Autonomous agents powered by foundation models can reason, plan, and use tools, but their fixed weights and context limits hinder sustained learning from experience over long-term deployment. While external memory systems attempt to bridge this gap, they mostly act as passive retrieval caches, evaluated primarily by wh...
Dongfang Li, Zhuo-En Chen, Bo-Nian Jia et al.· 0 citations
Prefix caching is critical for efficient large language model (LLM) serving, particularly for agentic workloads that repeatedly invoke the model with a growing conversation and tool-use history. By reusing the key-value (KV) states of previously processed prefixes, prefix caching avoids redundant prefill computation. I...
Lu-Chang Li, Shuai-Shuai Wang, Zhao Ruan et al.· 0 citations
The results suggest that the accuracy--cost trade-off of long-term agent memory depends not only on what information is retained, but also on the granularity at which it is consolidated, and that the accuracy--cost trade-off of long-term agent memory depends not only on what information is retained, but also on the gra...
Dongfang Li, Zi-Xuan Liu, Junmai Wang et al.· 3 citations
This work demonstrates a full-stack pathway from efficient trillion-parameter model post-training on Ascend infra to domain-specialized Flash models for solver-grounded mathematical modeling, advancing frontier-model systems for complex reasoning.
Dongfang Li, Xiaodong Luo, Ruoyu Sun et al.· 0 citations
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