arXiv:2509.03830v3 Announce Type: replace Abstract: Historic urban quarters are increasingly shaped by tourism and lifestyle consumption, yet planners often lack scalable evidence on what visitors notice, prefer, and criticize in these environments. This study proposes an AI-based, multimodal framework to decode tourist perception by combining visual attention, color-based aesthetic representation, and multidimensional satisfaction. We collect geotagged photos and review texts from a major Chinese platform and assemble a street view image set as a baseline for comparison across 12 historic urban quarters in Shanghai. We train a semantic segmentation model to quantify foregrounded visual elements in tourist-shared imagery, extract and compare color palettes between social media photos and street views, and apply a multi-task sentiment classifier to assess satisfaction across four experience dimensions that correspond to activity, physical setting, supporting services, and commercial offerings. Results show that tourist photos systematically foreground key streetscape elements and that the color composition represented on social media can differ from on-site street views, indicating a perception-reality gap that varies by quarter. The framework offers an interpretable and transferable approach to diagnose such gaps and to inform heritage management and visitor-oriented urban design.

arXiv:2508.01055v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have gained significant attention in chemistry. However, most existing datasets center on molecular-level property prediction and overlook the role of fine-grained functional group (FG) information. Incorporating FG-level data can provide valuable prior knowledge that links molecular structures with textual descriptions, which can be used to build more interpretable, structure-aware LLMs for reasoning on molecule-related tasks. Moreover, LLMs can learn from such fine-grained information to uncover hidden relationships between specific functional groups and molecular properties, thereby advancing molecular design and drug discovery. Here, we introduce FGBench, a dataset comprising 625K molecular property reasoning problems with functional group information. Functional groups are precisely annotated and localized within the molecule, which ensures the dataset's interoperability thereby facilitating further multimodal applications. FGBench includes both regression and classification tasks on 245 different functional groups across three categories for molecular property reasoning: (1) single functional group impacts, (2) multiple functional group interactions, and (3) direct molecular comparisons. In the benchmark of state-of-the-art LLMs on 7K curated data, the results indicate that current LLMs struggle with FG-level property reasoning, highlighting the need to enhance reasoning capabilities in LLMs for chemistry tasks. We anticipate that the methodology employed in FGBench to construct datasets with functional group-level information will serve as a foundational framework for generating new question-answer pairs, enabling LLMs to better understand fine-grained molecular structure-property relationships. The dataset and evaluation code are available at https://github.com/xuanliugit/FGBench.