36 citations · 42 across the 9 of their papers we have counts for
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cs.AI2026
NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models
Deepa Tilwani, Yash Saxena, Ankur Padia +2
Large Language Models (LLMs) have transformed natural language processing, but their lack of interpretable reasoning and tendency to hallucinate pose significant challenges for leg…
cs.AI2026★ 1 cited
Neurosymbolic Retrievers for Retrieval-augmented Generation
Yash Saxena, Manas Gaur
Retrieval Augmented Generation (RAG) has made significant strides in overcoming key limitations of large language models, such as hallucination, lack of contextual grounding, and i…
cs.IR2026
IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation
Yash Saxena, Ankur Padia, Kalpa Gunaratna +1
Interpretability in black-box dense retrievers remains a central challenge in Retrieval-Augmented Generation (RAG). Understanding how queries and documents semantically interact is…