Core Insights - The article discusses advancements in linear attention mechanisms, particularly DeltaNet, which aims to improve the efficiency and effectiveness of large language models (LLMs) by reducing the computational complexity associated with traditional attention mechanisms [5][10][12]. Group 1: Linear Attention Mechanisms - Linear attention mechanisms, such as DeltaNet, were introduced to address the computational bottleneck of traditional attention mechanisms, which exhibit quadratic complexity with respect to input length [5][12]. - DeltaNet's development has been a collaborative effort, with significant contributions from researchers since its inception in 2021, focusing on improving the update rules and parallelization of linear attention [7][20][21]. - The recent open-source releases of Qwen3-Next and Kimi Linear models by Alibaba and Kimi, respectively, incorporate linear attention mechanisms, indicating a shift towards these more efficient models in flagship applications [5][24]. Group 2: DeltaNet and Its Evolution - DeltaNet was initially overlooked due to a lack of key architectural improvements and suboptimal implementations, but recent advancements have led to its increased adoption in industry [20][24]. - The introduction of the Gated DeltaNet variant enhances memory control and retrieval performance, making it more suitable for modern hardware [7][21][24]. - The relationship between DeltaNet and other models, such as Kimi Linear, highlights the trend of integrating linear attention with traditional full attention mechanisms to balance speed and capacity [24][25]. Group 3: Future Directions and Challenges - The article emphasizes the need for further exploration of update rules in linear attention mechanisms, suggesting that improvements in this area could lead to better performance and scalability [48][49]. - There is a discussion on the potential of combining sparse attention with linear attention to address long-text processing challenges, which remains a significant hurdle in current models [46][49]. - The ongoing debate in the industry regarding the effectiveness of linear versus full attention mechanisms reflects the complexities and trade-offs involved in model design for various applications [27][30].