The field of deep search agents and large language models is rapidly evolving, with a focus on developing more sophisticated and autonomous systems. Recent developments have seen the introduction of multimodal deep search agents, such as WebWatcher, which can comprehend visual information and execute multi-turn retrieval with dynamic planning. Additionally, benchmarks like BrowseComp-Plus and DatasetResearch have been proposed to evaluate the performance of deep research agents and dataset discovery systems. These benchmarks have highlighted the limitations of current systems and the need for more advanced architectures and training methods. Notably, the K-Dense Analyst system has achieved state-of-the-art performance on the BixBench benchmark, demonstrating the potential for autonomous bioinformatics analysis. Furthermore, the development of open-source frameworks like OpenCUA and OdysseyBench is expected to accelerate research in this area. Some noteworthy papers include WebWatcher, which introduces a multi-modal agent for deep research with enhanced visual-language reasoning capabilities, and K-Dense Analyst, which achieves autonomous bioinformatics analysis through a hierarchical multi-agent system.