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This paper proposes a frequency-aware continual learning framework using large language models for smart contract vulnerability detection, addressing adaptation, forgetting, and consolidation challenges with efficient methods like FA-LoRA and APPM.
Introduces CamoNAS, a frequency-aware multi-resolution Neural Architecture Search framework for camouflaged object detection, achieving state-of-the-art results on four benchmarks.
FlowRAG proposes a novel semantic-aware retrieval framework that constructs a quad-level heterogeneous graph and uses frequency-aware weighted flow to extract explicit reasoning paths, achieving state-of-the-art performance on complex reasoning benchmarks.