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cs.AI2026
Learning Agent Execution for KV-Cache Management in Agentic Serving
Rui Zhang, Chaeeun Kim, Shaoting Feng +6
Multi-agent LLM systems have emerged as an important deployment paradigm for AI services, where each user request is decomposed into a sequence of specialized agents. Across these…
cs.AI2026
Are LLM-Generated GPU Kernels Production-Ready? A Trace-Driven Benchmark and Optimization Agent
Lingyun Yang, Yuxiao Wang, Shenghao Liang +8
Existing GPU kernel generation benchmarks draw problems from synthetic or curated sources that diverge from deployed workloads. We present Atrex-Bench, a benchmark whose 30 operato…
cs.AI2026
A Policy-Driven Runtime Layer for Agentic LLM Serving
Rui Zhang, Chaeeun Kim, Liting Hu
Multi-agent LLM systems have become the dominant production workload, but the serving stack was not built for them. The agent framework above knows agent identities, role, schemas,…