The field of artificial intelligence and signal processing is moving towards developing more secure and efficient models. Researchers are focusing on protecting large language models from unauthorized merging and developing innovative techniques for real-time audio signal processing. Notable advancements include the development of defense mechanisms that prevent model merging stealing and the creation of lightweight models for speech enhancement and music source separation. These advancements have the potential to improve the performance and security of AI models in various applications.

Some noteworthy papers in this area include: Do Not Merge My Model, which proposes a plug-and-play defense mechanism to prevent unauthorized model merging. Defending Unauthorized Model Merging via Dual-Stage Weight Protection, which presents a proactive dual-stage weight protection framework to disrupt merging compatibility while maintaining task fidelity. Real-Time Speech Enhancement via a Hybrid ViT, which introduces a novel transformer-based learning framework for real-time speech enhancement. Towards Practical Real-Time Low-Latency Music Source Separation, which proposes a lightweight real-time low-latency model for music source separation. Zipf-Gramming, which develops a new top-k n-gram extractor that is up to 35 times faster than the previous best alternative. IMSE, which proposes an ultra-lightweight network for speech enhancement using Inception Depthwise Convolution and Amplitude-Aware Linear Attention. Intermediate N-Gramming, which devises a multi-pass algorithm for accurately and quickly recovering the top-k most frequent n-grams.