AI RESEARCH
VeriScale: Adversarial Test-Suite Scaling for Verifiable Code Generation
arXiv CS.AI
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ArXi:2605.22368v1 Announce Type: cross As large language models (LLMs) are increasingly deployed for software engineering, constructing high-quality benchmarks is crucial for evaluating not just the functional correctness, but also the formal verifiability of generated code. However, existing benchmarks are limited by the quantity and quality of positive and negative test cases, leading to an overestimation of model capabilities in generating specifications and implementations. To address this, we propose VeriScale, a novel framework driven by the adversarial implementations.