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
The paper introduces a multi-objective reinforcement learning framework for planning AI data center build-outs, treating the long-term infrastructure decisions as a tiered Markov decision process. It proposes a preference-conditioned agent that can generate policies balancing cost, carbon, water, and reliability, and validates the approach on real site data across different commitment regimes. The study also presents a lightweight counterfactual rollout model to assess long-horizon dynamics and releases the associated environment, agents, and evaluation tools.
By Satyapragnya Kar, Rajarshi Das, PR Panigrahi
The paper introduces DRL-DCO, a Deep Reinforcement Learning‑governed dynamic orchestration of Differential Evolution and CMA‑ES for high‑dimensional structural model updating. A DDPG actor‑critic agent uses a progression‑aware state and diversity‑informed reward to continuously reallocate resources between exploration and exploitation, while controlling population size, elite preservation, and restarts. The trained actor can operate in a supervision‑free inference mode, enabling fast deployment with maintained performance, and demonstrates superior convergence accuracy and robustness on benchmark optimization and structural health monitoring problems.
By Lechen Li (State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau 519000, China, College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China), Rongye Shi (School of Artificial Intelligence, Beihang University, Beijing 100191, China), Wanhuan Zhou (State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau 519000, China)
arXiv:2602. 10429v2 Announce Type: replace-cross Abstract: AIvilization v0 is a publicly deployed large-scale artificial society that couples a resource-constrained sandbox with a unified LLM-agent architecture, aiming to sustain long-horizon autonomy while remaining executable under a rapidly changing environment.
By Wenkai Fan, Shurui Zhang, Xiaolong Wang, Haowei Yang, Tsz Wai Chan, Xingyan Chen, Junquan Bi, Zirui Zhou, Jia Liu, Kani Chen
arXiv:2607. 10630v1 Announce Type: cross Abstract: Robust motion planning in dense traffic requires autonomous vehicles to interact in rare and safety-critical scenarios that are underrepresented in naturalistic driving data.
By Tong Nie, Yuewen Mei, Junlin He, Yihong Tang, Jian Sun, Wei Ma
The paper introduces Decision‑Focused Learning via Neural Optimization Layers (BS‑DFL) for battery‑assisted grid‑import smoothing in AI data centers. It formulates a risk‑aware battery energy storage system scheduling problem that balances reserve commitments, demand‑responsive dispatch, operating costs, grid‑ramp penalties, and conditional value‑at‑risk. By training a constraint‑aware neural optimization layer directly on the scheduling objective, the method eliminates the need for optimal‑solution labels and enables fast, solver‑free rolling‑horizon scheduling, achieving a 71.70% reduction in minute‑to‑minute grid‑import changes and an 18.01% cost savings in a 10,000‑minute simulation.
By Xinda Zheng, Canchen Jiang, Frits de Nijs, Edward Lam, Markus Wagner
The paper introduces a reinforcement‑learning‑guided evolutionary policy optimization framework for scheduling heterogeneous agile Earth observation satellites, addressing task selection, satellite assignment, and sequencing under diverse visibility windows, maneuvering constraints, energy use, and storage limits. It combines assignment‑based indirect encoding with decoder‑based cost evaluation to capture satellite‑dependent constraints while integrating task gain, energy savings, and load balance into a single utility metric. The resulting RLOSMEA algorithm uses reinforcement learning to select high‑level search operators, achieving higher weighted utility and more stable convergence than baseline metaheuristics across varied AEOS scenarios.
By He Wang, Junyu Wu, Hui Li, Yanjie Song, Witold Pedrycz, Liang Li
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. 27626v1 Announce Type: new Abstract: Safety-critical IoT systems such as industrial closed-loop control, V2X coordination, and remote teleoperation require every sensor's peak Age of Information (peak AoI, also abbreviated PAoI) to stay below a hard per-slot deadline, not merely an average bound.
By Wentao Zhang, Wentao Mo
arXiv:2607. 26068v1 Announce Type: cross Abstract: Existing AI governance frameworks, including the EU AI Act and NIST AI RMF, address safety, transparency, and accountability but do not operationalize quantitative constraints on macro-socioeconomic stability.
By Sivasathivel Kandasamy
arXiv:2606. 27743v1 Announce Type: cross Abstract: Large Language Models (LLMs) inference is typically deployed under a static resource assumption, where models execute a fixed computational graph regardless of the runtime environment.
By Yuhang Chen, Jinhao Duan, Ruichen Zhang, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Parish Aggarwal, Frank Shyu, Luke Simon, Sandeep Pandey, Tianlong Chen, Xi Liu
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