Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks, including the scarcity of high-quality training data, complex task composition, and strong contextual dependencies.To address these, we construct JURISCQAD, a large-scale dataset of over 43,000 real-world Chinese legal queries annotated with expert-validated positive and negative responses, and design a structured task decomposition that converts each query into a legal element graph integrating entities, events, intents, and legal issues.We further propose JU-RISMA, a modular multi-agent framework supporting dynamic routing, statutory grounding, and stylistic optimization.Combined with the element graph, the framework enables strong context-aware reasoning, effectively capturing dependencies across legal facts, norms, and procedural logic.Trained on JURISCQAD and evaluated on a refined LawBench, our system significantly outperforms both general-purpose and legal-domain LLMs across multiple lexical and semantic metrics, demonstrating the benefits of interpretable decomposition and modular collaboration in Legal CQA.

As a key to accessing research impact, citation dynamics underpins research evaluation, scholarly recommendation, and the study of knowledge diffusion. Citation prediction is particularly critical for newborn papers, where early assessment must be performed without citation signals and under highly long-tailed distributions. We identify two key research gaps: (i) insufficient modeling of implicit factors of scientific impact, leading to reliance on coarse proxies; and (ii) a lack of bias-aware learning that can deliver stable predictions on lowly cited papers. We address these gaps by proposing a Bias-Aware Citation Prediction Framework, which combines multi-agent feature extraction with robust graph representation learning. First, a multi-agent x graph co-learning module derives fine-grained, interpretable signals, such as reproducibility, collaboration network, and text quality, from metadata and external resources, and fuses them with heterogeneous-network embeddings to provide rich supervision even in the absence of early citation signals. Second, we incorporate a set of robust mechanisms: a two-stage forward process that routes explicit factors through an intermediate exposure estimate, GroupDRO to optimize worst-case group risk across environments, and a regularization head that performs what-if analyses on controllable factors under monotonicity and smoothness constraints. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of our proposed model. Specifically, our model achieves around a 13% reduction in error metrics (MALE and RMSLE) and a notable 5.5% improvement in the ranking metric (NDCG) over the baseline methods.

The sparse channel estimation problem is drawing increasing attention in broadband wireless communication. Sparsity nature in structure and noise is one of the most important issues in such problems. Researchers have devised several sparsity penalty terms such as zero-attracting (ZA) and correntropy induced metric (CIM) to exploit potential sparse structure information and improve sparse channel estimate accuracy. To combat impulsive (sparse) noises, several adaptive filtering algorithms based on the maximum correntropy criterion (MCC) have been developed, which can achieve excellent performance, especially in heavy-tailed noises. Recently, the concept of mixture correntropy and the maximum mixture correntropy criterion (MMCC) were proposed to further promote the robustness of the MCC against impulsive noises. In this paper, a new robust and sparse adaptive filtering algorithm is developed to estimate the sparse channels with impulsive noises by combining the MMCC and CIM penalty. Thanks to the desirable property of the mixture correntropy, the proposed method behaves quite well with excellent convergence performance. Simulation results show that the new method can outperform several existing methods in sparse channel estimation including the MCC based methods.