8 citations · 9 across the 4 of their papers we have counts for
4 papers
Characterizing Deep Research: A Benchmark and Formal Definition
Abhinav Java, Ashmit Khandelwal, Sukruta Midigeshi +6
Information tasks such as writing surveys or analytical reports require complex search and reasoning, and have recently been grouped under the umbrella of \textit{deep research} --…
FrugalRAG: Less is More in RL Finetuning for Multi-Hop Question Answering
Abhinav Java, Srivathsan Koundinyan, Nagarajan Natarajan +1
Reinforcement learning (RL) based on the final answer's reward has driven recent progress in small language models (SLMs) on reasoning-heavy tasks such as math and code. However, a…
Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation
Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha +3
We introduce Plan*RAG, a novel framework that enables structured multi-hop reasoning in retrieval-augmented generation (RAG) through test-time reasoning plan generation. While exis…
GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval
Daman Arora, Anush Kini, Sayak Ray Chowdhury +3
Given a query and a document corpus, the information retrieval (IR) task is to output a ranked list of relevant documents. Combining large language models (LLMs) with embedding-bas…