arXiv cs.AI· Muhammad Ashfaq, Ahmed R. Sadik, Ewan Warburton, Pyry Kotilainen, Niko M\"akitalo, Tommi Mikkonen·· 1 天前AI 评分34
SafeOrchestrate：面向信息物理系统的运行时校验意图驱动编排架构
SafeOrchestrate: Runtime Checked Intent-Driven Orchestration for Cyber-Physical Systems
AI 导读
SafeOrchestrate 提出一种运行时校验的意图驱动 CPS 编排参考架构，将自然语言意图拆解为原子目标并生成可校验规范，再经 MCP 暴露的 TD 能力落地，用 SMT 护栏在执行前校验每个候选动作。
正文 · 原文
Abstract:Modern cyber physical systems (CPS) integrate heterogeneous sensors, actuators, controllers, and services, composed at runtime rather than at design time. Orchestrating such systems through natural language (NL) is attractive, but unconstrained large language model (LLM) tool use is risky because actions must respect device capabilities, runtime state, and safety constraints. We present Safe Orchestrate, a reference architecture for runtime checked, intent driven CPS orchestration. Given an NL intent, it derives atomic goals, generates a runtime checkable specification for each, grounds the specifications to device affordances, and verifies every candidate action against runtime state and safety constraints before execution. In a Web of Things (WoT) instantiation, the architecture produces Linear Temporal Logic (LTL) style specifications from the NL intent, grounds them to Thing Description (TD) affordances exposed through the Model Context Protocol (MCP), and checks candidate actions against satisfiability modulo theories (SMT) backed guardrails. We evaluate this instantiation on simulated smart home, greenhouse, and industrial automation tasks against a direct LLM to MCP baseline. Safe Orchestrate maintains 100% required action accuracy on every task and 100% MCP Execution Similarity (MCP ES) on all but one, where MCP ES is 92%. By contrast, the baseline degrades on multi device, event triggered, and ordered tasks, with MCP ES falling to 13%. These results suggest that combining LLM based specification generation with role aware grounding and runtime constraint checking improves reliability for open CPS orchestration without enriched TD annotations or planning domain expertise.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.09364 [cs.AI] |
| (or arXiv:2611.09364v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.09364 arXiv-issued DOI via DataCite |
Submission history
From: Ahmed R. Sadik Dr.-Ing. [view email]
[v1]
Fri, 6 Nov 2026 12:03:02 UTC (123 KB)
[v2]
Wed, 11 Nov 2026 08:55:00 UTC (125 KB)
来源：arXiv cs.AI · arxiv.org