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Author

Fang-Cheng Fu

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Preprint Oct 2026

Coda: Exploiting Admission Flexibility for Coding-Agent Serving

Coding agents powered by large language models (LLMs) repeatedly alternate between model inference and tool calls, creating long-lived sessions with reusable key-value (KV) states and asynchronous request resumptions. Logical readiness, however, does not ensure efficient admission in a shared serving system. Through di...

You-He Jiang, Fang-Cheng Fu, Bin-Hang Yuan et al. · 0 citations
#machine learning Preprint Sep 2026

Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving

As diffusion large language models (dLLMs) become more capable, they are moving from research settings to real-world \textit{serving}, where request management (such as scheduling and resource allocation) relies on accurate estimation of per-request inference cost. However, common cost proxies fall short for dLLMs: out...

Hao-Yu Zheng, Fang-Cheng Fu, Bin-Hang Yuan et al. · 0 citations

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