Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object and plays a critical role in knowledge fusion and integration.Traditional EA methods mainly rely on knowledge representation learning, but their performance is often limited under noisy or sparsely supervised scenarios.Recently, large language models (LLMs) have been introduced to EA and achieved notable improvements by leveraging rich semantic knowledge.However, existing LLM-based EA approaches typically treat LLMs as black-box decision makers, resulting in limited interpretability, and the direct use of large-scale triples substantially increases inference cost.To address these challenges, we propose EA-Agent, a reasoningdriven agent for EA.EA-Agent formulates EA as a structured reasoning process with multistep planning and execution, enabling interpretable alignment decisions.Within this process, it introduces attribute and relation triple selectors to filter redundant triples before feeding them into the LLM, effectively addressing efficiency challenges.Experimental results on three benchmark datasets demonstrate that EA-Agent consistently outperforms existing EA methods and achieves state-of-the-art performance.The source code is available at https: //github.com/YXNan0110/EA-Agent.

In the dynamic landscape of large-scale web search, Query-Driven Text Summarization (QDTS) aims to generate concise and informative summaries from textual documents based on a given query, which is essential for improving user engagement and facilitating rapid decision-making. Traditional extractive summarization models, based primarily on ranking candidate summary segments, have been the dominant approach in industrial applications. However, these approaches suffer from two key limitations: 1) The multi-stage pipeline often introduces cumulative information loss and architectural bottlenecks due to its weakest component; 2) Traditional models lack sufficient semantic understanding of both user queries and documents, particularly when dealing with complex search intents. In this study, we propose a novel framework to pioneer the application of generative models to address real-time QDTS in industrial web search. Our approach integrates large model distillation, supervised fine-tuning, direct preference optimization, and lookahead decoding to transform a lightweight model with only 0.1B parameters into a domain-specialized QDTS expert. Evaluated on multiple industry-relevant metrics, our model outperforms the production baseline and achieves a new state of the art. Furthermore, it demonstrates excellent deployment efficiency, requiring only 334 NVIDIA L20 GPUs to handle ~50,000 queries per second under 55~ms average latency per query.

Network alignment has attracted widespread attention in various fields. However, most existing works mainly focus on the problem of label sparsity, while overlooking the issue of noise in network alignment, which can substantially undermine model performance. Such noise mainly includes structural noise from noisy edges and labeling noise caused by human-induced and process-driven errors. To address these problems, we propose RANA, a Robust Active learning framework for noisy Network Alignment. RANA effectively tackles both structure noise and label noise while addressing the sparsity of anchor link annotations, which can improve the robustness of network alignment models. Specifically, RANA introduces the proposed Noise-aware Selection Module and the Label Denoising Module to address structural noise and labeling noise, respectively. In the first module, we design a noise-aware maximization objective to select node pairs, incorporating a cleanliness score to address structural noise. In the second module, we propose a novel multi-source fusion denoising strategy that leverages model and twin node pairs labeling to provide more accurate labels for node pairs. Empirical results on three real-world datasets demonstrate that RANA outperforms state-of-the-art active learning-based methods in alignment accuracy. Our code is available at https://github.com/YXNan0110/RANA.

In the application of remote sensing image (RSI) scene classification, in order to solve the contradiction between the accuracy of Convolutional Neural Network (CNN) and the large amount of model parameters, a novel dual knowledge distillation (DKD) model combining dual attention (DA) and spatial structure (SS) is designed. First, new DA and SS modules are constructed and introduced into ResNet101 and light-weight CNN designed as teacher and student networks respectively. Then, in order to improve its local feature extraction and high-level semantic representation abilities for RSI by transmission the DA and SS knowledge in the teacher network to the student network, we design the corresponding DA and SS distillation losses. The comparative experimental results based on AID and NWPU-45 datasets show that when the training ratio is 20%, the accuracy of the student network after DKD is improved by 7.57% and 7.28% respectively, and in the case of fewer parameters, DKD has higher accuracy than most other methods.