{
  "id": 18789,
  "url": "https://arxiv.org/abs/2608.11766v1",
  "title": "Instruction Alignment for Binary Code Representation Learning",
  "summary": "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",
  "authors": "Huaijin Wang, Shuai Wang",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T08:07:26.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18789",
  "original_url": "https://arxiv.org/abs/2608.11766v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}