Accurate Travel Time Estimation (TTE) is critical for ride-hailing platforms, where errors directly impact user experience and operational efficiency. While existing production systems excel at holistic route-level dependency modeling, they struggle to capture city-scale traffic dynamics and long-tail scenarios, leading to unreliable predictions in large urban networks. In this paper, we propose \model, a scalable and adaptive framework that synergistically integrates link-level modeling with industrial route-level TTE systems. Specifically, we propose a spatio-temporal external attention module to capture global traffic dynamic dependencies across million-scale road networks efficiently. Moreover, we construct a stabilized graph mixture-of-experts network to handle heterogeneous traffic patterns while maintaining inference efficiency. Furthermore, an asynchronous incremental learning strategy is tailored to enable real-time and stable adaptation to dynamic traffic distribution shifts. Experiments on real-world datasets validate MixTTE significantly reduces prediction errors compared to seven baselines. MixTTE has been deployed in DiDi, substantially improving the accuracy and stability of the TTE service.

Scientific researchers need intensive information about datasets to effectively evaluate and develop theories and methodologies. The information needs regarding datasets are implicitly embedded in particular research tasks, rather than explicitly expressed in search queries. However, existing scientific retrieval and question-answering (QA) datasets typically address straightforward questions, which do not align with the distribution of real-world research inquiries. To bridge this gap, we developed ScIRGen, a dataset generation framework for scientific QA & retrieval that more accurately reflects the information needs of professional science researchers, and uses it to create a large-scale scientific retrieval-augmented generation (RAG) dataset with realistic queries, datasets and papers. Technically, we designed a dataset-oriented information extraction method that leverages academic papers to augment the dataset representation. We then proposed a question generation framework by employing cognitive taxonomy to ensure the quality of synthesized questions. We also design a method to automatically filter synthetic answers based on the perplexity shift of LLMs, which is highly aligned with human judgment of answers' validity. Collectively, these methodologies culminated in the creation of the 61k QA dataset, ScIRGen-Geo. We benchmarked representative methods on the ScIRGen-Geo dataset for their question-answering and retrieval capabilities, finding out that current methods still suffer from reasoning from complex questions. This work advances the development of more sophisticated tools to support the intricate information needs of the scientific community.

Heterogeneous federated learning (HFL) enables collaborative learning across clients with diverse model architectures and data distributions while preserving privacy. However, existing HFL approaches often struggle to effectively address the challenges posed by model diversity, leading to suboptimal performance and limited generalization ability. This paper pro-poses Fed2PKD, a novel HFL framework that tackles these challenges through a two-pronged knowledge distillation approach. Fed2PKD combines prototypical contrastive knowledge distillation to align client embeddings with global class prototypes and semi-supervised global knowledge distillation to capture global data characteristics. Experimental results on three benchmarks (MNIST, CIFAR10, and CIFAR100) demonstrate that Fed2PKD significantly outperforms existing state-of-the-art HFL methods, achieving average improvements of up to 30.53%, 13.89%, and 5.80 % in global model accuracy, respectively. Furthermore, Fed2PKD enables personalized models for each client, adapting to their specific data distributions and model architectures while benefiting from global knowledge sharing. Theoretical analysis provides convergence guarantees for Fed2PKD under realistic assumptions. Fed2PKD represents a significant step forward in HFL, unlocking the potential for privacy-preserving collaborative learning in real-world scenarios with model and data diversity.