Blogs

We present Memory Sparse Attention (MSA): an end-to-end trainable, scalable sparse latent-state memory framework.

We have released our latest research on EverOS, now available on arXiv! Large Language Models are quickly evolving from “single turn chatbots” into long-term interactive agents. But as soon as an agent is expected to stay coherent across weeks of conversations, it runs into a practical ceiling: a limited context window and fragmented memory. Even with retrieval, many systems still behave like they are pulling isolated snippets—often missing conflicts, failing to update user state, or giving inconsistent guidance over time. In our latest research, we introduce EverOS, a self-organizing memory operating system that treats memory not as a flat store, but as a lifecycle—inspired by biological “engram” principles—so agents can continuously transform raw interactions into structured, evolving knowledge.

Using a unified, production-grade evaluation framework, we benchmarked leading memory systems — EverOS, Mem0, MemOS, Zep, and MemU — under the same datasets, metrics, and answer model. This framework provides a fair, transparent, and reproducible standard for evaluating real-world memory performance in the Agentic Era. And EverOS delivered best-in-class results across LoCoMo and LongMemEval.

EverOS is an intelligent memory operating system designed to give AI the ability not just to remember, but to understand, reason, and evolve. On the LoCoMo benchmark, our approach built upon EverOS achieved a 92.3% reasoning accuracy (evaluated by LLM-Judge), outperforming comparable methods in our internal evaluation.