{"slug": "frequency-aware-continual-learning-for-smart-contract-vulnerability-detection", "title": "Frequency-Aware Continual Learning for Smart Contract Vulnerability Detection with Large Language Models", "summary": "Researchers propose a three-stage pipeline for smart contract vulnerability detection using large language models, achieving a Micro-F1 of 0.8085 with only 0.4% trainable parameters. The framework, tested on the DIVE dataset, addresses catastrophic forgetting and adapter consolidation, with a merge cost of 156 ms and no additional runtime memory.", "body_md": "arXiv:2608.19680v1 Announce Type: new\nAbstract: 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 one challenge and feeds into the next. The adaptation stage uses Frequency-Aware Low-Rank Adaptation (FA-LoRA), which performs adaptation in the Fourier domain with per-frequency importance gates, requiring only 0.4% trainable parameters while outperforming standard LoRA and QLoRA. The continual learning stage applies Forget-Aware Replay (FAR), which uses these frequency gates to estimate per-sample forgetting risk via loss dynamics and prioritizes vulnerable knowledge for rehearsal, achieving an average Micro-F1 of 0.8022 across sequential tasks. The deployment stage employs Anchor-Protected Progressive Merging (APPM), which exploits the asymmetric generalization produced by FAR training to identify the strongest-generalizing adapter as an anchor and consolidates all adapters into a single model via anchor-protected weighted merging with frequency-domain gate competition. APPM achieves a Micro-F1 of 0.8085, within 2.7% of the independent per-task upper bound, at a merge cost of 156 ms and no additional runtime memory. Experiments on DIVE confirm the framework effectively addresses all three challenges for evolving blockchain ecosystems.", "url": "https://wpnews.pro/news/frequency-aware-continual-learning-for-smart-contract-vulnerability-detection", "canonical_source": "https://arxiv.org/abs/2608.19680", "published_at": "2026-08-21 04:00:00+00:00", "updated_at": "2026-08-21 04:12:49.790435+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research"], "entities": ["arXiv", "FA-LoRA", "FAR", "APPM", "DIVE"], "alternates": {"html": "https://wpnews.pro/news/frequency-aware-continual-learning-for-smart-contract-vulnerability-detection", "markdown": "https://wpnews.pro/news/frequency-aware-continual-learning-for-smart-contract-vulnerability-detection.md", "text": "https://wpnews.pro/news/frequency-aware-continual-learning-for-smart-contract-vulnerability-detection.txt", "jsonld": "https://wpnews.pro/news/frequency-aware-continual-learning-for-smart-contract-vulnerability-detection.jsonld"}}