Disha Ahuja

Long Chen, Ryan Razkenari, Yuxuan Zhou et al.

As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG and Agentic RAG. We provide implementation for 9 standardized RAG scenarios, and conduct experiments for a comprehensive comparison. These scenarios are designed for real use cases regarding data and domain restrictions, spanning from simple document-based retrieval to advanced features such as hybrid text-graph retrieval, integration with computed or pre-defined domain knowledge graphs, agentic multi-step planning, and agent-graph integration. Besides, we present a novel context engineering method for GraphRAG and Agentic RAG, addressing the context/memory overflow issues, efficiently managing text and graph retrievals with new representations and agentic loop design, leading to 19%-53% reduction on token usage. Moreover, further analysis identifies a retrieval-generation gap where expanded retrieval does not proportionally improve generation quality, suggesting retrieval-oriented metrics overstate advanced retrieval benefits. This work provides data-driven insights on when and how to use them for building production-ready intelligent RAG systems.

Arun CS Kumar, Disha Ahuja, Ashwath Aithal

A novel, adaptive ground-aware, and cost-effective 3D Object Detection pipeline is proposed. The ground surface representation introduced in this paper, in comparison to its uni-planar counterparts (methods that model the surface of a whole 3D scene using single plane), is far more accurate while being ~10x faster. The novelty of the ground representation lies both in the way in which the ground surface of the scene is represented in Lidar perception problems, as well as in the (cost-efficient) way in which it is computed. Furthermore, the proposed object detection pipeline builds on the traditional two-stage object detection models by incorporating the ability to dynamically reason the surface of the scene, ultimately achieving a new state-of-the-art 3D object detection performance among the two-stage Lidar Object Detection pipelines.