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To maintain high accuracy without massive parameters, we implement Rank-Stabilized Low-Rank Adapters (rsLoRA), knowledge distillation, and a custom Chain-of-Verification (CoVe) aggregation strategy to systematically screen and consolidate multiple draft respons","title":"Decoupled Smart Contract Audits: Lightweight LLM Framework via Distillation and Aggregation","url":"https://arxiv.org/abs/2606.03128","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.03128v1 Announce Type: cross \nAbstract: Smart contracts face critical security challenges that require thorough auditing in decentralized web services. While Large Language Models (LLMs) have shown promise in automated vulnerability detection, existing approaches lack severity evaluations with actionable remediation and demand unnecessarily massive computational overhead. In this study, we introduce an efficient end-to-end smart contract security audit framework utilizing lightweight, highly optimized open-source LLMs (0.6B-4B parameters). 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