Repository-level code completion relies on retrieval strategies to select relevant context from large codebases.Most existing methods employ single-dimensional retrieval based on textual similarity or dependency existence, leading to inconsistent performance across completion scenarios.Even task-adaptive and hybrid methods incur high computational costs while failing to explicitly consider structural hierarchy for retrieving functionally similar code.We introduce AIRCoder, a multidimensional retrieval framework that combines eight complementary metrics across three dimensions: textual similarity, dependency existence, and structural hierarchy.By proposing a structure-preserving chunking strategy and lightweight fusion module, AIRCoder learns context-dependent weights to adaptively integrate retrieval metrics for each query.Experiments on CrossCodeEval and RepoEval demonstrate that AIRCoder achieves an average improvement of 4.63% in exact match over the best baseline, with 10.2× higher efficiency and strong cross-language generalization across Python, Java, C#, and TypeScript. 1

Recent advances in diffusion models have demonstrated their remarkable potential in text generation, achieving performance that rivals or even exceeds autoregressive language models. However, existing diffusion language models face two critical challenges. First, they lack explicit mechanisms for semantic constraints, which is crucial for maintaining semantic consistency. Second, their non-autoregressive generation process at each timestep leads to insufficient token dependencies, resulting in semantic incompleteness or repetition in the generated text. To address these issues, we propose DiffuER (Diffusion with Embedding and Reconstruction loss). By utilizing word embeddings from a pre-trained masked language model as auxiliary regularization and leveraging an encoder-decoder module to reconstruct the original text from the generated sequence, DiffuER strengthens the model’s ability to capture semantics and maintain semantic consistency. Comprehensive experiments on four text generation tasks demonstrate DiffuER’s superiority over autoregressive, non-autoregressive models and diffusion-based baselines in terms of generation quality, diversity, and semantic consistency. Notably, our method achieves a 52.21 increase in generation diversity compared to autoregressive models, and a 38.7 decrease in perplexity compared to diffusion models. Our code is available at https://github.com/miaoGao/DiffuER.

Code style transformation models built on code Language Models (code LMs) have achieved remarkable success. However, they typically focus on basis style transformations, where the target style follows a single criterion, and often struggle with combination styles, where the target style involves multiple criteria. In practice, style guides encompass multiple criteria, making the lack of effective combination style transformation a major limitation to their real-world applicability. In this paper, we propose Absent-Basis-Combination (abbreviated as ABC), a novel framework for code style transformation that significantly improves combination style transformation and overcomes the limitations of existing approaches. We implement four variants of ABC with parameter sizes of 0.5B, 1.3B, 1.5B, and 3B, demonstrating consistent superiority over existing approaches across all model sizes in both basis and combination style transformations. Specifically, ABC achieves performance gains of up to 86.70%, and remains superior even when baseline approaches use three times the parameters. Furthermore, to address the lack of high-quality datasets and evaluation metrics, we construct and release a new style transformation dataset, Basis & Combination Code Style (abbreviated as BCC Style ), and introduce Code Sequence, Syntactic, Semantic and Stylistic BLEU (abbreviated as CS4BLEU), a novel code similarity metric that surpasses existing metrics in accuracy and consistency.

Few-shot anomalous node detection in dynamic networks has been extensively investigated in the field of research. In this few-shot scenario, the detection of these anomalous nodes is particularly challenging due to the continuously evolving network topology and data distribution over time, which is known as concept drift. Concept drift refers to the phenomenon where the underlying concepts or patterns in the data generation process change over time, leading to varying data distributions across different periods. Due to these changes in data distribution, the patterns learned during training may become invalid under the new data distribution. Existing models primarily aim to enhance the representation of evolving node attributes and relationships to mitigate the impact of concept drift in few-shot scenarios. However, the scarcity of anomalous samples further limits the model's ability to learn new patterns, thereby reducing its effectiveness in addressing concept drift in few-shot scenarios. To address this challenge, we propose the multitask asynchronous metalearning framework (MAMF), which aims to mitigate bias induced by concept drift in few-shot anomalous node detection. Our framework consists of four main components: a feature extractor, an anomaly simulator, an asynchronous learner, and a type detector. The feature extractor captures the relative variations of each node in an evolving graph stream. The anomaly simulator uses generative adversarial models to learn anomaly distributions and generate samples at different time intervals. The asynchronous learner samples from various time distributions to create metatasks for anomalous node detection, allowing it to adapt to changes between these distributions. To aid in few-shot anomalous node detection, the type detector is used for anomaly type recognition. Our framework achieves AUC improvements of 5.12%, 6.87%, and 1.91% over the best existing methods on Wikipedia, Reddit, and Mooc datasets, respectively, demonstrating its effectiveness and robustness in adapting to concept drift and detecting anomalous nodes.

Graph neural networks (GNNs) have achieved promising performance on semantic dependency parsing (SDP), owing to their powerful graph representation learning ability. However, training a high-performing GNN-based model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labeled data. To address this drawback, we propose a syntax-guided graph contrastive learning framework to pre-train GNNs with plenty of unlabeled data and fine-tune pre-trained GNNs with few-shot labeled SDP data. Through extensive experiments conducted on the SemEval-2015 Task 18 English dataset in three formalisms (DM, PAS, and PSD), we demonstrate that our framework achieves promising results when few-shot training samples are available. Furthermore, benefiting from the pre-training process, our framework exhibits notable advantages in the out-of-domain test sets.

The rapid development of heterogeneous networks has proposed new challenges to the long-standing link prediction problem. Existing models trained on the verified edge samples from different types usually learn type-specific knowledge, and their type-specific predictions may be contradictory for unverified edge samples with uncertain types. This challenge is termed edge-type disturbance in link prediction in heterogeneous networks. To address this challenge, we develop a disturbance-resilient prediction method ( DRPM ) comprising a structural characterizer, a type differentiator, and a resilient predictor. The structural characterizer is responsible for learning edge representations for link prediction. Concurrently, the type differentiator distinguishes type-specific edge representations to generate diverse type experts while maximizing their link prediction performances on specific types. Furthermore, the resilient predictor evaluates the reliability weights of different type experts to develop a resilient prediction mechanism to aggregate discriminable predictions. Extensive experiments conducted on various real-world datasets demonstrate the importance of the explainable introduction of the edge-type disturbance and the superiority of DRPM over state-of-the-art methods.

Abstract Meaning Representation (AMR) parsing is the task of translating a sentence to an AMR semantic graph which captures the basic meaning of the sentence, and is empowered by pre-trained Transformer models recently. These models encode the syntactic and semantic knowledge implicitly through self-supervised pre-trained tasks. We argue that encoding the syntactic and semantic knowledge explicitly is beneficial to AMR parsing and can improve data efficiency. Specifically, syntactic dependency and semantic role labeling (SRL) have similar sub-structures with AMR. In this work, we propose a novel linguistic knowledge-enhanced AMR parsing model, which augments the pre-trained Transformer with syntactic dependency and semantic role labeling structures of sentences. By applying a heterogeneous graph attention network, we can obtain syntactically and semantically augmented word representation, which can be integrated using an attentive integration layer and gating mechanism. Experimental results show that our model achieves state-of-the-art performance on different benchmarks, especially in out-of-domain and low-resource scenarios.