5 papers
AutoIndex: Learning Representation Programs for Retrieval
Sam O'Nuallain, Nithya Rajkumar, Ramya Narayanasamy +3
We present AutoIndex, a framework for learning representation programs: executable transformations that map raw documents into the representations exposed to a retrieval system. Ra…
Representation Matters in Randomized Smoothing for Audio Classification
Jong-Ik Park, Shreyas Chaudhari, José M. F. Moura +1
Randomized smoothing (RS) certifies robustness in the vector space where Gaussian noise is added. In audio classification, this space is often not uniquely defined as standard pipe…
Test-Time Strategies for More Efficient and Accurate Agentic RAG
Brian Zhang, Deepti Guntur, Zhiyang Zuo +7
Retrieval-Augmented Generation (RAG) systems face challenges with complex, multihop questions, and agentic frameworks such as Search-R1 (Jin et al., 2025), which operates iterative…
Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis
Reshmi Ghosh, Rahul Seetharaman, Hitesh Wadhwa +6
Retrieval Augmented Generation (RAG) is a widely used approach for leveraging external context in several natural language applications such as question answering and information r…
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries
Hitesh Wadhwa, Rahul Seetharaman, Somyaa Aggarwal +6
Retrieval Augmented Generation (RAG) enriches the ability of language models to reason using external context to augment responses for a given user prompt. This approach has risen…