Instruction Alignment for Binary Code Representation Learning
Binary code representation learning is a fundamental problem in software security and reverse engineering. Existing methods mainly learn function-level embeddings that capture coarse-grained semantic relationships between binary functions, but they largely ignore fine-grained instruction-level correspondences. This limitation misses valuable supervision signals available from compiler debug information, which can support the learning of more accurate and interpretable binary code representations
Record details
Published: 12 August 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 13 August 2026
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ethics.ai (12 August 2026), “Instruction Alignment for Binary Code Representation Learning,” evidence record 18789, https://ethics.ai/record/18789 (originally published by arXiv).
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