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
Leveraging the paradigm of autoregressive diffusion, text-conditioned interactive video world models aim to simulate temporally coherent environments guided by textual instructions. While enabling low-latency, long-duration generation is pivotal for embodied AI and simulation-based planning, current frameworks primaril...
Ze-Yu Zhang, Jin-Yuan Mao, Da-Kai An et al.· 0 citations
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
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