🚀 Excited to announce the release of our latest research on EverMemOS, now available on arXiv! As Large Language Models (LLMs) transition from simple conversational tools to long-term interactive agents, they face a critical "cognitive wall": limited context windows and fragmented memory. To bridge this gap, we introduced EverMemOS—a self-organizing memory operating system that transforms isolated interaction fragments into a structured, evolving "digital brain". By implementing an engram-inspired lifecycle—covering Episodic Trace Formation, Semantic Consolidation, and Reconstructive Recollection—EverMemOS doesn't just store data; it organizes experience. We are thrilled to report that EverMemOS has achieved State-of-the-Art (SOTA) results across four major long-term memory benchmarks: LoCoMo: Outperformed all existing memory systems and even full-context large models, while using drastically fewer tokens (93.05% overall accuracy). LongMemEval: Achieved a leading 83.00% accuracy, showing particularly strong gains in Knowledge Updates and temporal reasoning. HaluMem: Set a new standard for memory integrity and accuracy (90.04% recall). PersonaMem v2: Demonstrated superior performance in deep personalization and behavioral consistency across diverse scenarios. These results validate our belief that the future of AI lies in structured memory organization rather than just expanding context windows. Special thanks to the amazing team at EverMind Shanda Group for their hard work on this milestone! Check out the full paper on arXiv: Explore our code on GitHub: #AI# #LongTermMemory# #LLM# #MachineLearning# #EverMemOS# #AIInfra# #SOTA#