From Natural Language Intent to Executable Control: Hierarchical Feedback Framework for LLM-Based CPS Planning

Forum topic · 小凯 · 2026-10-19

Summary

This paper (arXiv:2610.22441) by Jinglin Dai, Jiawan Wang, Ruiqi Hong, and Lei Bu addresses two key gaps in using large language models (LLMs) for translating natural language commands into executable task plans for cyber-physical systems (CPSs): plans may misinterpret user intent, and logically plausible plans may still be infeasible under component dynamics, interactions, or safety constraints. The authors propose a hierarchical feedback framework in which LLMs generate both a structured task plan and corresponding signal temporal logic (STL) constraints. A sound algorithm identifies definite contradictions between the plan and constraints and provides feedback for revision. At the execution level, when control synthesis fails, diagnostic information such as violated constraints and conflicting component assignments is extracted and fed back to guide task replanning. Experiments across seven heterogeneous multi-component CPS scenarios show that the framework generates executable plans and feasible control executions satisfying the original natural language missions, achieving an 87.9% success rate with GPT-5.

Paper Overview

Field: Machine Learning Authors: Jinglin Dai, Jiawan Wang, Ruiqi Hong, Lei Bu Published: 2026-10-19 arXiv: 2610.22441

Abstract

Large language models (LLMs) provide a convenient interface for translating natural language commands into task plans for cyber-physical systems (CPSs). However, two gaps hinder their use for real execution: generated plans may misinterpret or omit the user's intent, and even a logically plausible plan may be infeasible under component dynamics, interactions, or safety constraints. Existing LLM-based planning approaches often lack informative feedback on why a generated plan fails, providing little guidance for revising the plan accordingly.

To address these challenges, the authors present a hierarchical feedback framework that uses different types of feedback information to guide LLMs in generating executable plans for CPSs:

LLMs are prompted to generate both a structured task plan and corresponding signal temporal logic (STL) constraints.
A sound algorithm identifies definite contradictions between the plan and the constraints, providing feedback for revising inconsistencies.
At the execution level, when control synthesis fails, the framework extracts diagnostic information — such as violated constraints and conflicting component assignments — and feeds it back to guide task replanning.

Results

Experiments on seven heterogeneous multi-component CPS scenarios show that the framework can generate executable plans and feasible control executions that satisfy the original natural language missions, with a success rate of 87.9% using GPT-5.

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