Urban region representation learning plays a fundamental role in urban computing, providing compact embeddings that support a wide range of downstream analytical tasks. Existing methods are developed under a single-city setting and rely on continuous vector representations learned from city-specific spatiotemporal data. Learning transferable and semantically meaningful region representations in cross-city scenarios remains a significant challenge. In this paper, we propose URBAN-PROTOS, a two-stage framework for cross-city learning of urban region representations. We first abstract multi-city mobility data into discrete Urban Prototypes, which capture city-invariant spatiotemporal regularities from localized heterogeneous contexts. These prototypes serve as shared semantic units across cities and are further integrated into the semantic space of large language models through a prototype-guided alignment mechanism. Extensive experiments conducted demonstrate that URBAN-PROTOS consistently outperforms existing methods in cross-city transfer settings, while maintaining strong performance in single-city evaluations. Further analyses show that the learned region embeddings encode deep semantic regularities shared across cities, providing robust support for cross-city generalization.

Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have demonstrated significant potential in single-turn reasoning tasks. With the paradigm shift toward self-evolving agentic learning, models are increasingly expected to learn from trajectories by synthesizing tools or accumulating explicit experiences. However, prevailing methods typically rely on large-scale LLMs or multi-agent frameworks, which hinder their deployment in resource-constrained environments. The inherent sparsity of outcome-based rewards also poses a substantial challenge, as agents typically receive feedback only upon task completion. To address these limitations, we introduce a Tool-Memory based self-evolving agentic framework SEARL. Unlike approaches that directly utilize interaction experiences, our method constructs a structured experience memory that integrates planning with execution. This provides a novel form of state abstraction that facilitates the aggregation of actions within functionally analogous contexts, such as tool reuse. Consequently, agents not only extract explicit knowledge from historical data but also leverage inter-trajectory correlations to densify reward signals. We evaluate our framework on knowledge reasoning and complex search tasks, demonstrating its effectiveness in achieving more practical and efficient agentic learning.