## Introduction Researchers have presented a new framework for fine-tuning large pre-trained language models, termed Fourier-Activated Adapter (FAA). This framework uses Fourier functions to optimize performance and reduce energy consumption. ## How it works The FAA decomposes intermediate representations into low- and high-frequency components, enabling frequency-aware modulation of semantic information. This design allows the model to selectively emphasize informative frequency bands during adaptation while preserving the representational capacity of the frozen backbone. ## Experiments and results Experiments conducted on GLUE, E2E NLG, and instruction-tuning benchmarks have demonstrated that FAA achieves competitive or superior performance compared to existing parameter-efficient fine-tuning methods, while maintaining low computational and memory overhead. ## Ablation studies Ablation studies further verify the effectiveness of frequency-aware activation and adaptive weighting mechanisms, highlighting FAA as a robust and efficient approach for post-training large language models.