The paper reviews 194 papers on using artificial intelligence to optimize data center energy use, coding 63 of them. It finds that most control studies validate only in simulation, none consider water withdrawal or embodied carbon, and savings estimates overlap across methods, preventing ranking. The authors propose CLEAR‑DC, a framework that links control and workload demand through elasticity, reports net benefits, and records energy, carbon, water, embodied share, and validation venue.
By Mohammed Basharath Ullah, Summaiya Unnisa Begum, Mohammed Nadeem Ullah
The paper introduces Conformal Reservation for Orchestration (CRO), an online algorithm that uses past execution traces to allocate compute and bandwidth for agentic AI systems at dispatch time. CRO provides finite‑sample, distribution‑free coverage guarantees, ensuring that resource reservations meet specified probabilistic deadlines. Simulations demonstrate that CRO uniquely satisfies target coverage across all risk levels while reducing over‑reservation—and thus resource waste—by about half compared to a Chebyshev‑bound baseline.
By Masoud Shokrnezhad, Tarik Taleb
arXiv:2608.29397v1 Announce Type: new
Abstract: Tool-use benchmarks generally evaluate whether an agent completes a workflow using appropriate tools and valid arguments. However, feasibility alone is...
By Zixiang Xu, Jiaan Wang, Fandong Meng
arXiv:2606. 15954v1 Announce Type: cross Abstract: Agentic AI systems act through tools and sub-agents, yet the controls meant to bound their financial and environmental cost still sit on dashboards evaluated beside or after execution.
By Gaston Besanson
arXiv:2608. 09366v1 Announce Type: new Abstract: Large-scale learning systems often face the challenge of balancing multiple, potentially competing objectives, such as fairness, accuracy, and latency.
By Corinna Cortes, Yishay Mansour, Mehryar Mohri
arXiv:2604. 25848v2 Announce Type: replace Abstract: We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times.
By An Nguyen, Hoang Nguyen, Phuong Le, Hung Pham, Cuong Do, Laurent El Ghaoui
arXiv:2607. 12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions.
By Takumi Shioda, Kohei Terashima, Tatsuo Nagai
arXiv:2608. 04265v1 Announce Type: cross Abstract: Evaluations of LLM planning agents largely ask whether a task succeeds or a declared plan is followed.
By J. de Curt\`o, I. de Zarz\`a
arXiv:2608. 12915v1 Announce Type: cross Abstract: The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality.
By Nicoletta Tsiopani, Moysis Symeonides, George Pallis, Marios D. Dikaiakos
arXiv:2608. 09380v1 Announce Type: new Abstract: Long-horizon complex tasks require agents to repeatedly observe states, formulate plans, invoke tools, verify results, and recover from failures in continuously changing environments.
By Siqi Wang, Xinlin Li, Zhenglin Li, Li Li
The paper introduces a task-aware harness provisioning method for large language model agents used in mission‑critical infrastructure operations. By mapping each task to an optimal harness configuration—determined through mathematical task classification and empirical agent execution data—the authors propose a map‑guided escalation algorithm that starts with a minimal harness and expands only when necessary. Experiments on liquid‑cooling and power‑grid tasks show that this approach can improve accuracy and reduce token usage compared to full harness provisioning, revealing a domain‑dependent accuracy‑cost trade‑off.
By Liangtao Lin, Qingang Zhang, Zhaomeng Zhu, Tianwei Zhang, Yonggang Wen
arXiv:2608. 05144v2 Announce Type: replace Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failure, hidden constraints, or a misspecified objective.
By Boxiu Li, Zimo Wen, Yijia Fan, Chuan Wen, Fan Yang, Hangxi Guo, Jiaao Wu, Jiachen Zhang, Junxiang Lei, Mukai Li, Ruize Tang, Runjing Gu, Shibo Hu, Sihan Chen, Sufeng Guo, Wanbo Zhang, Xian Zhang, Xiaoyu Chen, Xuanhe Zhou, Xuyao Huang, Yifei Gao, Yifei Shen, Yilin Chen, Yuheng Wu, Yuzhe Zhang, Zelong Zhao, Zhijie Deng