The field of parallel computing and verification is rapidly advancing, with a focus on improving the efficiency and correctness of large-scale systems. One of the key directions is the development of new programming paradigms, such as the data-autonomous paradigm, which enables highly parallel computation by making data elements autonomous. Additionally, researchers are exploring new techniques for verifying the correctness of parallel systems, including the use of formal methods and property-based testing. Another area of interest is the improvement of synchronization methods for concurrent data structures, which is crucial for achieving high performance in parallel systems. Furthermore, advances in dynamic race detection and deadlock-free session types are being made, which will help to ensure the correctness and reliability of parallel programs. Notable papers in this area include the introduction of TrainVerify, a system for verifiable distributed training of large language models, and the development of a sampling-based dynamic race detector that can detect data races in sub-linear time. The Autonomous Data Language and the Deadlock-free Context-free Session Types are also noteworthy, as they provide new approaches to parallel programming and concurrency control.