The field of multi-agent systems and geospatial analysis is witnessing significant advancements, driven by innovative approaches to distributed mapping, adversarial world modeling, and flexible inference of learning rules. Researchers are developing more efficient and scalable methods for collaborative mapping, enabling robots to better understand their environments and perform downstream applications. Additionally, the use of generative world models and adversarial co-evolution is leading to the emergence of complex behaviors and more robust agents. In geospatial analysis, large language models are being evaluated for their capabilities in automating workflows and generating code, revealing both promise and limitations. Noteworthy papers include: OpenMulti, which introduces a Cross-Agent Instance Alignment module for instance-level multi-agent distributed implicit mapping. Learning an Adversarial World Model, which proposes a system where a generative attacker agent learns an implicit world model to synthesize challenging environments for cooperative defender agents. GeoAnalystBench, which presents a benchmark for assessing large language models on geoprocessing tasks and reveals a clear gap between proprietary and open-source models.