Smart contracts are a core component of blockchain ecosystems, but their transparency and immutability make them vulnerable to attacks, leading to significant financial losses. Thus, repairing vulnerabilities in smart contracts is crucial for establishing a trustworthy blockchain environment. Existing smart contract vulnerability repair methods suffer from a critical "one-for-all" design limitation, where a single model is tasked with fixing diverse vulnerability types, leading to suboptimal performance due to insufficient specialization. To address this, we propose MoEFix, a novel framework leveraging a Mixture-of-Experts (MoE) architecture tailored for smart contract characteristics. MoEFix partitions vulnerabilities into subspaces, trains specialized experts for each type (e.g., reentrancy, integer overflow), and employs a vulnerability-aware router to dynamically allocate repairs. We further redesign the repair workflow to align with large language models, enabling end-to-end secure contract generation instead of partial patches, and to achieve this, we curated a dataset of 1,391 contracts covering five critical vulnerability types.To validate our approach, we extend the benchmark PVD test suite. Experiments demonstrate that MoEFix outperforms state-of-the-art methods by 21.64% in overall accuracy, achieving improvements of 26.19% (reentrancy) and 23.08% (delegatecall) for specific vulnerabilities.

Modern code review is essential for software quality, but the complexity of codebases and time demands of manual reviews drive interest in automation for greater efficiency and consistency.However, current automated methods often fail to generate meaningful review comments and lack explainability, limiting developers' understanding and trust.This paper presents EXE-Reviewer, aimed at generating more EXplainable and Effective review comments.To enhance effectiveness, we integrate focus information into an existing model to improve its ability to extract key insights, thereby elevating comment quality.To improve explainability, we connect explanatory information (justification behind solutions) to causality, utilizing causality extraction techniques and introducing an explanatory loss.Furthermore, we devise two metrics to assess the quantity and quality of explanatory content, enhancing insight into the model's explanations.We compare EXE-Reviewer to state-ofthe-art methods in terms of effectiveness and explainability of the generated review comments.Experimental results show that EXE-Reviewer achieves a BLEU-4 score of 7.36%, surpassing the state-of-the-art baseline of 18.52%.Meanwhile, both explainability metrics and empirical study demonstrate notable improvements in explainability of the review comments generated by EXE-Reviewer, highlighting the effectiveness of our approach in generating accurate and comprehensible review comments to developers.