Sparse Mixture of Experts (sMoE) has become a pivotal approach for scaling large vision-language models, offering substantial capacity while maintaining computational efficiency through dynamic, sparse activation of experts. However, existing routing mechanisms, typically based on similarity scoring, struggle to effectively capture the underlying input structure. This limitation leads to a trade-off between expert specialization and balanced computation, hindering both scalability and performance. We propose Input Domain Aware MoE, a novel routing framework that leverages a probabilistic mixture model to better partition the input space. By modeling routing probabilities as a mixture of distributions, our method enables experts to develop clear specialization boundaries while achieving balanced utilization. Unlike conventional approaches, our routing mechanism is trained independently of task-specific objectives, allowing for stable optimization and decisive expert assignments. Empirical results on vision-language tasks demonstrate that our method consistently outperforms existing sMoE approaches, achieving higher task performance and improved expert utilization balance.

Fault localization seeks to locate the suspicious statements possible for causing a program failure. Experimental evidence shows that fault localization effectiveness is affected adversely by the existence of coincidental correctness (CC) test cases, where a CC test case denotes the test case which executes a fault but no failure occurs. Even worse, CC test cases are prevailing in realistic testing and debugging, leading to a severe issue on fault localization effectiveness. Thus, it is indispensable to accurately detect CC test cases and alleviate their harmful effect on fault localization effectiveness.To address this problem, we propose NeuralCCD: a neural coincidental correctness detection approach by integrating multiple features. Specifically, NeuralCCD first leverages suspiciousness score, coverage ratio and similarity to define three CC detection features. Based on these CC detection features and CC labels, NeuralCCD utilizes multi-layer perceptron to learn a different feature-based model for a program, and finally combine the trained models of different programs as an ensemble system to detect CC test cases. To evaluate the effectiveness of NeuralCCD, we conduct large-scale experiments on 247 faulty version of five representative benchmarks and compare NeuralCCD with four state-of-the-art CC detection approaches. The experimental results show that NeuralCCD significantly improves the effectiveness of CC detection, e.g., NeuralCCD yields by at most 109.5%, 93% and 81.3% improvement of Top-1, Top-3 and Top-5 over Tech-I when utilized in Dstar formular.