9 citations · 16 across the 7 of their papers we have counts for
9 papers
Carnot: Interpretable, Interactive, and Optimized Execution of Deep Research Queries
Matthew Russo, Yash Agarwal, Tianyu Li +5
Enterprises increasingly seek to query data lakes using natural language via AI-driven tools like semantic operators or deep research agents. However, the latter operates as an opa…
Agent-Aided Design for Dynamic CAD Models
Mitch Adler, Matthew Russo, Michael Cafarella
In the past year, researchers have created agentic systems that can design real-world CAD-style objects in a training-free setting, a new variety of system that we call Agent-Aided…
SAGE: Selective Attention-Guided Extraction for Token-Efficient Document Indexing
Xinzhi Wang, Peter Baile Chen, Gerardo Vitagliano +5
Large language models with long context windows can answer complex questions directly from full-length academic, technical, and policy documents, but passing entire documents is of…
SemBench: A Benchmark for Semantic Query Processing Engines
Jiale Lao, Andreas Zimmerer, Olga Ovcharenko +12
We present a benchmark targeting a novel class of systems: semantic query processing engines. Those systems rely inherently on generative and reasoning capabilities of state-of-the…
Deep Research is the New Analytics System: Towards Building the Runtime for AI-Driven Analytics
Matthew Russo, Tim Kraska
With advances in large language models (LLMs), researchers are creating new systems that can perform AI-driven analytics over large unstructured datasets. Recent work has explored…
KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Eugenie Lai, Gerardo Vitagliano, Ziyu Zhang +16
Discovering insights from a real-world data lake potentially containing unclean, semi-structured, and unstructured data requires a variety of data processing tasks, ranging from ex…
Abacus: A Cost-Based Optimizer for Semantic Operator Systems
Matthew Russo, Chunwei Liu, Sivaprasad Sudhir +4
LLMs enable an exciting new class of data processing applications over large collections of unstructured documents. Several new programming frameworks have enabled developers to bu…
A Declarative System for Optimizing AI Workloads
Chunwei Liu, Matthew Russo, Michael Cafarella +7
A long-standing goal of data management systems has been to build systems which can compute quantitative insights over large corpora of unstructured data in a cost-effective manner…
Accelerating Aggregation Queries on Unstructured Streams of Data
Matthew Russo, Tatsunori Hashimoto, Daniel Kang +2
Analysts and scientists are interested in querying streams of video, audio, and text to extract quantitative insights. For example, an urban planner may wish to measure congestion…