KV cache as an agent runtime [R]
一个研究团队正在探索将KV缓存作为代理运行时，以提高大型语言模型的交互性和响应能力。这种方法，在之前的论文如“Hogwild! Inference”和“AsyncReasoning”中有所阐述，涉及修改模型的推理状态。未来的工作包括一个Qwen3.8-27B代理使用类似技术交互式地玩DOOM环境，这表明模型推理/运行时设计可能是代理能力中一个未被充分探索的方面。
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- 2026年9月7日 09:03
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Our research team has been exploring an alternative approach to achieving interactivity and better responsiveness with LLM systems.
One of the team members wrote up a post about it: https://research.yandex.com/blog/the-kv-cache-as-an-agent-runtime
The post sums up the overall idea of modifying models inference state (KV-cache) for achieving a more interactive LLMs. This idea was used in our lab's previous papers Hogwild! Inference, and AsyncReasoning, the post also contains a preview of the future work in this direction, where a Qwen3.8-27B agent is playing a DOOM env interactively using similar techniques.
We think that its interesting whether model inference/runtime design is itself an under-explored axis of agent capabilities, alongside models and the harness (e.g. harness is too abstract, changing model is too costly, do we need something in between?)