Designing FSMs Specifications from Requirements with GPT 4.0

• URL: http://arxiv.org/abs/2603.29140v1
• Date: Tue, 31 Mar 2026 01:42:25 GMT
• Title: Designing FSMs Specifications from Requirements with GPT 4.0
• Abstract summary: This paper proposes an LLM-based framework for designing Finite state machines from requirements.<n>It also suggests an expert-centric approach based on FSM mutation and test generation for repairing the FSMs produced by LLMs.
• Score: 3.4410212782758047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Finite state machines (FSM) are executable formal specifications of reactive systems. These machines are designed based on systems' requirements. The requirements are often recorded in textual documents written in natural languages. FSMs play a crucial role in different phases of the model-driven system engineering (MDE). For example, they serve to automate testing activities. FSM quality is critical: the lower the quality of FSM, the higher the number of faults surviving the testing phase and the higher the risk of failure of the systems in production, which could lead to catastrophic scenarios. Therefore, this paper leverages recent advances in the domain of LLM to propose an LLM-based framework for designing FSMs from requirements. The framework also suggests an expert-centric approach based on FSM mutation and test generation for repairing the FSMs produced by LLMs. This paper also provides an experimental analysis and evaluation of LLM's capacities in performing the tasks presented in the framework and FSM repair via various methods. The paper presents experimental results with simulated data. These results and methods bring a new analysis and vision of LLMs that are useful for further development of machine learning technology and its applications to MDE.

Related papers

• LLM-Assisted Repository-Level Generation with Structured Spec-Driven Engineering [4.751657370831477]
  State-of-the-art Large Language Models (LLMs) excel in code generation at the function level, but output quality significantly declines when scaling to repository-level systems.<n>To address this, we propose structured spec-driven engineering (SSDE)<n>We argue that structured specifications as LLM inputs make high-quality, repository-level code generation a tangible goal, while at the same time offering superior verifiability.
  arXiv  Detail & Related papers  (2026-05-04T10:58:22Z)
• Codified Finite-state Machines for Role-playing [70.86310301713068]
  We introduce Codified Finite-State Machines (CFSMs), a framework that automatically codifies textual character profiles into FSMs.<n>CFSMs extract key states and transitions directly from the profile, producing interpretable structures that enforce character consistency.<n>We extend CFSMs into Codified Probabilistic Finite-State Machines (CPFSMs), where transitions are modeled as probability distributions over states.
  arXiv  Detail & Related papers  (2026-02-05T17:19:18Z)
• LLM-FSM: Scaling Large Language Models for Finite-State Reasoning in RTL Code Generation [3.4714122723537333]
  We present LLM-FSM, a benchmark that evaluates how well large language models (LLMs) can recover finite-state machine (FSM) behavior.<n>Unlike prior specification-to-RTL benchmarks that rely on manually constructed examples, LLM-FSM is built through a fully automated pipeline.
  arXiv  Detail & Related papers  (2026-02-03T04:48:26Z)
• AutoFSM: A Multi-agent Framework for FSM Code Generation with IR and SystemC-Based Testing [2.5793366206387827]
  This paper proposes AutoFSM, a collaborative framework designed for finite state machine (FSM) code generation tasks.<n>AutoFSM introduces a structurally clear intermediate representation (IR) to reduce syntax error rate during code generation.<n>It is the first to integrate SystemC-based modeling with automatic testbench generation.
  arXiv  Detail & Related papers  (2025-12-12T09:15:46Z)
• CFD-copilot: leveraging domain-adapted large language model and model context protocol to enhance simulation automation [6.71937346130764]
  CFD-copilot is a framework designed to facilitate natural language-driven CFD simulation from setup to post-processing.<n>For post-processing, the framework utilizes the model context protocol (MCP), an open standard that decouples LLM reasoning from external tool execution.<n>The framework was evaluated on benchmarks including the NACA0012 airfoil and the three-element 30P-30N airfoil.
  arXiv  Detail & Related papers  (2025-12-08T11:42:32Z)
• An Agentic Flow for Finite State Machine Extraction using Prompt Chaining [0.0]
  FlowFSM is a novel agentic framework that leverages Large Language Models (LLMs) to extract accurate Finite-State Machines (FSMs) from RFC documents.<n>FlowFSM achieves high extraction precision while minimizing hallucinated transitions, showing promising results.
  arXiv  Detail & Related papers  (2025-07-15T11:50:25Z)
• MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science [62.96434290874878]
  Current Multi-Modal Large Language Models (MLLM) have shown strong capabilities in general visual reasoning tasks.<n>We develop a new framework, named Multi-Modal Scientific Reasoning with Physics Perception and Simulation (MAPS) based on an MLLM.<n>MAPS decomposes expert-level multi-modal reasoning task into physical diagram understanding via a Physical Perception Model (PPM) and reasoning with physical knowledge via a simulator.
  arXiv  Detail & Related papers  (2025-01-18T13:54:00Z)
• FVEval: Understanding Language Model Capabilities in Formal Verification of Digital Hardware [4.480157114854711]
  We present FVEval, the first comprehensive benchmark for characterizing large language models (LLMs) performance in tasks pertaining to formal verification (FV) The benchmark consists of three sub-tasks that measure LLM capabilities at different levels. We present both collections of expert-written verification collateral and methodologies to scalably generate synthetic examples aligned with FV.
  arXiv  Detail & Related papers  (2024-10-15T21:48:57Z)
• FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability [70.84333325049123]
  FoFo is a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats. This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats.
  arXiv  Detail & Related papers  (2024-02-28T19:23:27Z)
• Towards Generating Executable Metamorphic Relations Using Large Language Models [46.26208489175692]
  We propose an approach for automatically deriving executable MRs from requirements using large language models (LLMs) To assess the feasibility of our approach, we conducted a questionnaire-based survey in collaboration with Siemens Industry Software.
  arXiv  Detail & Related papers  (2024-01-30T13:52:47Z)
• SimLM: Can Language Models Infer Parameters of Physical Systems? [56.38608628187024]
  We investigate the performance of Large Language Models (LLMs) at performing parameter inference in the context of physical systems. Our experiments suggest that they are not inherently suited to this task, even for simple systems. We propose a promising direction of exploration, which involves the use of physical simulators to augment the context of LLMs.
  arXiv  Detail & Related papers  (2023-12-21T12:05:19Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)
• Instruction Tuning for Large Language Models: A Survey [52.86322823501338]
  We make a systematic review of the literature, including the general methodology of supervised fine-tuning (SFT)<n>We also review the potential pitfalls of SFT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies.
  arXiv  Detail & Related papers  (2023-08-21T15:35:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.