AGENTIC-SYSTEMSApril 27, 20261 min read9 views

QuantClaw: Precision Where It Matters for OpenClaw

QuantClaw is a dynamic precision routing plugin that reduces cost by up to 21.4% and latency by 15.7% for AI agent systems by intelligently assigning precision based on task complexity.

Computer Science > Artificial Intelligence

Title:QuantClaw: Precision Where It Matters for OpenClaw

View PDF HTML (experimental)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.

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Source: arXiv cs.AI Recent

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