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News/Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs
arxiv
PublishedJune 24, 2026 at 4:00 AM
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Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs

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Publisher summary· verbatim

arXiv:2603.15510v2 Announce Type: replace Abstract: The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification. While Large Language Models (LLMs) show promise in mitigating this issue, they often fail on complex programs, producing invariants that are

Models mentioned
01
  • 01meta-llama logo
    Llama-3.1
    meta-llama/Llama-3.1
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    Llama-3.1
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  • 04
    GPT-OSS-120B
  • 05
    GPT-5.2
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#program-verification#large-language-models#fine-tuning#automated-verification
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Mentioned models
05
  • 01
    Qwen3
  • 02
    Llama-3.1
    meta-llama/Llama-3.1
  • 03
    Mistral
  • 04
    GPT-OSS-120B
  • 05
    GPT-5.2
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#program-verification#large-language-models#fine-tuning#automated-verification
Mentioned companies
01
GitHub

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