3 papers
cs.IR2026
Web Retrieval-Aware Chunking (W-RAC) for Efficient and Cost-Effective Retrieval-Augmented Generation Systems
Uday Allu, Sonu Kedia, Tanmay Odapally +1
Retrieval-Augmented Generation (RAG) systems critically depend on effective document chunking strategies to balance retrieval quality, latency, and operational cost. Traditional ch…
cs.CL2025
The Instruction Gap: LLMs get lost in Following Instruction
Vishesh Tripathi, Uday Allu, Biddwan Ahmed
Large Language Models (LLMs) have shown remarkable capabilities in natural language understanding and generation, yet their deployment in enterprise environments reveals a critical…
cs.LG2025
Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding
Vishesh Tripathi, Tanmay Odapally, Indraneel Das +2
Retrieval-Augmented Generation (RAG) systems have revolutionized information retrieval and question answering, but traditional text-based chunking methods struggle with complex doc…