3 citations · 3 across the 2 of their papers we have counts for
5 papers
What Happens When the Model Eats the Stack? Rethinking the Research Agenda for Data Agents to Withstand the Bitter Lesson
Liana Patel, Siddharth Jha, Negar Arabzadeh +3
The bitter lesson poses an existential question for the data systems community, whereby large language models (LLMs) trained end-to-end are rapidly internalizing new capabilities t…
Measuring Agents in Production
Melissa Z. Pan, Negar Arabzadeh, Riccardo Cogo +22
LLM-based agents already operate in production across many industries, yet we lack an understanding of what technical methods make deployments successful. We present the first syst…
DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis
Liana Patel, Negar Arabzadeh, Harshit Gupta +4
The ability to research and synthesize knowledge is central to human expertise and progress. A new class of AI systems--designed for generative research synthesis--aims to automate…
Text2SQL is Not Enough: Unifying AI and Databases with TAG
Asim Biswal, Liana Patel, Siddarth Jha +5
AI systems that serve natural language questions over databases promise to unlock tremendous value. Such systems would allow users to leverage the powerful reasoning and knowledge…
Semantic Operators: A Declarative Model for Rich, AI-based Data Processing
Liana Patel, Siddharth Jha, Melissa Pan +4
The semantic capabilities of large language models (LLMs) have the potential to enable rich analytics and reasoning over vast knowledge corpora. Unfortunately, existing systems eit…