Recent advances in large language models (LLMs) have yielded impressive gains on mathematical reasoning benchmarks via supervised fine-tuning (SFT).However, the brittleness of these models under input perturbations has cast doubt on whether such improvements reflect genuine reasoning abilities or merely superficial alignment with expected output formats.We investigate the mechanisms behind SFT improvements in small-scale LLMs, addressing four key questions: (1) Are performance gains primarily due to format alignment rather than reasoning?(2) Can high-quality supervision encourage genuine reasoning?(3) Does scaling data shift learning from format alignment to deeper reasoning?(4) Are format alignment gains consistent across model sizes and architectures?Through controlled experiments, we find that most performance improvements arise from format alignment rather than genuine reasoning enhancement.Moreover, SFT's effectiveness is strongly influenced by the alignment between the base model's inductive biases and the teacher model's output distribution, rather than the teacher's raw strength.Finally, scaling up training data offers diminishing returns and does not fundamentally alter the model's reasoning behavior.These findings suggest that current SFT practices may overestimate the reasoning abilities of LLMs and underscore the need for more rigorous evaluation methods.

Urban warming differs markedly from regional background trends, highlighting the unique behavior of urban climates and the challenges they present. Accurately predicting local urban climate necessitates modeling the interactions between urban surfaces and atmospheric forcing. Although off-the-shelf machine learning (ML) algorithms offer considerable accuracy for climate prediction, they often function as black boxes, learning data mappings rather than capturing physical evolution. As a result, they struggle to capture key land-atmosphere interactions and may produce physically inconsistent predictions. To address these limitations, we propose UCformer, a novel multi-task, physics-guided Transformer architecture designed to emulate nonlinear urban climate processes. UCformer jointly estimates 2-m air temperature $\(T\)$, specific humidity $\(q\)$, and dew point temperature $\(t\)$ in urban areas, while embedding domain and physical priors into its learning structure. Experimental results demonstrate that incorporating domain and physical knowledge leads to significant improvements in emulation accuracy and generalizability under future urban climate scenarios. Further analysis reveals that learning shared correlations across cities enables the model to capture transferable urban surface–atmosphere interaction patterns, resulting in improved accuracy in urban climate emulation. Finally, UCformer shows strong potential to fit real-world data: when fine-tuned with limited observational data, it achieves competitive performance in estimating urban heat fluxes compared to a physics-based model.

Traffic simulation provides interactive data for the optimization of traffic control policies. However, existing traffic simulators are limited by their lack of scalability and shortage in input data, which prevents them from generating interactive data from traffic simulation in the scenarios of real large-scale city road networks.

Data-enabled smart transportation has attracted a surge of interest from machine learning and data mining researchers nowadays due to the bloom of online ride-hailing industry and rapid development of autonomous driving. Large-scale high quality route data and trading data (spatiotemporal data) have been generated every day, which makes AI an urgent need and preferred solution for the decision making in intelligent transportation systems. While a large of amount of work have been dedicated to traditional transportation problems, they are far from satisfactory for the rising need. We propose a half-day workshop at CIKM 2019 for the professionals, researchers, and practitioners who are interested in mining and understanding big and heterogeneous data generated in transportation, and AI applications to improve the transportation system. We plan to have several invited talks from both academia and industry. This workshop would be organized by Shanghai Jiao Tong University, Didi Chuxing and Pennsylvania State University.

editorial Free Access Share on ACM TIST Special Issue on Urban Intelligence Editors: Bo An Nanyang Technological University Nanyang Technological UniversityView Profile , Nick Jennings Imperial College Imperial CollegeView Profile , Zhenhui Jessie Li Pennsylvania State University Pennsylvania State UniversityView Profile Authors Info & Claims ACM Transactions on Intelligent Systems and TechnologyVolume 9Issue 3May 2018 Article No.: 23pp 1–4https://doi.org/10.1145/3154942Published:24 November 2017Publication History 0citation611DownloadsMetricsTotal Citations0Total Downloads611Last 12 Months37Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF