Research Papers | Code Metal Research

Research Papers

All papers and articles from Code Metal Research.

Paper List

1. The Trust Problem Has Shifted: What Formal Verification Can and Cannot Guarantee About AI-Generated Code

   Code Metal Article
   Formal Verification
   July 8, 2026
   A clear-eyed technical assessment of formal verification for AI-generated code: which approaches are credible, what barriers remain, and where the market will emerge first.

2. The Real Cost of Leaving NVIDIA

   Code Metal Article
   GPU/CUDA
   June 4, 2026
   What Automated Transpilation Actually Costs, and What It Doesn't.

3. AI-generated code that works — and proves it

   Code Metal Article
   Formal Verification
   May 18, 2026
   How Code Metal combines AI with formal methods to build trusted code translation systems, and welcoming Prof. Loris D'Antoni as our first Code Metal Scholar.

4. Counting Without Running: Evaluating LLMs' Reasoning About Code Complexity

   Research Paper
   GPU/CUDA
   December 4, 2025
   Introduces gpuFLOPBench, a benchmark containing 577 CUDA kernels to evaluate whether language models can predict floating-point operation counts without execution, revealing limitations in understanding hardware-specific performance details.

5. Verified Code Transpilation with LLMs

   Research Paper
   Formal Verification
   October 15, 2025
   LLMLift uses large language models (LLMs) to automate code translation between domain-specific languages (DSLs), ensuring functional correctness and outperforming traditional tools in speed and efficiency.

6. Workflows vs Agents for Code Translation

   Research Paper
   LLMs
   September 22, 2025
   Compares structured workflows versus agentic approaches for MATLAB-to-HDL translation, showing that agentic methods with the Model Context Protocol increase simulation reach rates by over 20 percentage points on mid-sized models.

7. UniPar: A Unified LLM-Based Framework for Parallel and Accelerated Code Translation in HPC

   Research Paper
   HPC
   September 15, 2025
   Introduces UniPar, an evaluation framework for assessing how large language models translate between parallel programming languages, achieving 69% compilation success and 33% functional correctness through fine-tuning and compiler-guided repair.

8. MONOCODER: Domain-Specific Code Language Model for HPC Codes and Tasks

   Research Paper
   HPC
   December 18, 2024
   MONOCODER is a smaller, HPC-specific language model that outperforms larger, general-purpose LLMs on HPC code tasks, offering better performance in code generation and comprehension.

9. Combining AI with formal verification for efficient migration of legacy code

   Code Metal Article
   Formal Verification
   December 18, 2024
   Advanced techniques for deploying and optimizing large language models on resource-constrained edge devices without compromising performance.

10. Code migration with formal verification for performance improvement of legacy code

Code Metal Article
Formal Verification
December 20, 2023
Domain-specific languages (DSL) have been a common alternative to general-purpose programming languages to implement domain-specific optimizations and simplify programming of domain-specific hardware.