Frequency-Aware Continual Learning for Smart Contract Vulnerability Detection with Large Language Models
Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges. First, new vulnerability categories demand parameter-efficient adaptation, since full retraining is prohibitive for sequentially arriving tasks. Second, training per-task adapters on a shared backbone causes catastrophic forgetting of previously learned vulnerabilities. Third, the resulting multiplicity of adapters must be consolidated into a single model, since task identity is unknown at inference time. Each challenge arises directly from the solution to its predecessor, making an integrated framework essential. We propose a three-stage pipeline in which each stage addresses ...
| Authors | Tenghui Huang, Jiawen Kang, Dongning Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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