Agent Benchmarks and Evaluation

Benchmarks, environments, and evaluation methodology for coding, tool use, computer use, and expert-level professional work.

My team builds benchmarks and environments for coding agents, tool and MCP use, computer use, and expert-level professional work, and develops the evaluation methodology behind Scale’s public leaderboards.

Recent papers include READY (Chatrath et al., 2026), a framework for qualifying agents for reliable enterprise deployment under human oversight and cost constraints; ChainWorld (Siu et al., 2026), which composes atomic OSWorld tasks into long-horizon desktop workloads and shows that current computer-use agents complete at most 31% of them; and DrugDiscoveryBench (Akyürek et al., 2026), an expert-curated benchmark of early-stage drug-discovery tasks for coding agents (see also AI for Science).

References

2026

  1. READY or Not: Reliable Enterprise Agent Deployment
    Veronica Chatrath, Bryan Zhu, Jingxuan Fan, George Pu, Soham Dinesh Tiwari, Soham Dan, Ryan Young, Yuan Li, Yuang Yao, Apaar Shanker, Minglai Yang, Daniel Yue Zhang, Yunzhong He, Ying Liu, Chenguang Wang, and 2 more authors
    arXiv preprint arXiv:2609.02095, Sep 2026
  2. ChainWorld: Composing Long-Horizon Desktop Workloads from Atomic OSWorld Tasks
    Vincent Siu, Manasi Sharma, Dawn Song, Daniel Yue Zhang, Chenguang Wang, and Ying Liu
    arXiv preprint arXiv:2606.21654, Jun 2026
  3. Report
    DrugDiscoveryBench: Can Coding Agents Assist Early-Stage Drug Discovery?
    Afra Feyza Akyürek, Xinming Tu, Alec Gutmanstein, Jason Qin, Divyansh Agarwal, Sofia Monasdotter, Sergey Chekhov, Brenda Hernandez Villegas, Kirill Chugunov, Judah Engel, Veronica Chatrath, Oscar Kavanagh, Geobio Boo, Ernesto Hernandez, Ying Liu, and 7 more authors
    Scale AI technical report, with Phylo , Jun 2026