Interview with Yash Saxena: how is external knowledge used in AI systems?

Summary

In a new series of interviews, we’re meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) . In this first article of the series, we caught up with Yash Saxena to find out how he is trying to make it easier to understand how external knowledge is used in AI systems. Tell us a bit about your PhD – where are you studying, and what is the topic of your research? I am a PhD student in Computer Science at the University of Maryland, Baltimore County (UMBC), advised by Dr. Manas Gaur in the Knowledge Infused AI and Inference (KAI2) Lab. My PhD focuses on making AI systems that use external information easier to understand and audit. Many AI systems now look through external documents before answering a question. My research follows what happens to that evidence as it moves through the system. I study where it came from, why certain evidence was selected, whether it actually supports the claim being made, and whether the context needed to interpret that evidence correctly is preserved along the way. I call this broader research direction IKnowFlow , or Interpretable Knowledge Flow . The aim is to keep the evidence trail visible so that someone can inspect a generated claim and understand how the system arrived at it. This is especially important in areas such as law, science, and healthcare, where an incorrect AI-generated claim can have serious real-world consequences. Could you give us an overview of the research you’ve carried out so far during your PhD? I started by studying source attribution, or how an AI-generated claim can be linked back to the source that supports it. In REASONS (REtrieval and Automated citationS Of scieNtific Sentences), we evaluated retrieval and citation generation across more than 20,000 scientific papers from 12 research areas. I later compared two ways of adding citations to large language model responses: generating them together with the answer or assigning them afterward. In our human evaluation, adding citations afterward was more accurate, but this also showed that a citation alone does not necessarily reveal how the model arrived at its answer. From there, I started looking earlier in the process. Interpretable Modular Retrieval Neural Networks (IMR…