arxiv: 2512.12794 · v2 · submitted 2025-12-14 · 📡 eess.SY · cs.SY

Recognition: 2 theorem links

· Lean Theorem

A Rule-Aware Prompt Framework for Structured Numeric Reasoning in Cyber-Physical Systems

Yichen Liu , Hongyu Wu , Bo Liu

Authors on Pith no claims yet

Pith reviewed 2026-05-16 22:13 UTC · model grok-4.3

classification 📡 eess.SY cs.SY

keywords rule-aware promptingnumeric reasoningsmart gridanomaly detectionLLMpower systemz-score normalizationIEEE 118-bus

0 comments

The pith

A modular prompt framework encodes grid rules and z-score normalized values so LLMs can reason over numeric telemetry while staying consistent with operating constraints.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Smart grids generate streams of numeric measurements that must obey explicit rules for safe operation, yet most LLMs still struggle to apply those rules directly to numbers rather than text. The paper builds a reusable prompt architecture that splits the input into separate modules for domain context, numeric normalization, rule statements, and output format. By keeping the rule text independent of the actual measurement blocks and using z-scores to represent deviations, the prompts stay short and aligned with power-system criteria. Experiments on the IEEE 118-bus network show that this structure, especially when paired with a hybrid LLM-plus-deep-learning classifier, raises both rule adherence and anomaly-detection scores while lowering token counts.

Core claim

The framework decomposes prompts into reusable modules (role, domain context, numeric normalization, rule-aware reasoning, value block, and output schema) and demonstrates that separating rule specification from z-score-based numeric deviations produces concise, rule-aligned reasoning; when tested on numeric anomaly detection in the IEEE 118-bus network, rule-aware z-score value blocks together with a hybrid LLM+DL setup improve consistency with grid operating rules, raise detection performance, and reduce token usage compared with standard prompting regimes.

What carries the argument

Modular prompt architecture that isolates rule specification from z-score normalized numeric value blocks, allowing plug-in of diverse grid operating rules without rewriting the entire prompt.

If this is right

Prompts become shorter and directly aligned with power-system criteria because rules are stated separately from the numeric blocks.
LLMs exhibit higher consistency with explicit grid operating rules on numeric telemetry.
Anomaly detection performance improves when the rule-aware prompt is combined with a downstream deep-learning classifier.
Token consumption drops because z-score representation replaces raw numeric strings and verbose rule repetition.
The same modular interface can accept new rule sets without redesigning the prompt skeleton.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The separation of rules from numeric representation could be reused in other cyber-physical domains that pair sensor streams with formal constraints, such as industrial control systems or autonomous vehicles.
Real-time grid operators might integrate the output schema module directly into existing SCADA dashboards to surface rule violations alongside numeric alerts.
A follow-up test could measure how performance changes when the z-score block is replaced by other normalization schemes or when the hybrid classifier is removed entirely.

Load-bearing premise

The modular prompt structure and z-score normalization will produce reliable rule-following behavior in LLMs across diverse real-world grid conditions and rule sets beyond the single IEEE 118-bus case shown.

What would settle it

Measure whether rule consistency and anomaly-detection F1 scores remain above the reported baselines when the same framework is applied to a different transmission network (for example, IEEE 300-bus) with operating rules that were never used to design the prompt modules.

Figures

Figures reproduced from arXiv: 2512.12794 by Bo Liu, Hongyu Wu, Yichen Liu.

read the original abstract

Smart grids rely on high-dimensional numeric telemetry and explicit operating rules to maintain reliable and secure operation. Recent large language models (LLMs) are increasingly considered as candidate decision-support components for power system operations, yet most deployments focus on textual logs, alerts, or operator messages and do not directly address rule-grounded reasoning over numeric grid measurements. This paper proposes a rule-aware prompt framework that systematically encodes power system domain context, numeric normalization, and decision rules into a modular prompt architecture for LLMs. The framework decomposes prompts into reusable modules, including role, domain context, numeric normalization, rule-aware reasoning, value block, and output schema, and exposes an interface for plugging in diverse grid operating rules. A key design element separates rule specification from the representation of normalized numeric deviations, enabling concise prompts aligned with power system criteria. To illustrate its behavior, we instantiate the framework on numeric anomaly detection in the IEEE 118-bus transmission network and evaluate several prompting and adaptation regimes. The results show that rule-aware, z-score-based value blocks and a hybrid LLM+DL architecture substantially improve both consistency with grid operating rules and anomaly detection performance while reducing token usage, providing a reusable bridge between grid telemetry and general-purpose LLMs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The modular prompt framework separates rules from z-score normalized numbers in a reusable way, but the single IEEE 118-bus example provides no metrics or baselines to support the performance claims.

read the letter

The paper's main contribution is a prompt architecture that splits LLM inputs for power grid data into distinct modules: role, domain context, numeric normalization via z-scores, rule-aware reasoning, value blocks, and output schema. This lets rules sit apart from the normalized telemetry, which keeps prompts shorter and more aligned with actual grid criteria than just feeding raw logs or alerts into a model. The IEEE 118-bus anomaly detection example illustrates how a hybrid LLM plus deep learning setup might apply it in practice. That separation is a practical step for anyone trying to get LLMs to follow explicit operating rules on numeric measurements rather than text summaries. It extends prior LLM work in power systems by focusing directly on structured numeric reasoning instead of operator messages. The design looks straightforward to adapt for different rule sets, which is the part that could see reuse. The central claims about better rule consistency, improved anomaly detection, and lower token counts come without numbers, baselines, error bars, or a clear evaluation protocol. The test stays on one network and one task, so there is no check on whether the z-score blocks or modules hold up under different bus counts, voltage limits, or contingency rules. That makes the reusability part more of a design assertion than a demonstrated result. Readers working on LLM tools for cyber-physical systems or smart grid decision support would find the template worth trying out. It gives a concrete structure they can implement and test on their own data. I would send this to peer review. The modular idea is clear and addresses a real gap, but the authors need to add quantitative results and broader tests before the claims can be evaluated properly.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a modular rule-aware prompt framework for enabling LLMs to perform structured numeric reasoning over high-dimensional telemetry in cyber-physical systems such as smart grids. Prompts are decomposed into reusable modules (role, domain context, numeric normalization, rule-aware reasoning, value block, output schema) with an interface for inserting grid operating rules. A central design choice separates explicit rule specification from z-score normalized numeric deviations. The framework is illustrated on anomaly detection in the IEEE 118-bus network, where rule-aware z-score value blocks combined with a hybrid LLM+DL architecture are claimed to improve rule consistency, anomaly detection performance, and token efficiency.

Significance. If the performance claims can be substantiated with quantitative evidence, the work offers a practical, reusable method for grounding LLMs in domain rules and normalized numeric data without fine-tuning, which could aid reliable decision support in power-system operations. The modular separation of rules from z-score blocks is a sensible and potentially generalizable design choice. The current single illustrative case without metrics or baselines, however, prevents a full assessment of significance.

major comments (2)

Abstract: the central claim that rule-aware z-score value blocks and the hybrid LLM+DL architecture 'substantially improve both consistency with grid operating rules and anomaly detection performance while reducing token usage' is unsupported because no quantitative metrics, baselines, error bars, accuracy/F1 scores, or evaluation protocol are reported for the IEEE 118-bus case.
Evaluation section (IEEE 118-bus instantiation): the manuscript provides only a qualitative illustration rather than a controlled comparison against standard prompting, DL-only, or non-z-score baselines, which is load-bearing for the headline performance claims.

minor comments (2)

The description of how the DL component interfaces with the LLM prompt modules (e.g., whether DL outputs feed into value blocks or post-process LLM answers) lacks a diagram or pseudocode, reducing clarity.
An explicit example of a complete instantiated prompt (including a sample z-score value block and rule insertion) would help readers verify the claimed token reduction and modularity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that the performance claims require quantitative substantiation and will revise the manuscript to include a controlled evaluation with metrics and baselines for the IEEE 118-bus case.

read point-by-point responses

Referee: Abstract: the central claim that rule-aware z-score value blocks and the hybrid LLM+DL architecture 'substantially improve both consistency with grid operating rules and anomaly detection performance while reducing token usage' is unsupported because no quantitative metrics, baselines, error bars, accuracy/F1 scores, or evaluation protocol are reported for the IEEE 118-bus case.

Authors: We acknowledge that the abstract overstates the current results. The IEEE 118-bus example in the manuscript is intended as an illustration of framework behavior rather than a benchmark. In revision we will rewrite the abstract to describe the contribution accurately as a modular prompt framework demonstrated on an illustrative case, and we will add a new quantitative evaluation section. This section will report anomaly detection accuracy, F1 scores, rule-consistency percentage, token counts, and comparisons against standard prompting and DL-only baselines, with error bars from repeated runs and a clear evaluation protocol. revision: yes
Referee: Evaluation section (IEEE 118-bus instantiation): the manuscript provides only a qualitative illustration rather than a controlled comparison against standard prompting, DL-only, or non-z-score baselines, which is load-bearing for the headline performance claims.

Authors: We agree that a qualitative illustration alone cannot support the headline claims. We will expand the evaluation section into a controlled quantitative study on the IEEE 118-bus network. The revised section will define an evaluation protocol that injects known anomalies, provides ground-truth labels, and measures detection performance (precision, recall, F1), rule adherence, and token efficiency. We will include four baselines: vanilla LLM prompting, DL-only classifier, LLM without z-score normalization, and the proposed hybrid architecture. Results will be reported with statistical measures. revision: yes

Circularity Check

0 steps flagged

No circularity; modular prompt design evaluated empirically on single case

full rationale

The paper proposes a reusable modular prompt framework (role, domain context, numeric normalization, rule-aware reasoning, value block, output schema) for LLM-based numeric reasoning in power systems and demonstrates it via an empirical case study on IEEE 118-bus anomaly detection. No equations, fitted parameters, or derivations are present that could reduce a claimed result to its own inputs by construction. The central contribution is a design pattern whose performance is measured directly on held-out telemetry; the single-network evaluation is a limitation on generalization but does not create circularity. No self-citations are load-bearing for any uniqueness claim, and no ansatz or renaming of known results occurs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that LLMs will reliably follow the proposed modular structure for numeric reasoning and that z-score normalization plus rule separation produces measurable gains; no free parameters or invented physical entities are introduced.

axioms (1)

domain assumption Large language models can perform structured reasoning over numeric telemetry when prompts are decomposed into role, context, normalization, rules, values, and output schema modules.
Invoked as the basis for the framework's effectiveness in the abstract.

invented entities (1)

Rule-aware prompt modules (role, domain context, numeric normalization, rule-aware reasoning, value block, output schema) no independent evidence
purpose: To encode power system rules and normalized numeric deviations into concise, reusable prompts for LLMs.
Newly defined components introduced by the framework.

pith-pipeline@v0.9.0 · 5517 in / 1351 out tokens · 76198 ms · 2026-05-16T22:13:16.663987+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We propose a rule-aware prompt framework that decomposes prompts into reusable modules... numeric normalization... z-score-based value blocks
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

three-sigma criterion... zi = (xi − μi)/max(σi, ϵ)... flagi if |zi| ≥ 3.0

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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