PalmClaw: Native On-Device Agent Framework Boosts Efficiency by 94.9%
Score
75提出原生手机Agent设计，与主流GUI操作方案形成明显差异，提供了新的技术视角。
实验设计较为完整，包含多任务对比和量化指标，但缺少在真实用户场景的长期评估。
直接降低延迟和资源消耗，开源代码便于开发者集成，对移动端自动化有显著实用价值。
发布于2026年7月，属于最新前沿研究，且正值端侧Agent热潮。
来自arXiv预印本，尚未经同行评议，但代码开源可验证，属于学术领域常见信源。
Base 83 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
The framework addresses the core pain point of GUI-dependent mobile agents, with solid experimental results and open-source code, though further validation in real-world scenarios is needed.
AI commentary
「This paper stands out by rethinking the mobile agent interaction paradigm at the system level, not just GUI simulation. For phone manufacturers and app developers, PalmClaw shows a more efficient path for on-device agent deployment and is worth paying attention to.」
Quick overview
PalmClaw is an open-source agent framework running natively on mobile phones, exposing device capabilities as structured tools so that LLM agents can directly invoke mobile functions instead of GUI-based sequences. Experiments show an 11.5% relative improvement in task success and a 94.9% reduction in completion time over the strongest baseline, with lower setup overhead. The framework defines clear execution boundaries, making each action explicit and controllable, offering a new paradigm for on-device agent deployment.
- PalmClaw runs natively on phones, managing sessions, memory, skills, tools, and the agent loop locally.
- Device capabilities are exposed as tools with explicit arguments, structured results, and clear execution boundaries.
- Achieves 11.5% relative improvement in task success and 94.9% reduction in completion time over GUI-based baselines.
- Open-source code is available on GitHub.
Full text
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