QuantClaw: Precision Where It Matters for OpenClaw

arXiv cs.AI / 4/27/2026

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Key Points

The paper studies how quantization affects the performance of the OpenClaw autonomous agent across realistic, multi-turn and long-context workflows, finding that precision needs vary significantly by task.
It introduces QuantClaw, a plug-and-play “precision routing” plugin that dynamically selects lower-cost precision for lightweight tasks and retains higher precision for demanding ones.
Experiments on GLM-5 with an FP8 baseline show QuantClaw can reduce latency and computational cost while maintaining or improving task performance.
Reported results include up to 21.4% cost savings and 15.7% latency reduction across a range of agent tasks, illustrating precision as a dynamic resource for agent systems.

Abstract

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.