1 citations · 1 across the 12 of their papers we have counts for
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MemAgent: Learning to Manage Heterogeneous Memory Providers for LLM Agents
Yongxian Wei, Yilin Zhao, Runxi Cheng +5
Current agents remain largely stateless across tasks, limiting their ability to continually improve from prior interactions and making memory essential for long-horizon agentic beh…
Closed-Form Spectral Regularization for Multi-Task Model Merging
Yongxian Wei, Runxi Cheng, Xingxuan Zhang +4
Model merging combines several independently fine-tuned experts into a single multi-task model without any training data, reducing the storage, serving, and decentralized-developme…
Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory
Runxi Cheng, Yuchen Guan, Yongxian Wei +7
Scaling conditional memory offers a promising way to increase language-model capacity, but existing methods such as Engram learn large memory tables from scratch during pre-trainin…
Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse
Chi Zhang, Mengqi Zhang, Xiaotian Ye +5
Sequential knowledge editing in large language models often causes catastrophic collapse of the model's general abilities, especially for parameter-modifying methods. Existing appr…