Want to get a data center online quickly? As the data-center boom puts pressure on the grid, some companies say the answer isn't just more power plants but software that dials down centers' energy-guzzling ways when demand spikes. At the end of a tense and scoreless first half of a soccer match between the English men's team and rival Germany, millions of Brits let out a collective sigh and did what they so often do in moments of stress: They made tea. That wave of electric kettles clicking on, however, caused a different kind of stress: a huge and sudden increase in demand for electricity. But National Grid, which operates the local transmission network, was ready. Just as those kettles started heating up, an AI program sent instructions to a data center in London to slow down some of the facility's power-hungry chips. This reduction helped make sure there was enough supply to match demand, staving off potential blackouts or damage to electrical hardware.

We apply one-dimensional convolutional neural networks to the Frobenius traces of elliptic curves over $\mathbb{Q}$ and evaluate and interpret their predictive capacity. In keeping with similar experiments by Kazalicki--Vlah, Bujanović--Kazalicki--Novak, and Pozdnyakov, we observe high accuracy predictions for the analytic rank across a range of conductors. We interpret the prediction using saliency curves and explore the interesting interplay between murmurations and Mestre--Nagao sums, the details of which vary with the conductor and the (predicted) rank.

The framework of centralized training with decentralized execution (CTDE) [8,28]isone ofthe popular frameworks for solving cooperative multi-agent tasks.

Centralized training with decentralized execution (CTDE) has achieved great success in cooperative multi-agent reinforcement learning (MARL) in practical applications. However, CTDE-based methods typically suffer from poor zero-shot generalization ability with dynamic team composition and varying partial observability. To tackle these issues, we propose a spontaneously grouping mechanism, termed Self-Organized Group (SOG), which is featured with conductor election (CE) and message summary (MS). In CE, a certain number of conductors are elected every $T$ time-steps to temporally construct groups, each with conductor-follower consensus where the followers are constrained to only communicate with their conductor. In MS, each conductor summarize and distribute the received messages to all affiliate group members to hold a unified scheduling. SOG provides zero-shot generalization ability to the dynamic number of agents and the varying partial observability.

The application of machine learning (ML) methods, a subfield of artificial intelligence (AI), to the solution of electromagnetic boundary value problems (BVPs) is currently a highly active area of research. Deep neural networks such as neural operators (Kovachki et al. 2023) and physics-informed neural networks, in which information about the BVP (and possibly measurement data) is integrated into the loss function of the network, often aim to replace traditional numerical methods such as the finite element (FE) method, compare, for example, with (Guo et al. 2025; Rezende and Schuhmann 2025). This work addresses an orthogonal problem: How can AI methods be used to reduce the time required to set up electromagnetic simulation models, rather than solving the numerical models themselves? The focus is thus on the assisted generation of simulation models, whereby the numerical scheme itself remains unaffected. A conceptually related direction has recently emerged in the computational fluid dynamics (CFD) community.

In cooperative Multi-Agent Reinforcement Learning (MARL), efficient exploration is crucial for optimizing the performance of joint policy. However, existing methods often update joint policies via independent agent exploration, without coordination among agents, which inherently constrains the expressive capacity and exploration of joint policies. To address this issue, we propose a conductor-based joint policy framework that directly enhances the expressive capacity of joint policies and coordinates exploration. In addition, we develop a Hierarchical Conductor-based Policy Optimization (HCPO) algorithm that instructs policy updates for the conductor and agents in a direction aligned with performance improvement. A rigorous theoretical guarantee further establishes the monotonicity of the joint policy optimization process. By deploying local conductors, HCPO retains centralized training benefits while eliminating inter-agent communication during execution. Finally, we evaluate HCPO on three challenging benchmarks: Star-CraftII Multi-agent Challenge, Multi-agent MuJoCo, and Multi-agent Particle Environment. The results indicate that HCPO outperforms competitive MARL baselines regarding cooperative efficiency and stability.