VENUSS evaluates 25+ VLMs across 2600+ sequential driving scenarios and finds top models reach only 57% accuracy versus 65% for humans, with good static detection but poor performance on vehicle dynamics and temporal relations.
Applications, challenges, and future directions of human-in-the-loop learning,
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Sentra-Guard reports 99.96% detection of adversarial LLM prompts with AUC 1.00 and ASR of 0.004% using a hybrid SBERT-FAISS and transformer classifier architecture with multilingual translation and human feedback.
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How Well Do Vision-Language Models Understand Sequential Driving Scenes? A Sensitivity Study
VENUSS evaluates 25+ VLMs across 2600+ sequential driving scenarios and finds top models reach only 57% accuracy versus 65% for humans, with good static detection but poor performance on vehicle dynamics and temporal relations.
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Sentra-Guard: A Real-Time Multilingual Defense Against Adversarial LLM Prompts
Sentra-Guard reports 99.96% detection of adversarial LLM prompts with AUC 1.00 and ASR of 0.004% using a hybrid SBERT-FAISS and transformer classifier architecture with multilingual translation and human feedback.