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.

Geo-localization aims to infer the geographic origin of a given signal. In computer vision, geo-localization has served as a demanding benchmark for compositional reasoning and is relevant to public safety. In contrast, progress on audio geo-localization has been constrained by the lack of high-quality audio-location pairs. To address this gap, we introduce AGL1K, the first audio geo-localization benchmark for audio language models (ALMs), spanning 72 countries and territories. To extract reliably localizable samples from a crowd-sourced platform, we propose the Audio Localizability metric that quantifies the informativeness of each recording, yielding 1,444 curated audio clips. Evaluations on 16 ALMs show that ALMs have emerged with audio geo-localization capability. We find that closed-source models substantially outperform open-source models, and that linguistic clues often dominate as a scaffold for prediction. We further analyze ALMs’ reasoning traces, regional bias, error causes, and the interpretability of the localizability metric. Overall, AGL1K establishes a benchmark for audio geo-localization and may advance ALMs with better geospatial reasoning capability.

Remote sensing has emerged as an important domain for multimodal large language models (MLLMs), where reliable understanding requires locating small, dense, and spatially subtle visual evidence. However, our diagnostic analysis shows that current MLLMs often fail because their reasoning is not grounded to the correct visual evidence, leading to wrong-region attention or missed task-relevant objects. To address this issue, we introduce RsEvi-8B, an evidence-aware remote sensing MLLM that reasons with parseable bounding-box-grounded evidence. To enable this capability, we construct RsEvi-248K, a large-scale supervised dataset spanning VQA, visual grounding, object detection, object counting, and scene classification. The dataset provides verified bounding-box-grounded reasoning traces, where each intermediate visual claim is aligned with supporting evidence regions and filtered for both answer correctness and evidence faithfulness. To improve evidence-faithful reasoning, we further propose the evidence-aware GRPO, which uses an evidence verifier to regulate learning on generated evidence spans while preserving task-level outcome optimization. Extensive experiments across five remote-sensing task families show that RsEvi-8B improves the dataset-balanced average score by 17.15 points over Qwen3-VL-8B-Instruct, establishing explicit visual grounding as a key ingredient for reliable remote-sensing reasoning.

Large language models (LLMs) exhibit strong semantic reasoning and open-ended generation abilities, but aligning these abilities with structured sequential generation remains challenging. This challenge is particularly evident in out-of-town (OOT) POI sequence generation, where a model must infer transferable travel intent from a user’s hometown behaviors, adapt to cross-city interest drift, and generate a coherent destination trajectory under structural constraints. Existing approaches either rely on latent ID-based transfer with limited interpretability or directly use LLMs for sequence generation without explicitly grounding inferred semantics into position-aware predictions. To address this gap, we propose SemPOI-RL, a framework that aligns LLM semantic reasoning with structured sequence generation for interpretable OOT recommendation. Specifically, we first fine-tune an LLM to infer destination-oriented travel styles from users’ hometown trajectories, using natural language as an interpretable semantic intermediate. We then introduce a Semantic POI Alignment Module (SPAM) to ground these inferred styles into a style-conditioned masked autoencoder for position-aware trajectory generation. Finally, we apply reinforcement learning with recommendation-oriented rewards to align LLM-generated styles with downstream sequence quality. Experiments on two real-world datasets show that SemPOI-RL consistently outperforms both traditional recommenders and direct LLM baselines, while providing interpretable style attribution across different phases of a trip. The code is available at https://github.com/Wind-Flipped/SemPOI-RL.

In many real-world scenarios (e.g., academic networks, social platforms), different types of entities are not only associated with texts but also connected by various relationships, which can be abstracted as Text-Attributed Heterogeneous Graphs (TAHGs). Current pretraining tasks for Language Models (LMs) primarily focus on separately learning the textual information of each entity and overlook the crucial aspect of capturing topological connections among entities in TAHGs. In this paper, we present a new pretraining framework for LMs that explicitly considers the topological and heterogeneous information in TAHGs. Firstly, we define a context graph as neighborhoods of a target node within specific orders and propose a topology-aware pretraining task to predict nodes involved in the context graph by jointly optimizing an LM and an auxiliary heterogeneous graph neural network. Secondly, based on the observation that some nodes are text-rich while others have little text, we devise a text augmentation strategy to enrich textless nodes with their neighbors’ texts for handling the imbalance issue. We conduct link prediction and node classification tasks on three datasets from various domains. Experimental results demonstrate the superiority of our approach over existing methods and the rationality of each design. Our code is available at https://github.com/Hope-Rita/THLM.

Hierarchical text classification aims to leverage label hierarchy in multi-label text classification. Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels. Since global hierarchy is static and irrelevant to text samples, it makes these methods hard to exploit hierarchical information. Contrary to global hierarchy, local hierarchy as a structured labels hierarchy corresponding to each text sample. It is dynamic and relevant to text samples, which is ignored in previous methods. To exploit global and local hierarchies, we propose Hierarchy-guided BERT with Global and Local hierarchies (HBGL), which utilizes the large-scale parameters and prior language knowledge of BERT to model both global and local hierarchies. Moreover, HBGL avoids the intentional fusion of semantic and hierarchical modules by directly modeling semantic and hierarchical information with BERT. Compared with the state-of-the-art method HGCLR, our method achieves significant improvement on three benchmark datasets.