Temporal Benchmark

Temporal benchmarks are datasets designed to evaluate the performance of models on tasks involving time-dependent data, spanning diverse domains like person re-identification, geometry problem-solving, and spatiotemporal forecasting. Current research focuses on developing more comprehensive and diverse benchmarks that address limitations in existing datasets, particularly regarding handling complex spatial relationships and large temporal changes, and on adapting existing models (like LLMs and deep autoregressive models) or developing novel architectures (such as sparse vector quantization) for improved performance. These advancements are crucial for improving the accuracy and reliability of models across various applications, including weather forecasting, autonomous navigation, and environmental monitoring.