We present City Of Light, a Unity-based, city-scale 116 km² simulator of Paris for high-throughput embodied AI research. COL fuses open geographic information system sources into geo-anchored, per-tile meshes and provides a configurable, stochastic runtime with controllable traffic and pedestrians. Agents receive frame-synchronized multi-sensor observations (RGB, depth, normals, semantics) and execute step-synchronized actions to navigate the environment. To support high-rate vision pipelines, we introduce TURBO, a Unity-Python bridge that streams multi-camera observations and allows control at up to 1300 FPS, achieving higher throughput than ML-Agents in our benchmark. We also provide a Street View Digital Twin that aligns simulator viewpoints with corresponding real-world panoramas for frame-accurate visual comparison and quantitative matching. COL enables fast scripting, large-scale data collection, and reinforcement learning in geo-anchored urban settings.

Chemical staining methods, while reliable, are time consuming and can be resource-intensive, involving costly chemical reagents and raising environmental concerns. This underscores the compelling need for alternative solutions such as virtual staining, which not only accelerates the diagnostic process but also enhances the flexibility of stain applications without the associated physical and chemical costs. Generative artificial intelligence technologies prove to be immensely useful in addressing these challenges. However, in healthcare, particularly within computational pathology, the high-stakes nature of decisions complicates the adoption of these tools due to their often opaque processes. Our work introduces an innovative approach that harnesses generative models for virtual stain transformations, improving performance, trustworthiness, scalability, and adaptability within computational pathology. The core of the proposed methodology involves a singular Hematoxylin and Eosin (H&E) encoder that supports multiple stain decoders. This design prioritizes critical regions in the latent space of H&E tissues, leading to a richer representation that enables precise synthetic stain generation by the decoders. Tested to simultaneously generate eight different stains from a single H&E slide, our method also offers significant scalability benefits for routine use by loading only necessary model components during production. We integrate label-free knowledge during training, using loss functions and regularization to minimize artifacts, thereby enhancing the accuracy of virtual staining in both paired and unpaired settings. To build trust in these synthetic stains, we employ a real-time self-inspection methodology using trained discriminators for each stain type, providing pathologists with confidence heatmaps to aid in their evaluations. In addition, we perform automatic quality checks on new H&E slides to ensure that they conform to the trained H&E distribution, guaranteeing the generation of high-quality synthetic stained slides. Recognizing the challenges pathologists face in adopting new technologies, we have encapsulated our method in an open-source, cloud-based proof-of-concept system. This system enables users to easily and virtually stain their H&E slides through a browser, eliminating the need for specialized technical knowledge and addressing common hardware and software challenges. It also facilitates real-time user feedback integration. Lastly, we have curated a novel dataset comprising eight different paired H&E/stains related to pediatric Crohn's disease at diagnosis, providing 30 whole slide images (WSIs) for each stain set (total of 480 WSIs) to stimulate further research in computational pathology.