新闻
When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers found that vision-language models represent spatial relationships far less faithfully than word meanings, despite performing well on compositional benchmarks.
- 为何重要
- Matters for engineers building or evaluating VLMs for tasks requiring accurate scene understanding, object relationships, or spatial reasoning.
- 注意
- The models lose spatial information during aggregation into global representations, so output-based evaluation alone misses this systematic failure.
- language model
- reasoning
- eval
- benchmark
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