Brief

Researchers have introduced STM3, a novel Mixture-of-Experts framework designed to enhance long-term spatio-temporal time-series prediction. This approach integrates a Multiscale Mamba architecture with a Disentangled Mixture-of-Experts (DMoE) to efficiently capture diverse multiscale information. STM3 also employs an adaptive graph causal network to model complex spatial dependencies and uses a stable routing strategy with causal contrastive learning for robust representation. Experiments on ten real-world benchmarks show STM3 achieving state-of-the-art results, outperforming previous models significantly on datasets like PEMSD8. AI

Researchers have developed AutoMCU, a novel system that leverages LLM-based multi-agent approaches to customize neural networks for microcontroller units (MCUs). This method prioritizes feasibility by integrating vendor toolchain feedback early in the design process, significantly reducing the search cost and time compared to traditional hardware-aware neural architecture search methods. AutoMCU has demonstrated competitive accuracy on benchmark datasets and successful deployment on STM32 microcontrollers, making edge intelligence more accessible. AI