Agentic Information Seeking for Knowledge Acquisition by Revanth Reddy

Venue: SIT001

Abstract:  The vast expansion of online information has shifted the challenge from simply locating data to efficiently filtering and aggregating relevant content from diverse sources. This talk introduces innovative methodologies aimed at enhancing automated information seeking capabilities within intelligent systems. I will present a modular, agent-based framework that decomposes the information-seeking process into navigation, extraction, and aggregation components. This approach enables exploratory behaviors that significantly outperform current web agents. Next, I will discuss the application of these techniques to tackle novel challenges in knowledge acquisition across various domains: 1) Automated Wikipedia Updates: An approach to automatically update and maintain Wikipedia articles using real-time online sources, and 2) Software Issue Localization: How enabling information-seeking over code repositories helps identify and address localization issues for software problems.

Bio: Revanth Gangi Reddy is a research scientist at Google DeepMind, working on Gemini Post-Training. He finished his Ph.D. in Computer Science at the University of Illinois Urbana-Champaign, advised by Prof. Heng Ji. His research interests lie in knowledge-driven natural language processing, focusing on agentic search, ranking models, and retrieval-augmented generation. Revanth’s work has been published in leading conferences such as ICLR, ACL, AAAI, EMNLP, NAACL, and SIGIR, and he also presented a tutorial on Open-Retrieval Question Answering at IJCAI 2023. Revanth has previously done research internships at Salesforce Research, Apple, AI2, IBM Research, and Amazon Science, and was the team lead for UIUC at the Alexa SocialBot Grand Challenge 5. Revanth holds a Bachelor’s degree in Computer Science from the Indian Institute of Technology Madras and is a Siebel Scholar (class of 2022).