{
  "id": 4399,
  "url": "https://arxiv.org/abs/2605.13229v1",
  "title": "Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization",
  "summary": "LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While preference-based learning offers a promising alignment strategy, it is hindered by unreliable semantic rewards derived from sparse test cases or restrictive reference translations. We argue that a robust semantic reward for code translation must be derived directly from the source code. In this paper, we propose CTO to improve code translation with ",
  "authors": "Yuhan Wu, Huan Zhang, Wei Cheng, Chen Shen, Jingyue Yang, Wei Hu",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-13T09:19:39.000Z",
  "fetched_at": "2026-07-14T16:30:59.236Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4399",
  "original_url": "https://arxiv.org/abs/2605.13229v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}