6 papers
CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning
Chung-En Johnny Yu, David Garcia, Brian Jalaian +1
Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model's confidence nor that of the aggregated answer measures re…
SCoOP: Semantic Consistent Opinion Pooling for Uncertainty Quantification in Multiple Vision-Language Model Systems
Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian
Combining multiple Vision-Language Models (VLMs) can enhance multimodal reasoning and robustness, but aggregating heterogeneous models' outputs amplifies uncertainty and increases…
Visual Reasoning Agent: Robust Vision Systems in Remote Sensing via Inference-Time Scaling
Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian
Building robust vision systems for high-stakes domains such as remote sensing requires stronger visual reasoning than what single-pass inference typically provides; yet, retraining…
ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models
Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian
Large Vision-Language Models (LVLMs) exhibit strong multimodal capabilities but remain vulnerable to hallucinations from intrinsic errors and adversarial attacks from external expl…
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models
Chung-En, Yu, Hsuan-Chih +3
To develop trustworthy Vision-Language Models (VLMs), it is essential to address adversarial robustness and hallucination mitigation, both of which impact factual accuracy in high-…
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI
Alice Bizzarri, Chung-En Yu, Brian Jalaian +2
The prevailing approaches in Network Intrusion Detection Systems (NIDS) are often hampered by issues such as high resource consumption, significant computational demands, and poor…