Tengjun Jin — accepted AI-conference papers

2026

Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation

Sayash Kapoor, Benedikt Stroebl, Peter Kirgis, Nitya Nadgir, Zachary S Siegel, Boyi Wei +25

ICLR 2026poster

AI agents have been developed for complex real-world tasks from coding to customer service. But AI agent evaluations suffer from many challenges that undermine our understanding of how well agents really work (Figure 1). We introduce the Holistic Agent Leaderboard (HAL) to address these challenges.…

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2025

Establishing Best Practices in Building Rigorous Agentic Benchmarks

Yuxuan Zhu, Tengjun Jin, Yada Pruksachatkun, Andy K Zhang, Shu Liu, Sasha Cui +20

NeurIPS 2025poster

Benchmarks are essential for quantitatively tracking progress in AI. As AI agents become increasingly capable, researchers and practitioners have introduced agentic benchmarks to evaluate agents on complex, real-world tasks. These benchmarks typically measure agent capabilities by evaluating task ou…

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