The proliferation of agent benchmarks has created critical fragmentation that threatens research productivity. Each new benchmark requires substantial custom integration, creating an "integration tax" that limits comprehensive evaluation. We propose CUBE (Common Unified Benchmark Environments), a universal protocol standard built on MCP and Gym that allows benchmarks to be wrapped once and used everywhere. By separating task, benchmark, package, and registry concerns into distinct API layers, CUBE enables any compliant platform to access any compliant benchmark for evaluation, RL training, or data generation without custom integration. We call on the community to contribute to the development of this standard before platform-specific implementations deepen fragmentation as benchmark production accelerates through 2026.
@article{arxiv.2603.15798,
title = {CUBE: A Standard for Unifying Agent Benchmarks},
author = {Alexandre Lacoste and Nicolas Gontier and Oleh Shliazhko and Aman Jaiswal and Kusha Sareen and Shailesh Nanisetty and Joan Cabezas and Manuel Del Verme and Omar G. Younis and Simone Baratta and Matteo Avalle and Imene Kerboua and Xing Han Lù and Elron Bandel and Michal Shmueli-Scheuer and Asaf Yehudai and Leshem Choshen and Jonathan Lebensold and Sean Hughes and Massimo Caccia and Alexandre Drouin and Siva Reddy and Tao Yu and Yu Su and Graham Neubig and Dawn Song},
journal= {arXiv preprint arXiv:2603.15798},
year = {2026}
}
Comments
Position paper. 10 pages. Reference implementation: https://github.com/The-AI-Alliance/cube-standard