Author

Kaihua Fu

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Preprint Sep 2026

Capture the lifecycle: KV Cache management in ReAct Agents with KVTether

Efficient serving of long-context reasoning-and-acting (ReAct) agents relies on KV cache reuse to reduce large language model (LLM) prefill latency and monetary cost. However, a semantic gap exists between agent harnesses and the underlying serving stack. Through context mutation, tool execution, and subagent coordinat...

Kai-Hua Fu, Yu-Kun Zhou, Chaokun Chang et al. · 0 citations

Preprint Aug 2026

From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems

This work presents AgentSysBench, a benchmark suite and measurement toolkit with ten representative agentic applications and unified systems-level instrumentation, and identifies six properties that distinguish agentic workloads from conventional LLM serving.

Chaokun Chang, Yu-Kun Zhou, Kai-Hua Fu et al. · 11 citations