MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education
Paper recorded by Signals 4 on 2026-09-16 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17
Category: cs.AI · 人工智能 · first seen 2026-09-17
Abstract
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
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Cite this page: MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education: the #31 most recent of 300 cs.AI papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/muse-benchmarking-large-vision-language-models-on-multi-modal-understanding-in-s.html
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