Amazon's Used Over 350 AI Shots in Season 2. Its Creator Isn't Sorry The show, which follows David's ascent to King of Israel, used four times as much AI this season, including for many of its battle scenes. A dusty visual overlay partially obscures crowds of men in the desert, sword-fighting in armor and on horseback. With some wardrobe tweaks, this scene could look like something out of or . But showrunner Jon Erwin says he didn't have the budget to bring these scenes to life. Instead, he used AI .

Unlocking the potential of transformers on datasets of large physical systems depends on overcoming the quadratic scaling of the attention mechanism. This work explores combining the Erwin architecture with the Native Sparse Attention (NSA) mechanism to improve the efficiency and receptive field of transformer models for large-scale physical systems, addressing the challenge of quadratic attention complexity. We adapt the NSA mechanism for non-sequential data, implement the Erwin NSA model, and evaluate it on three datasets from the physical sciences -- cosmology simulations, molecular dynamics, and air pressure modeling -- achieving performance that matches or exceeds that of the original Erwin model. Additionally, we reproduce the experimental results from the Erwin paper to validate their implementation.

Self-attention scales quadratically with input size, limiting its use for large-scale physical systems. Although sparse attention mechanisms provide a viable alternative, they are primarily designed for regular structures such as text or images, making them inapplicable for irregular geometries. In this work, we present Ball Sparse Attention (BSA), which adapts Native Sparse Attention (NSA) (Yuan et al., 2025) to unordered point sets by imposing regularity using the Ball Tree structure from the Erwin Transformer (Zhdanov et al., 2025). We modify NSA's components to work with ball-based neighborhoods, yielding a global receptive field at sub-quadratic cost. On an airflow pressure prediction task, we achieve accuracy comparable to Full Attention while significantly reducing the theoretical computational complexity. Our implementation is available at https://github.com/britacatalin/bsa.

Large-scale physical systems defined on irregular grids pose significant scalability challenges for deep learning methods, especially in the presence of long-range interactions and multi-scale coupling. Traditional approaches that compute all pairwise interactions, such as attention, become computationally prohibitive as they scale quadratically with the number of nodes. We present Erwin, a hierarchical transformer inspired by methods from computational many-body physics, which combines the efficiency of tree-based algorithms with the expressivity of attention mechanisms. Erwin employs ball tree partitioning to organize computation, which enables linear-time attention by processing nodes in parallel within local neighborhoods of fixed size. Through progressive coarsening and refinement of the ball tree structure, complemented by a novel cross-ball interaction mechanism, it captures both fine-grained local details and global features. We demonstrate Erwin's effectiveness across multiple domains, including cosmology, molecular dynamics, and particle fluid dynamics, where it consistently outperforms baseline methods both in accuracy and computational efficiency.

IBM and Verizon have announced a joint venture in which the companies will work together on 5G and edge computing technology to help enable the future of industry 4.0. The partnership will combine the high speed and low latency of Verizon's 5G and Multi-access Edge Compute capabilities, IoT devices and sensors with IBM's expertise in artificial intelligence, hybrid multiload, edge computing, asset management and connected operations. In a press release, the companies said the partnership will help industrial enterprises find ways to use edge computing to accelerate access to near real-time, actionable insights into operations to improve efficiencies. The first solutions planned from the collaboration are to mobile asset tracking and management solutions to hep enterprises improve operations, optimize production quality and enhance worker safety. For those first solutions, the companies plan to leverage Verizon's 5G Ultra Wideband network, Multi-access Edge Computing (MEC), ThingSpace IoT Platform and Critical Asset Sensor solution, which will be jointly offered with IBM's Maximo Monitor with IBM Watson and advanced analytics.