Nishu Garg

Diversity Meets Relevancy: Multi-Agent Knowledge Probing for Industry 4.0 Applications

Industrial data scientists require deep domain understanding to model asset conditions effectively, yet traditional sources such as Subject Matter Experts (SMEs) and Failure Modes and Effects Analysis (FMEA) documents are often unavailable or incomplete. We present a deployed Multi-Agent System (MAS