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cs.CV2026
EDCT-Bench: Uncovering Faithfulness Gaps in VLMs via Explanation-Driven Counterfactual Testing
Sihao Ding, Santosh Vasa, Aditi Ramadwar +1
Vision-Language Models (VLMs) can produce Natural Language Explanations (NLEs) that sound plausible yet remain inconsistent with the visual evidence they cite. We present Explanati…
cs.CV2026
BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations
Thomas Monninger, Shaoyuan Xie, Qi Alfred Chen +1
The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic understanding abilities, which are es…