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[2511.03475] ContextPilot: Fast Long-Context Inference via Context Reuse
Summary
ContextPilot introduces a novel approach to accelerate long-context inference in AI, enhancing reasoning quality while reducing latency through context reuse techniques.
Why It Matters
As AI applications increasingly rely on long-context inference, optimizing prefill latency is crucial for improving performance. ContextPilot addresses this challenge by balancing speed and reasoning quality, making it significant for developers and researchers in machine learning and natural language processing.
Key Takeaways
- ContextPilot accelerates long-context inference by reusing context effectively.
- The system maintains reasoning quality while reducing prefill latency by up to 3x.
- Innovative techniques include context indexing, ordering, and succinct annotations.
- ContextPilot is modular and integrates seamlessly with existing inference engines.
- The approach is open-sourced, promoting further research and development.
Computer Science > Machine Learning arXiv:2511.03475 (cs) [Submitted on 5 Nov 2025 (v1), last revised 23 Feb 2026 (this version, v3)] Title:ContextPilot: Fast Long-Context Inference via Context Reuse Authors:Yinsicheng Jiang, Yeqi Huang, Liang Cheng, Cheng Deng, Xuan Sun, Luo Mai View a PDF of the paper titled ContextPilot: Fast Long-Context Inference via Context Reuse, by Yinsicheng Jiang and 5 other authors View PDF HTML (experimental) Abstract:AI applications increasingly depend on long-context inference, where LLMs consume substantial context to support 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. We present ContextPilot, a system that accelerates prefill by introducing context reuse as a new mechanism for faster long-context inference. ContextPilot introduces a context index to identify overlapping context blocks across LLM interactions (e.g., across users and turns). It further proposes context ordering and de-duplication techniques to maximize KV-cache reuse. To preserve reasoning quality under reuse, it introduces succinct context annotations that prevent quality degradation. Finally, ContextPilot is built around a mo...