HalluHunter is a knowledge-graph and rule-based NLP framework that iteratively generates single- and multi-hop questions to uncover factual errors in LLMs, triggering errors in up to 55% of cases on nine models while preserving coverage.
Exploring adversarial robustness of multi-sensor perception systems in self driving
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Presents an AV Resilient architecture with redundancy, diversity, adaptive reconfiguration, and anomaly- and hash-based intrusion detection, experimentally validated on the Quanser QCar platform for detecting depth camera blinding and perception module tampering.
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Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models
HalluHunter is a knowledge-graph and rule-based NLP framework that iteratively generates single- and multi-hop questions to uncover factual errors in LLMs, triggering errors in up to 55% of cases on nine models while preserving coverage.
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Security and Resilience in Autonomous Vehicles: A Proactive Design Approach
Presents an AV Resilient architecture with redundancy, diversity, adaptive reconfiguration, and anomaly- and hash-based intrusion detection, experimentally validated on the Quanser QCar platform for detecting depth camera blinding and perception module tampering.