AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices
The paper introduces AgentStop, a method designed to enhance the efficiency of local AI agents by terminating tasks early to save energy. This approach aims to reduce the energy consumption of large language model-based agents while maintaining performance. The findings suggest that predictive early termination can lead to a significant reduction in wasted energy with minimal impact on task utility.
- ▪AgentStop predicts and preemptively terminates tasks unlikely to succeed, reducing wasted energy by 15-20%.
- ▪The method has minimal impact on task performance, with less than a 5% drop in utility.
- ▪Locally deployed AI agents consume more resources compared to traditional single-inference workloads.
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Computer Science > Machine Learning arXiv:2605.15206 (cs) [Submitted on 1 May 2026] Title:AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices Authors:Dzung Pham, Kleomenis Katevas, Ali Shahin Shamsabadi, Hamed Haddadi View a PDF of the paper titled AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices, by Dzung Pham and 3 other authors View PDF HTML (experimental) Abstract:Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based question answering. While remote, cloud-based agents offer scalability and ease of deployment, they raise privacy concerns, depend on network connectivity, and incur recurring API costs.
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