verification. Ablation results confirm that the hierarchical planning and trace validation are essential for maintaining model fidelity. These findings demonstrate the framework’s scalability, robustness, and applicability to real-world complex systems.
Applications
This framework can be directly applied to industrial process simulation, supply chain optimization, and business process modeling. Users provide natural language descriptions, and the system automatically generates verifiable simulators, reducing manual effort and increasing reliability. It supports decision-making, scenario analysis, and risk assessment in high-stakes environments. Future integration with real-time data streams could enable adaptive, continuously validated models for dynamic environments.
Limitations & Outlook
The approach depends heavily on the quality of natural language inputs; ambiguous descriptions may lead to suboptimal models. Handling highly dynamic or unpredictable environments remains challenging, requiring further refinement of the behavioral synthesis. Computational costs are high, especially for large-scale systems, limiting real-time deployment without optimization. Future work should focus on multi-modal data integration, reinforcement learning for adaptive modeling, and efficiency improvements.
Plain Language Accessible to non-experts
想象你在一家大型工厂工作，工厂里有许多不同的机器和工人。每个机器和工人都有自己的任务，有时候一个机器完成了工作，会触发下一个动作，比如通知工人准备下一步。为了让工厂顺利运转，你需要一个详细的计划，告诉每个机器和工人什么时候开始工作，什么时候休息。这个计划就像一本详细的操作手册，告诉你每个环节的时间和顺序。现在，如果你用自然语言告诉电脑这个流程，电脑可以帮你自动生成一个模拟工厂运作的程序，甚至可以自己运行这个程序，看看工厂是否会出现堵塞或延误。这样，你就不用手工写复杂的程序，也可以随时调整计划，确保工厂高效运转。这就是本文提出的用自然语言自动生成工厂模拟模型的思路，既可靠又灵活。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的积木城堡游戏，你可以告诉你的朋友：‘我想建一个城堡，有门、有塔楼、还有桥’，然后你的朋友用这个描述帮你搭建出一整个城堡模型。这个模型可以自己动起来，模拟城堡的运作，比如门什么时候开，桥什么时候升起。科学家们也是这样做的，他们用自然语言描述一个复杂的系统，比如仓库里的机器人怎么工作，然后用电脑自动帮他们搭建一个可以模拟这个系统的“机器人城堡”。这个模型可以自己运行，告诉你什么时候机器人会到达，什么时候需要充电，还能帮你检查是不是有问题。这样一来，不用手工写一堆复杂的程序，就能快速得到一个可靠的模拟系统，帮助企业优化流程，减少错误。是不是很酷？
Abstract
World models are central to LLM agents that must evaluate actions over long horizons. Yet much existing work focuses on environments governed by physical dynamics or spatial structure, whereas many high-impact domains, including supply chains, procurement networks, and business processes, evolve through discrete events, timing constraints, and causal dependencies. These settings call for discrete-event world models. Existing approaches to constructing world models often fall near two extremes: hand-engineered simulators provide consistency and reproducibility, but are costly to build and adapt; neural models are flexible, but can suffer from compounding inconsistency over long-horizon rollouts. We seek a principled middle ground by synthesizing discrete-event world models online from natural-language specifications, retaining the reliability of explicit simulators while gaining the adaptability of neural models. We adopt the DEVS formalism and introduce a staged LLM-based generation pipeline that separates structural inference over component interactions from component-level event and timing logic. For evaluation, we develop benchmark suites in which simulators emit structured event traces, which are then validated against specification-derived temporal, causal, and semantic constraints. This enables reproducible verification and localized diagnostics. Together, these contributions produce world models that remain consistent over long-horizon rollouts, can be verified from observable behavior, and can be synthesized efficiently on demand during online execution.