4 citations · 7 across the 11 of their papers we have counts for
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cs.CL2026
Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment
Zhuo Zuo, Li Yue, Wenhao Zheng +2
Despite their strong general capabilities, large language models (LLMs) often remain unreliable when outputs must be numerically precise. A key reason is the training objective: st…
cs.DC2026
LAPS: A Length-Aware-Prefill LLM Serving System
Jianshu She, Zonghang Li, Hongchao Du +7
LAPS identifies and disaggregates requests with different prompt lengths in LLM serving to reduce TTFT latency. While recent systems have decoupled the prefill and decode stages to…