This article discusses advancements in the Deepseek model, highlighting reduced attention complexity and innovations in reinforcement learning training. It also critiques the assumptions surrounding open-source large language models and questions the benchmarks used to evaluate their performance.

This article presents a new approach for predicting image locations on Earth by integrating map-based reasoning into large vision-language models. It develops a two-stage optimization method that combines reinforcement learning with test-time scaling to enhance prediction accuracy. The authors introduce MAPBench, a benchmark for evaluating geolocalization performance on real-world images.