MemT-1.0
🧠 我的角色：技术顾问，负责模型的强化学习部分
开源一周下载 10 万+
MemT-1.0 将记忆作为模型固有的计算能力而非外部检索：通过动态局部记忆与可训练的超记忆（hyper memory），在固定大小的持久记忆状态中沉淀历史交互，推理时无需重读原始文本即可跨会话调用过往信息。
核心成果：
- ⚡ 在 128K 历史长度下，Nano 模型相比全上下文 KV 缓存实现约 1.86 倍端到端推理加速与 66 倍持久化存储压缩
- 📈 在参数化记忆基线上平均领先，与检索增强方法持平，并基本保持通用能力
- 📦 2026 年 10 月开源 Nano（36B-A3B）与 Flash（288B-A17B）两个模型及 66 页技术报告，发布一周下载量突破 10 万次
Authors
Haotian Chen
(he/him)
Research Assistant Professor · LLM Post-Training / AI4AI
I am a Research Assistant Professor at the School of Artificial Intelligence, Shanghai Jiao Tong University, where I work with Prof. Junchi Yan at RethinkLab. I study how to build AI systems that can automate long-horizon, effort-intensive, and creativity-demanding tasks such as research, engineering, and development. My current work focuses on LLM post-training for autonomous agents, agent memory and continual learning, and AI4Research / AI4AI. Before joining SJTU, I received my PhD in Data Science from Fudan University, advised by Prof. Xiangdong Zhou, and completed postdoctoral research at Tsinghua University (THUNLP), working with Prof. Zhiyuan Liu and Prof. Maosong Sun. I was also a research intern at the Machine Learning Research Group of Microsoft Research Asia, mentored by Xiao Yang, and at the Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, working with Prof. Yang Yu.