{
  "id": 4306,
  "url": "https://arxiv.org/abs/2605.15334v1",
  "title": "From I/O to Code with Discovery Agent",
  "summary": "The automatic synthesis of a program from any form of specification is regarded as a holy grail of computer science. Fueled by LLMs, NL2Code has achieved tremendous success, yet the fundamentally more challenging task of synthesizing programs from input-output behavior, which we refer to as IO2Code, remains largely unsolved. Whereas NL2Code can exploit the semantic alignment between natural language and code acquired during pretraining, IO2Code requires recovering underlying principles from conc",
  "authors": "Yihong Dong, Jiaru Qian, Haoran Zhang, Peixu Wang, Binhua Li, Zhi Jin et al.",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-14T18:57:32.000Z",
  "fetched_at": "2026-07-14T16:30:54.920Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4306",
  "original_url": "https://arxiv.org/abs/2605.15334v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}