AI RESEARCH
ContextPilot: Fast Long-Context Inference via Context Reuse
arXiv CS.LG
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ArXi:2511.03475v4 Announce Type: replace AI applications increasingly depend on long-context inference, where LLMs consume substantial context to stronger reasoning. Common examples include retrieval-augmented generation, agent memory layers, and multi-agent orchestration. As input contexts get longer, prefill latency becomes the main bottleneck. Yet today's prefill acceleration techniques face a trade-off: they either preserve reasoning quality but deliver little KV-cache reuse, or improve reuse at the cost of degraded reasoning quality.