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A Rule-Aware Prompt Framework for Structured Numeric Reasoning in Cyber-Physical Systems
arXiv:2512.12794v1 Announce Type: new
Abstract: Many cyber-physical systems (CPS) rely on high-dimensional numeric telemetry and explicit operating rules to maintain safe and efficient operation. Recent large language models (LLMs) are increasingly considered as decision-support components in such systems, yet most deployments focus on textual inputs and do not directly address rule-grounded reasoning over numeric measurements. This paper proposes a rule-aware prompt framework that systematically encodes CPS domain context, numeric normalization, and decision rules into a modular prompt architecture for LLMs. The framework decomposes prompts into five reusable modules, including role specification, CPS domain context, numeric normalization, rule-aware reasoning, and output schema, and exposes an interface for plugging in diverse rule sets. A key design element is separating rule specification from the representation of normalized numeric deviations, which enables concise prompts that remain aligned with domain rules. We analyze how different normalization strategies and prompt configurations influence rule adherence, interpretability, and token efficiency. The framework is model-agnostic and applicable across CPS domains. To illustrate its behavior, we instantiate it on numeric anomaly assessment in an IEEE 118-bus electric power transmission network and evaluate several prompting and adaptation regimes. The results show that rule-aware, z-score-based value blocks and a hybrid LLM-detector architecture can substantially improve consistency with CPS rules and anomaly detection performance while reducing token usage, providing a reusable bridge between numeric telemetry and general-purpose LLMs.
Abstract: Many cyber-physical systems (CPS) rely on high-dimensional numeric telemetry and explicit operating rules to maintain safe and efficient operation. Recent large language models (LLMs) are increasingly considered as decision-support components in such systems, yet most deployments focus on textual inputs and do not directly address rule-grounded reasoning over numeric measurements. This paper proposes a rule-aware prompt framework that systematically encodes CPS domain context, numeric normalization, and decision rules into a modular prompt architecture for LLMs. The framework decomposes prompts into five reusable modules, including role specification, CPS domain context, numeric normalization, rule-aware reasoning, and output schema, and exposes an interface for plugging in diverse rule sets. A key design element is separating rule specification from the representation of normalized numeric deviations, which enables concise prompts that remain aligned with domain rules. We analyze how different normalization strategies and prompt configurations influence rule adherence, interpretability, and token efficiency. The framework is model-agnostic and applicable across CPS domains. To illustrate its behavior, we instantiate it on numeric anomaly assessment in an IEEE 118-bus electric power transmission network and evaluate several prompting and adaptation regimes. The results show that rule-aware, z-score-based value blocks and a hybrid LLM-detector architecture can substantially improve consistency with CPS rules and anomaly detection performance while reducing token usage, providing a reusable bridge between numeric telemetry and general-purpose LLMs.
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