机器之心·2025-12-14 01:30Group 1 - The article discusses the concept of "Memory as a Context" and its potential to redefine the memory mechanisms of Transformers, addressing the limitations of current LLM memory capabilities [6][8]. - Google's Titans architecture introduces a neural long-term memory module that allows for online learning and optimization during testing, marking a shift from passive data storage to active learning [7][8]. - The Titans framework includes three architectural variants: "Memory as a Context," "Memory as a Gate," and "Memory as a Layer," each representing different approaches to integrating memory capabilities with Transformer models [7][8]. Group 2 - The article highlights the evolution of LLM memory mechanisms from static caches to adaptive test-time learning systems, enabling models to adjust memory strategies dynamically based on task requirements [9][10]. - A review of the past seven years of research on core memory operations—reading, writing, forgetting, and capacity management—reveals the limitations of static caching mechanisms and recent advancements in improving these operations [10]. - The research emphasizes the importance of selective writing, real-time decision-making, and adaptive resource allocation in enhancing the memory capabilities of Transformers [10].