首次系统评估网页智能体能耗，揭示其能效与性能不直接相关。
Promoting Sustainable Web Agents: Benchmarking and Estimating Energy Consumption through Empirical and Theoretical Analysis
- 结合实测与理论估算，量化网页智能体能耗差异
- 发现不同设计哲学导致能耗相差数倍，但效果未必更好
- 呼吁在评测中加入能耗指标，提升透明度
网页智能体（如 OpenAI Operator、Google Project Mariner）是推动大语言模型应用边界的强大系统，可自主完成网页导航、表单填写和价格比对等任务。尽管该领域研究活跃，但其带来的可持续性问题尚未被充分关注。本文从理论估算和实测基准两个角度，首次探索网页智能体的能源消耗与 $CO_2$ 排放成本。结果表明，不同设计思路导致能耗差异显著，且更高能耗并不一定带来更好性能。同时指出，部分智能体缺乏模型参数与流程披露，严重制约能耗评估。本研究倡导在评测体系中引入能耗专用指标，推动更可持续的智能体发展。
原文摘要 · Abstract (English)
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.
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