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cs.LG2025
Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs
Hao Kang, Qingru Zhang, Han Cai +4
Large language models (LLMs) have shown remarkable performance across diverse reasoning and generation tasks, and are increasingly deployed as agents in dynamic environments such a…
cs.LG2023
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning
Bowen Cao, Qichen Ye, Weiyuan Xu +1
Learning representations for graph-structured data is essential for graph analytical tasks. While remarkable progress has been made on static graphs, researches on temporal graphs…