A recent analysis on CSDN reveals the research roadmap of 360 AI Research Institute through six papers accepted at top conferences in the first half of 2026. The line begins with MoSA (Mixture of Sparse Attention), a novel attention mechanism designed to reduce computational cost while maintaining performance, and extends into multi-modal learning, efficient fine-tuning, and domain-specific adaptations. The papers cover topics such as sparse attention for long-context models, cross-modal representation learning, and lightweight architectures for edge deployment. This collection is significant because it shows a coherent research strategy from a major Chinese AI lab, moving beyond individual contributions to a systematic exploration of efficiency and multi-modality. For overseas AI researchers and engineers, understanding this roadmap can inform collaboration opportunities, benchmark comparisons, and awareness of emerging techniques that may influence the global AI landscape. The post itself is a summary and analysis, not a reproduction of the papers, making it a valuable signal without copyright concerns.
This post deconstructs six top-tier conference papers from 360 AI Research Institute in the first half of 2026, starting from MoSA (Mixture of Sparse Attention) and covering a research line. It offers a rare glimpse into the strategic focus of a major Chinese AI lab, including multi-modal and efficient model innovations. For overseas researchers, this signals emerging directions and potential collaboration areas.