Precise identification of spatial unit functional features in the city is a pre-condition for urban planning and policy-making. However, inferring unknown attributes of urban spatial units from data mining of spatial interaction remains a challenge in geographic information science. Although neural-network approaches have been widely applied to this field, urban dynamics, spatial semantics, and their relationship with urban functional features have not been deeply discussed. To this end, we proposed semantic-enhanced graph convolutional neural networks (GCNNs) to facilitate the multi-scale embedding of urban spatial units, based on which the identification of urban land use is achieved by leveraging the characteristics of human mobility extracted from the largest mobile phone datasets to date. Given the heterogeneity of multi-modal spatial data, we introduced the combination of a systematic data-alignment method and a generative feature-fusion method for the robust construction of heterogeneous graphs, providing an adaptive solution to improve GCNNs’ performance in node-classification tasks. Our work explicitly examined the scale effect on GCNN backbones, for the first time. The results prove that large-scale tasks are more sensitive to the directionality of spatial interaction, and small-scale tasks are more sensitive to the adjacency of spatial interaction. Quantitative experiments conducted in Shenzhen demonstrate the superior performance of our proposed framework compared to state-of-the-art methods. The best accuracy is achieved by the inductive GraphSAGE model at the scale of 250 m, exceeding the baseline by 25.4%. Furthermore, we innovatively explained the role of spatial-interaction factors in the identification of urban land use through the deep learning method.

Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments. The primary limitation lies in their reliance on static schema representations, which fails to resolve semantic ambiguity and scale effectively to large, complex databases. To address this, we propose APEX-SQL, an Agentic Text-to-SQL Framework that shifts the paradigm from passive translation to agentic exploration. Our framework employs a hypothesis-verification loop to ground model reasoning in real data. In the schema linking phase, we use logical planning to verbalize hypotheses, dual-pathway pruning to reduce the search space, and parallel data profiling to validate column roles against real data, followed by global synthesis to ensure topological connectivity. For SQL generation, we introduce a deterministic mechanism to retrieve exploration directives, allowing the agent to effectively explore data distributions, refine hypotheses, and generate semantically accurate SQLs. Experiments on BIRD (70.65% execution accuracy) and Spider 2.0-Snow (53.03% execution accuracy) demonstrate that APEX-SQL outperforms competitive baselines. Further analysis reveals that agentic exploration acts as a performance multiplier, unlocking the latent reasoning potential of foundation models in enterprise settings. Ablation studies confirm the critical contributions of each component in ensuring robust and accurate data analysis. Our code is released at https://github.com/Tencent/APEX-SQL-Project.

LLMs, trained on a diverse range of resources, internalize a substantial amount of knowledge. Understanding this internalized knowledge is crucial for assessing the accuracy and reliability of their outputs, guiding improvements in model design, and addressing potential biases. In this chapter, we explore two primary forms of knowledge within LLMs: taxonomy and factual knowledge. We delve into the nature of internalized knowledge, examining how these models store and utilize information. Additionally, we introduce CRAG, a comprehensive benchmark for evaluating both the internalized knowledge of LLMs and their performance in retrieved-augmented generation tasks.

Large Language Model (LLM)-based multi-agent systems have shown great potential in supporting complex tasks in the medical domain, such as improving diagnostic accuracy and facilitating multidisciplinary collaboration. However, despite the advancement, there is a lack of structured frameworks to guide the design of these systems in medical problem-solving. In this paper, we conduct a comprehensive survey of existing medical multi-agent systems, and propose a medical-specific taxonomy along three key dimensions: team composition, medical knowledge augmentation, and agent interaction. We further outline several future research directions, such as incorporating human-AI collaboration to ensure that human expertise and multi-agent reasoning jointly address complex clinical tasks, designing and evaluating agent profiles, and developing self-evolving systems that adapt to evolving medical knowledge and rapidly changing clinical environments. In summary, our work provides a structured overview of medical multi-agent systems and highlights key opportunities to advance their research and practical deployment.