A tech industry insider's guide to where to stay, eat, work, and play while visiting the tech scene's mothership, San Francisco. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. You've probably read plenty of recent news stories about how San Francisco is a failed city. Our infrastructure is crumbling, our streets are scary, our social fabric is torn and frayed. Most of that stuff is false. Yes, San Francisco has issues, but they're the same problems nearly all US cities are facing as they struggle to reorient themselves to our new, post-pandemic economic reality. The "doom loop" narrative that's often repeated in the national press is a gross exaggeration. The truth is that San Francisco is thriving.

This benchmark suite provides a comprehensive evaluation framework for assessing both individual LLMs and multi-agent systems in real-world planning scenarios. The suite encompasses eleven designed problems that progress from basic to highly complex, incorporating key aspects such as multi-agent coordination, inter-agent dependencies, and dynamic environmental disruptions. Each problem can be scaled along three dimensions: the number of parallel planning threads, the complexity of inter-dependencies, and the frequency of unexpected disruptions requiring real-time adaptation. The benchmark includes detailed specifications, evaluation metrics, and baseline implementations using contemporary frameworks like LangGraph, enabling rigorous testing of both single-agent and multi-agent planning capabilities. Through standardized evaluation criteria and scalable complexity, this benchmark aims to drive progress in developing more robust and adaptable AI planning systems for real-world applications.

We study the image-based geolocalization problem, aiming to localize ground-view query images on cartographic maps. Current methods often utilize cross-view localization techniques to match ground-view query images with 2D maps. However, the performance of these methods is unsatisfactory due to significant cross-view appearance differences. In this paper, we lift cross-view matching to a 2.5D space, where heights of structures (e.g., trees and buildings) provide geometric information to guide the cross-view matching. We propose a new approach to learning representative embeddings from multi-modal data. Specifically, we establish a projection relationship between 2.5D space and 2D aerial-view space. The projection is further used to combine multi-modal features from the 2.5D and 2D maps using an effective pixel-to-point fusion method. By encoding crucial geometric cues, our method learns discriminative location embeddings for matching panoramic images and maps. Additionally, we construct the first large-scale ground-to-2.5D map geolocalization dataset to validate our method and facilitate future research. Both single-image based and route based localization experiments are conducted to test our method. Extensive experiments demonstrate that the proposed method achieves significantly higher localization accuracy and faster convergence than previous 2D map-based approaches.

One woman set up a Tinder date with dozens of men to have them battle for her heart. Dozens of dudes showed up to Union Square for Tinder dates on Sunday -- only to learn they were all there to meet the same woman, and she wanted them to battle it out "Hunger Games"-style for her heart. One Twitter user recounting his version of events says he met a woman named Natasha through the hookup app and she invited him to meet her at the public space to watch her friend DJ -- but he arrived to a totally surreal scene. "I make my way to Union Square. Eat a hot dog and look over by this open lot by 17th Ave and there is a stage and a DJ and about 100 ppl and cameras and sh-t and I think well this is some random ass Manhattan sh-t," wrote @bvdhai.

Here at San Francisco Marriott Union Square, we set the gold standard for comfort, convenience and brilliant travel. Located just steps away from Union Square, you'll have unbeatable access to premier dining, shopping and entertainment. Our hotel is right by one of the city's iconic cable car lines, making it easy to hop aboard and explore numerous attractions including Chinatown, Fisherman's Wharf and Pier 39. Innovation Enterprise has organised a discounted room block at the hotel just for attendees of the summit! To take advantage of our special rate of $219 per night, please click here.