Tengjun Jin

According to our database, Tengjun Jin authored at least 11 papers between 2024 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

ELT-Bench-Verified: Benchmark Quality Issues Underestimate AI Agent Capabilities.

Christopher Zanoli, Andrea Giovannini, Tengjun Jin, Ana Klimovic, Yotam Perlitz

CoRR, March, 2026

ReViSQL: Achieving Human-Level Text-to-SQL.

Yuxuan Zhu, Tengjun Jin, Yoojin Choi, Daniel Kang

CoRR, March, 2026

Accelerating Approximate Analytical Join Queries over Unstructured Data with Statistical Guarantees.

Yuxuan Zhu, Tengjun Jin, Chenghao Mo, Daniel Kang

CoRR, March, 2026

Pervasive Annotation Errors Break Text-to-SQL Benchmarks and Leaderboards.

Tengjun Jin, Yoojin Choi, Yuxuan Zhu, Daniel Kang

Proc. VLDB Endow., January, 2026

Text-to-SQL Benchmarks are Broken: An In-Depth Analysis of Annotation Errors.

Tengjun Jin, Yoojin Choi, Yuxuan Zhu, Daniel Kang

Proceedings of the 16th Conference on Innovative Data Systems Research, 2026

2025

ELT-Bench: An End-to-End Benchmark for Evaluating AI Agents on ELT Pipelines.

Tengjun Jin, Yuxuan Zhu, Daniel Kang

Proc. VLDB Endow., October, 2025

Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation.

Sayash Kapoor, Benedikt Stroebl, Peter Kirgis, Nitya Nadgir, Zachary S. Siegel, Boyi Wei, Tianci Xue, Ziru Chen, Felix Chen, Saiteja Utpala, Franck Ndzomga, Dheeraj Oruganty, Sophie Luskin, Kangheng Liu, Botao Yu, Amit Arora, Dongyoon Hahm, Harsh Trivedi, Huan Sun, Juyong Lee, Tengjun Jin, Yifan Mai, Yifei Zhou, Yuxuan Zhu, Rishi Bommasani, Daniel Kang, Dawn Song, Peter Henderson, Yu Su, Percy Liang, Arvind Narayanan

CoRR, October, 2025

Establishing Best Practices for Building Rigorous Agentic Benchmarks.

Yuxuan Zhu, Tengjun Jin, Yada Pruksachatkun, Andy Zhang, Shu Liu, Sasha Cui, Sayash Kapoor, Shayne Longpre, Kevin Meng, Rebecca Weiss, Fazl Barez, Rahul Gupta, Jwala Dhamala, Jacob Merizian, Mario Giulianelli, Harry Coppock, Cozmin Ududec, Jasjeet S. Sekhon, Jacob Steinhardt, Antony Kellerman, Sarah Schwettmann, Matei Zaharia, Ion Stoica, Percy Liang, Daniel Kang

CoRR, July, 2025

PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees.

Yuxuan Zhu, Tengjun Jin, Stefanos Baziotis, Chengsong Zhang, Charith Mendis, Daniel D. Kang

Proc. ACM Manag. Data, June, 2025

PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees (Technical Report).

Yuxuan Zhu, Tengjun Jin, Stefanos Baziotis, Chengsong Zhang, Charith Mendis, Daniel Kang

CoRR, March, 2025

2024

AIDB: a Sparsely Materialized Database for Queries using Machine Learning.

Tengjun Jin, Akash Mittal, Chenghao Mo, Jiahao Fang, Chengsong Zhang, Timothy Dai, Daniel Kang

Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning, 2024