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20242026
most citedSupporting Our AI Overlords: Redesigning Data Systems to be Agent-First

2 citations · 3 across the 12 of their papers we have counts for

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10 papers · 1 filter

cs.DB2026

The Time is Here for Just-in-Time Systems: Challenges and Opportunities

Shu Liu, Alexander Krentsel, Shubham Agarwal +8

Core systems like key-value stores have historically taken years to build, and are designed to be general so as to amortize cost across deployments, paying a significant performanc…

cs.DB2026

PLOP: Cost-Based Placement of Semantic Operators in Hybrid Query Plans

Qiuyang Mang, Yufan Xiang, Hangrui Zhou +5

Recent database systems have introduced semantic operators that leverage large language models (LLMs) to filter, join, and project over structured data using natural language predi…

cs.DB2026

Semantic Data Processing with Holistic Data Understanding

Youran Sun, Sepanta Zeighami, Bhavya Chopra +2

Semantic operators have increasingly become integrated within data systems to enable processing data using Large Language Models (LLMs). Despite significant recent effort in improv…

cs.DB2026

Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents

Ruiying Ma, Shreya Shankar, Ruiqi Chen +7

However, building reliable data agents remains difficult because real enterprise data is fragmented across many heterogeneous database systems, with duplicated and inconsistent dat…

cs.DB2026

Arming Data Agents with Tribal Knowledge

Shubham Agarwal, Asim Biswal, Sepanta Zeighami +3

Natural language to SQL (NL2SQL) translation enables non-expert users to query relational databases through natural language. Recently, NL2SQL agents, powered by the reasoning capa…

cs.DB2026

Task Cascades for Efficient Unstructured Data Processing

Shreya Shankar, Sepanta Zeighami, Aditya Parameswaran

Modern database systems allow users to query or process unstructured text or document columns using LLM-powered functions. Users can express an operation in natural language (e.g.,…

cs.DB2025

Featurized-Decomposition Join: Low-Cost Semantic Joins with Guarantees

Sepanta Zeighami, Shreya Shankar, Aditya Parameswaran

Large Language Models (LLMs) are being increasingly used within data systems to process large datasets with text fields. A broad class of such tasks involves a semantic join-joinin…

cs.DB2025

Multi-Objective Agentic Rewrites for Unstructured Data Processing

Lindsey Linxi Wei, Shreya Shankar, Sepanta Zeighami +3

One year ago, we open-sourced DocETL, a declarative system for LLM-powered data processing that, as of March 2026, has 3.7K GitHub stars and users across domains (e.g., journalism,…

cs.DB2025

Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees

Sepanta Zeighami, Shreya Shankar, Aditya Parameswaran

Large Language Models (LLMs) are being increasingly used as a building block in data systems to process large text datasets. To do so, LLM model providers offer multiple LLMs with…

cs.DB2025

LLM-Powered Proactive Data Systems

Sepanta Zeighami, Yiming Lin, Shreya Shankar +1

With the power of LLMs, we now have the ability to query data that was previously impossible to query, including text, images, and video. However, despite this enormous potential,…