Web agents, like OpenAI's Operator and Google's Project Mariner, are powerful agentic systems pushing the boundaries of Large Language Models (LLM). They can autonomously interact with the internet at the user's behest, such as navigating websites, filling search masks, and comparing price lists. Though web agent research is thriving, induced sustainability issues remain largely unexplored. To highlight the urgency of this issue, we provide an initial exploration of the energy and $CO_2$ cost associated with web agents from both a theoretical -via estimation- and an empirical perspective -by benchmarking. Our results show how different philosophies in web agent creation can severely impact the associated expended energy, and that more energy consumed does not necessarily equate to better results. We highlight a lack of transparency regarding disclosing model parameters and processes used for some web agents as a limiting factor when estimating energy consumption. Our work contributes towards a change in thinking of how we evaluate web agents, advocating for dedicated metrics measuring energy consumption in benchmarks.
Promoting AI Equity in Science: Generalized Domain Prompt Learning for Accessible VLM Research
Shanghai Jiao Tong University（上海交通大学）；Eastern Institute of Technology（东方理工大学）；Yale University（耶鲁大学）；Renmin University of China（中国人民大学）；Southern University of Science and Technology（南方科技大学）；Ningbo Institute of Digital Twin（宁波数字孪生研究院）
Benchmarking Energy Efficiency of Large Language Models Using vLLM
Allumni Master Applied IT Fontys University of Applied Sciences Eindhoven, The Netherlands；Sustainable Data \& AI Group Fontys University of Applied Sciences Eindhoven, The Netherlands
Pareto Optimal Benchmarking of AI Models on ARM Cortex Processors for Sustainable Embedded Systems
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；University of Freiburg（弗赖堡大学）；Hahn-Schickard（哈恩-施克尔德研究所）；Center for Artificial Intelligence and Robotics (CAIRO)（人工智能与机器人中心）；Technische Hochschule Würzburg-Schweinfurt（图宾根-施韦因富特技术大学）
Eco-Bee: A Personalised Multi-Modal Agent for Advancing Student Climate Awareness and Sustainable Behaviour in Campus Ecosystems
University of Hull（赫尔大学）；The Spaceship Academy（太空学院）；City St George’s, University of London（伦敦大学城市学院）；King’s College London（伦敦国王学院）；Lancaster University（兰卡斯特大学）；University of Oxford（牛津大学）
Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency
SINTEF（辛泰夫（挪威研究机构））；Singapore Management University（新加坡管理大学）；Oslo Metropolitan University（奥斯陆城市大学）；Kristiania University of Applied Sciences（克里斯蒂安尼亚应用科技大学）
本研究在Raspberry Pi 4上评估了28个量化LLM在能效、推理性能和准确率上的权衡，通过硬件级能耗分析与多数据集基准测试，为资源受限环境下的可持续LLM部署提供了可操作的见解。
Instance Configuration for Sustainable Job Shop Scheduling
Universitat Politècnica de València（巴塞罗那理工大学）；valgrAI - Valencian Graduate School and Research Network of Artificial Intelligence（瓦伦西亚人工智能研究生学院和研究网络）；Research Network of Artificial Intelligence（人工智能研究网络）
Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning
Computer Science and Applied Mathematics Laboratory (LIMA), Faculty of Science and Technology, Chadli Bendjedid University（计算机科学与应用数学实验室（LIMA），科学与技术学院，查德利·本杰迪德大学）；Mathematical Modeling and Numerical Simulation Laboratory (LAM2SIN), Faculty of Technology, Badji Mokhtar University（数学建模与数值模拟实验室（LAM2SIN），技术学院，Badji Mokhtar大学）；School of Business, Istanbul Medipol University（商业学院，伊斯坦布尔Medipol大学）；Department of Economics and Management, Khazar University（经济学与管理学院，Khazar大学）；University College, Korea University（大学学院，韩国大学）；Clinic of Economics, Azerbaijan State University of Economics (UNEC)（经济学诊所，阿塞拜疆国家经济大学（UNEC））；Department of Computer Science, Faculty of Technology, LRI Laboratory, Badji Mokhtar University（计算机科学系，技术学院，LRI实验室，Badji Mokhtar大学）
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning
Technical University of Munich（慕尼黑工业大学）；National University of Sciences & Technology Pakistan（巴基斯坦国立科技大学）；German Aerospace Center（德国航空航天中心）；University of Bristol（布里斯托大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration
organization= nasc Research, School of Computer Science \& IT, University College Cork , country = IE；organization= School of Computing, Engineering；Digital Technologies, Teesside University , country= UK；organization= International Energy Research Centre, Tyndall National Institute, Cork , country = IE；Radiation Technologies Group, CCDCU, Faculty of Engineering；Technology, Sunway University , country = Malaysia；organization= Department of Physics, College of Science, Korea University , country = Republic of Korea
