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cs.CV2026
What Do They See? Interpreting Complex Road Scenarios Through the Eyes of Vision-Language-Action Models for Safe and Trustworthy Autonomous Vehicle Learning
Kalpana Panda, Wesley Maia, Vinti Agarwal +1
End-to-end autonomous driving models are now able to navigate complex road scenarios, mapping raw sensor observations directly to observed paths for open-loop evaluation and often…
cs.CV2026
When Prompts Ignore Structure: Graph-Based Attribute Reasoning for Calibrated VLMs
Tanay Sodha, Aditya Sharma, Ramya Hebbalaguppe +2
Reliable confidence estimation remains a key limitation of test-time adaptation in vision-language models (VLMs), where prompt tuning improves zero-shot accuracy but often degrades…