QuantClaw: Precision Where It Matters for OpenClaw

2026-04-24

AI Summary

This work addresses the high computational and economic costs of existing autonomous agent systems—such as OpenClaw—stemming from long-context processing and multi-turn reasoning, where conventional uniform quantization often degrades performance on complex tasks. To overcome this limitation, we propose QuantClaw, a plug-and-play dynamic precision routing plugin that, for the first time, reveals significant variation in task sensitivity to quantization. Treating numerical precision as a schedulable resource, QuantClaw dynamically allocates varying precisions (e.g., FP8) based on task characteristics, preserving higher precision along critical execution paths. Experiments demonstrate that QuantClaw reduces inference cost by up to 21.4% and latency by 15.7% on GLM-5, while maintaining or even improving task performance.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• quantization
• autonomous agents
• precision
• computational cost
• latency

Innovation

• quantization
• precision routing
• agent systems
• cost efficiency
• dynamic precision