How does varying the number of active experts (k) in sparse MoE vision-language models affect VQA accuracy and

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often presented as a subjectively validated binary property, we consider it a multi-faceted concept. We identify 12 conceptual properties, such as Compactness and Correctness, that should be evaluated for comprehensively assessing the quality of an explanation. Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the eva

Research goal: How does varying the number of active experts (k) in sparse MoE vision-language models affect VQA accuracy and inference latency on benchmarks like VQAv2 or GQA, and does the optimal k correlate with visual complexity metrics such as object count or scene clutter?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.3/10.