LLM-Based Embeddings for Program Analysis and Optimization

• URL: http://arxiv.org/abs/2608.07894v1
• Date: Sat, 08 Aug 2026 03:41:59 GMT
• Title: LLM-Based Embeddings for Program Analysis and Optimization
• Abstract summary: We present the first application of program embeddings from Large Language Models (LLMs)<n>We generate program embeddings directly from source and IR code using a simple approach.<n>Experiments show that combining source and IR code embeddings achieves an error rate of 1.54% in algorithm classification.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances have highlighted the potential of machine learning, particularly Large Language Models (LLMs), for analyzing and optimizing programs. We present the first application of program embeddings from LLMCompiler---an LLM massively pretrained on intermediate representation (IR) code---to representative program analysis and optimization tasks. We generate program embeddings directly from source and IR code using a simple approach: split programs into chunks, independently embed each chunk with pretrained LLMs, and then aggregate the chunk embeddings into a single program embedding. Our experiments show that combining source and IR code embeddings achieves an error rate of 1.54\% in algorithm classification, a 12\% improvement over the current state-of-the-art, and a competitive accuracy on heterogeneous device mapping. These findings suggest that training a performance-aware LLM for embedding IR code might yield state-of-the-art results in code optimization tasks.

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