由于长上下文输入和多轮推理，OpenClaw 等自主代理系统带来了巨大的效率挑战。这导致现实世界开发中的计算和货币成本过高。虽然量化是降低成本和延迟, 的标准方法，但其在实际场景中对代理性能的影响仍不清楚。在这项工作, 中，我们分析了 OpenClaw, 上不同复杂工作流程的量化灵敏度，并表明精度要求高度依赖于任务。基于这一观察,，我们提出了 QuantClaw, 一个即插即用的精度路由插件，可以根据任务特征动态分配精度。 QuantClaw 将轻量级任务路由到更低成本的配置，同时为要求苛刻的工作负载保持更高的精度, 节省成本并加速推理，而不会增加用户复杂性。实验表明，我们的 QuantClaw 可以保持或提高任务性能，同时减少延迟和计算成本。在一系列代理任务, 上，它在 GLM-5 (FP8 基准) 上实现了高达 21.4% 的成本节省和 15.7% 的延迟减少。这些结果凸显了将精度视为代理系统中的动态资源的好处。
Autonomous agent systems such as OpenClaw introduce significant efficiency challenges due to long-context inputs and multi-turn reasoning. This results in prohibitively high computational and monetary costs in real-world development. While quantization is a standard approach for reducing cost and latency, its impact on agent performance in realistic scenarios remains unclear. In this work, we analyze quantization sensitivity across diverse complex workflows over OpenClaw, and show that precision requirements are highly task-dependent. Based on this observation, we propose QuantClaw, a plug-and-play precision routing plugin that dynamically assigns precision according to task characteristics. QuantClaw routes lightweight tasks to lower-cost configurations while preserving higher precision for demanding workloads, saving cost and accelerating inference without increasing user complexity. Experiments show that our QuantClaw maintains or improves task performance while reducing both latency and computational cost. Across a range of agent tasks, it achieves up to 21.4% cost savings and 15.7% latency reduction on GLM-5 (FP8 baseline). These results highlight the benefit of treating precision as a dynamic resource in agent systems.
科目: 人工智能 (cs.AI); 计算和语言 (cs.CL)
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)