QuantClaw: Precision Where It Matters for OpenClaw
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914504910045184 |
|---|---|
| author | Zhang, Manyi Li, Ji-Fu Sun, Zhongao Liu, Xiaohao Dong, Zhenhua Yu, Xianzhi Bai, Haoli Xia, Xiaobo |
| author_facet | Zhang, Manyi Li, Ji-Fu Sun, Zhongao Liu, Xiaohao Dong, Zhenhua Yu, Xianzhi Bai, Haoli Xia, Xiaobo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22577 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | QuantClaw: Precision Where It Matters for OpenClaw Zhang, Manyi Li, Ji-Fu Sun, Zhongao Liu, Xiaohao Dong, Zhenhua Yu, Xianzhi Bai, Haoli Xia, Xiaobo Artificial Intelligence Computation and Language 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. |
| title | QuantClaw: Precision Where It Matters for OpenClaw |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2604.22577 |