{
  "id": 15216857,
  "title": "Ship Your Interpreter",
  "url": "https://urgent.news/2026/10/17/ship-your-interpreter",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-17T14:16:33.000Z",
  "source": {
    "name": "Lobsters",
    "slug": "lobsters",
    "url": "https://www.basis.ai/blog/ship-your-interpreters/"
  },
  "original_language": "en",
  "account": "As AI models grow in their ability to detect and manipulate software vulnerabilities, the need for secure coding practices has become increasingly urgent. However, creating verified software that can withstand attacks remains a challenge. One promising approach is using large language models (LLMs) to automatically generate formal semantics - mathematical descriptions of programming languages - which can then be used to prove the correctness of code. In a recent experiment, an LLM was tasked with creating a semantics and proof for a small imperative language called WHILE. The project highlights the potential of LLMs to accelerate verification efforts, but also points out the limitations of this approach. Developing formal semantics is time-consuming and requires expert knowledge, even for well-studied languages. Moreover, relying solely on tests to validate semantics is insufficient, as mistakes can still slip through undetected. A truly robust verification system must include a way to verify the semantics themselves. One promising path forward is to have the LLM prove that its generated semantics match the behavior of an existing interpreter, treating the interpreter as the ultimate source of truth. By compiling the interpreter to an instruction set like RISC-V, whose behavior is formally defined, the LLM can generate readable rules for the language and verify them using a proof assistant like Lean. The experiment demonstrated that this approach can indeed produce a verified semantics for WHILE, though the process is computationally intensive and requires substantial expertise. While the results are promising, scaling this method to larger, more complex languages will require significant advances in automated theorem proving and specification techniques. Nonetheless, the experiment underscores the transformative potential of AI-assisted verification in building more secure software systems.",
  "summary": null,
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}