Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy 事件

Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy arXiv:2606.03142v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) show strong visualization interpretation, yet it is unclear whether their responses reflect genuine reasoning over visual evidence or factual priors learned during training. Current evaluations mix these two sources, obscuring when correct visual interpretation is overridden by memorized facts. We present a framework that isolates vis