Xuyang Chen

Weixuan Liu, Qingfeng Zhuge, Xuyang Chen

Legal article retrieval is critical for building traceable and reliable legal AI systems, where conclusions must be grounded in specific legal articles. However, existing open-domain retrieval methods rely heavily on surface-level lexical or semantic similarity, making it difficult for them to distinguish legally relevant articles from those that are textually similar but legally inapplicable or misaligned with the user's underlying intent. To bridge this gap, we propose \textsc{LexPath}, a domain-oriented multi-path framework comprising a multi-path retrieval module and an intent-aware reranking module. The retrieval module combines two complementary legal-specific paths to collect candidate articles: an IRAC-guided sparse path that expands queries with legally informative keywords, and a structure-guided dense path trained with hard negatives derived from legal hierarchy and citation relations. Then, the reranking module further refines the candidate ranking by incorporating the intent consistency score between queries and legal articles. We evaluate \textsc{LexPath} on two publicly available benchmarks focusing on general-public queries and a self-constructed benchmark targeting domain-professional scenarios. Experimental results demonstrate that \textsc{LexPath} consistently outperforms lexical, dense, hybrid, and adaptive retrieval-augmented generation (RAG) baselines. Ablation studies further verify the effectiveness of each component.

Zhenghao Peng, Xuyang Chen, Chengwen Xu et al.

Neural network based approximate computing is a universal architecture promising to gain tremendous energy-efficiency for many error resilient applications. To guarantee the approximation quality, existing works deploy two neural networks (NNs), e.g., an approximator and a predictor. The approximator provides the approximate results, while the predictor predicts whether the input data is safe to approximate with the given quality requirement. However, it is non-trivial and time-consuming to make these two neural network coordinate---they have different optimization objectives---by training them separately. This paper proposes a novel neural network structure---AXNet---to fuse two NNs to a holistic end-to-end trainable NN. Leveraging the philosophy of multi-task learning, AXNet can tremendously improve the invocation (proportion of safe-to-approximate samples) and reduce the approximation error. The training effort also decrease significantly. Experiment results show 50.7% more invocation and substantial cuts of training time when compared to existing neural network based approximate computing framework.