SOURCE-LINKED INTELLIGENCE
MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education
Large vision-language models have achieved remarkable progress in multi-modal understanding, yet their capabilities in educational settings remain insufficiently evaluated. In AI-assisted language learning, models must interpret artistic imagery, understand its semantic, affective, and cultural content, and reason about visual context to support meaningful interaction. However, existing benchmarks primarily focus on real-world images or domain-specific educational reasoning, providing limited coverage of artistic educational content. To address this gap, we introduce MUSE, a benchmark for eval
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T17:26:10.000Z
- arXiv · Artificial Intelligence · 2026-09-16T17:26:10.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.