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LLMs automate sound abstract interpreters for program verification

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SAIL, a framework that uses large language models to automatically generate sound abstract interpreters for program verification. This approach automates a previously manual and tedious process, synthesizing abstract transformers for complex non-linear operators. Evaluations show SAIL can match and even surpass the performance of human-designed transformers, producing sound and precise results for neural network verification. AI

IMPACT Automates complex program verification tasks, potentially improving software reliability and security.

RANK_REASON The cluster contains an academic paper detailing a new framework and methodology for synthesizing abstract interpreters using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs automate sound abstract interpreters for program verification

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The cluster contains an academic paper detailing a new framework and methodology for synthesizing abstract interpreters using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Qiuhan Gu, Avaljot Singh, Gagandeep Singh ·

    SAIL: Sound Abstract Interpreters with LLMs

    arXiv:2511.13663v2 Announce Type: replace-cross Abstract: How to construct globally sound abstract interpreters to safely approximate program behaviors remains a bottleneck in abstract interpretation. In this paper, we show the potential of using state-of-the-art LLMs to automate…

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