1 citations · 2 across the 4 of their papers we have counts for
4 papers
Mem-α: Learning Memory Construction via Reinforcement Learning
Yu Wang, Ryuichi Takanobu, Zhiqi Liang +4
Large language model (LLM) agents are constrained by limited context windows, necessitating external memory systems for long-term information understanding. Current memory-augmente…
K2-Think: A Parameter-Efficient Reasoning System
Zhoujun Cheng, Richard Fan, Shibo Hao +28
K2-Think is a reasoning system that achieves state-of-the-art performance with a 32B parameter model, matching or surpassing much larger models like GPT-OSS 120B and DeepSeek v3.1.…
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions
Yuanzhe Hu, Yu Wang, Julian McAuley
Recent benchmarks for Large Language Model (LLM) agents primarily focus on evaluating reasoning, planning, and execution capabilities, while another critical component-memory, enco…
M+: Extending MemoryLLM with Scalable Long-Term Memory
Yu Wang, Dmitry Krotov, Yuanzhe Hu +6
Equipping large language models (LLMs) with latent-space memory has attracted increasing attention as they can extend the context window of existing language models. However, retai…