AI Agent for Reverse-Engineering Legacy Finite-Difference Code and Translating to Devito
Researchers have developed an AI agent framework that automates the translation of legacy finite-difference code into Devito, a modern computational framework. The system combines retrieval-augmented generation (RAG) with large language models and implements reinforcement learning feedback mechanisms to enable dynamic code transformation with validation across correctness, structure, and API compliance.
This research addresses a significant technical challenge in scientific computing: modernizing legacy codebases without losing functionality or introducing errors. Many institutions maintain decades-old finite-difference implementations in Fortran and other legacy languages, creating barriers to performance optimization and maintenance. The AI agent framework tackles this problem through a sophisticated multi-layered approach that goes beyond simple code translation.
The system's innovation lies in its hybrid architecture combining GraphRAG with static code analysis. By constructing a knowledge graph of Devito's capabilities and parsing legacy code structure, the agent can understand both the source logic and target platform requirements simultaneously. The three-level query strategy derived from Fortran analysis ensures contextual relevance when retrieving guidance from the knowledge base, addressing a core limitation of naive language model approaches that often generate syntactically correct but semantically incorrect translations.
For the scientific computing and computational finance sectors, this framework could accelerate migration to modern platforms like Devito, potentially unlocking performance improvements and reducing technical debt. The validation framework integrating static analysis with G-Eval represents enterprise-grade quality assurance, critical for domains where computational errors have real consequences. The reinforcement learning feedback mechanism enables continuous improvement of translation quality without human intervention.
Developers and research institutions using legacy numerical code should monitor this project's maturation. If successfully deployed, similar agent-based approaches could extend to other legacy-to-modern code migrations, creating broader economic value across finance, climate modeling, and materials science sectors.
- →AI agent framework automates translation of legacy finite-difference code to Devito using RAG and LLMs with multi-stage retrieval pipelines.
- →GraphRAG optimization with Leiden community detection enables efficient knowledge graph querying across seismic, CFD, and performance optimization domains.
- →Pydantic-based constraints and G-Eval validation ensure translated code maintains execution correctness, structural soundness, and mathematical consistency.
- →Reinforcement learning feedback mechanisms enable dynamic, adaptive code transformation beyond static translation approaches.
- →Framework addresses significant technical debt in scientific computing by automating safe migration of legacy codebases to modern platforms.