LLM-Based Embeddings for Program Analysis and Optimization

arXiv:2608.07894 · cs.LG, cs.PL · Submitted 2026-08-08 · Read on arXiv

Calvin Higgins, Marco Alvarez

cs.LG, cs.PL

Submitted: 2026-08-08

Updated: 2026-08-11

Journal ref: 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-8

DOI: 10.1109/IJCNN64981.2025.11227194

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embeddings — 39× in this paper · explained in 2 episodes
• Accuracy — 21× in this paper · explained in 1 episode
• Ablation — 8× in this paper · explained in 1 episode
• Decoder — 7× in this paper · explained in 2 episodes
• GNNs — 6× in this paper · explained in 2 episodes
• Hyperparameters — 5× in this paper · explained in 2 episodes
• Language Models (LLMs) — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 9 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Overfitting — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Conference on Compiler Construction — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Contextual Understanding — 1× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 8 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 13 episodes
• International Conference on Learning Representations — 1× in this paper
• International Conference on Machine Learning — 1× in this paper
• Knowledge Distillation — 1× in this paper · explained in 15 episodes
• Multimodal approach — 1× in this paper · explained in 2 episodes
• Parallel Architectures and Compilation Techniques — 1× in this paper
• Performance Gap — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 15 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 16 episodes

Sources

• Code Llama: Open Foundation Models for Code

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