Sasha Cui — accepted AI-conference papers

2026

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Mike A Merrill, Alexander Glenn Shaw, Nicholas Carlini, Boxuan Li, Harsh Raj, Ivan Bercovich +78

ICLR 2026poster

AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 1.5: a carefully…

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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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