arXiv Machine Learning October 16, 2026 By Eric Wolos, Michael Doyle

Identifier-Free Code Embedding Models for Scalable Search

Read the original on arXiv Machine Learning →

The paper introduces Identifier-Free Code Embedding Models designed to improve function association for binary reverse engineering. It formalizes the bidirectional association problem between source code and decompiled, stripped code, and evaluates embedding models on this task. By fine‑tuning a Qwen3‑Embedding model with contrastive learning, the authors achieve significant performance gains over existing baselines and demonstrate generalization to a constant‑algorithm association task.

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