6 papers
SANA: What Matters for QA Agents over Massive Data Lakes?
Austin Senna Wijaya, Jiaxiang Liu, Haonan Wang +1
Exploratory question answering (EQA) over data lakes requires an LLM agent to discover relevant sources, analyze retrieved data, and adapt its actions based on intermediate results…
LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake
Haonan Wang, Jiaxiang Liu, Yurong Liu +11
Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved. In cont…
Data Flow Control: Data Safety Policies for AI Agents
Charlie Summers, Eugene Wu
Agents increasingly generate SQL, orchestrate pipelines, and automate data analysis on behalf of users. While recent work improves query correctness, correctness is not safety. A q…
BranchBench: Aligning Database Branching with Agentic Demands
Elaine Ang, Sam Weldon, In Keun Kim +3
Branchable databases are evolving from developer tools to infrastructure for agentic workloads characterized by speculative mutations and non-linear state exploration. Traditional…
An approach for systematic decomposition of complex llm tasks
Tianle Zhou, Jiakai Xu, Guanhong Liu +3
Large Language Models (LLMs) suffer from reliability issues on complex tasks, as existing decomposition methods are heuristic and rely on agent or manual decomposition. This work i…
Toward Systems Foundations for Agentic Exploration
Jiakai Xu, Tianle Zhou, Eugene Wu +1
Agentic exploration, letting LLM-powered agents branch, backtrack, and search across many execution paths, demands systems support well beyond today's pass-at-k resets. Our benchma…