AI Agent for Reverse-Engineering Legacy Finite-Difference Code and Translating to Devito

• URL: http://arxiv.org/abs/2601.18381v1
• Date: Mon, 26 Jan 2026 11:31:00 GMT
• Title: AI Agent for Reverse-Engineering Legacy Finite-Difference Code and Translating to Devito
• Authors: Yinghan Hou, Zongyou Yang,
• Abstract summary: This study develops an integrated AI framework to facilitate the transformation of legacy finite difference implementations into the Devito environment.<n>Retrieval-Augmented Generation (RAG) and open-source Large Language Models are combined through multi-stage iterative in the system's hybrid LangGraph architecture.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To facilitate the transformation of legacy finite difference implementations into the Devito environment, this study develops an integrated AI agent framework. Retrieval-Augmented Generation (RAG) and open-source Large Language Models are combined through multi-stage iterative workflows in the system's hybrid LangGraph architecture. The agent constructs an extensive Devito knowledge graph through document parsing, structure-aware segmentation, extraction of entity relationships, and Leiden-based community detection. GraphRAG optimisation enhances query performance across semantic communities that include seismic wave simulation, computational fluid dynamics, and performance tuning libraries. A reverse engineering component derives three-level query strategies for RAG retrieval through static analysis of Fortran source code. To deliver precise contextual information for language model guidance, the multi-stage retrieval pipeline performs parallel searching, concept expansion, community-scale retrieval, and semantic similarity analysis. Code synthesis is governed by Pydantic-based constraints to guarantee structured outputs and reliability. A comprehensive validation framework integrates conventional static analysis with the G-Eval approach, covering execution correctness, structural soundness, mathematical consistency, and API compliance. The overall agent workflow is implemented on the LangGraph framework and adopts concurrent processing to support quality-based iterative refinement and state-aware dynamic routing. The principal contribution lies in the incorporation of feedback mechanisms motivated by reinforcement learning, enabling a transition from static code translation toward dynamic and adaptive analytical behavior.

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