# BioXArena-Pro: a research-grade benchmark for autonomous biomedical machine learning agents

**Authors:** Loka Li, Duzhen Zhang, Xingbo Du, Leonard Song, Kun Zhang, Le Song  
**Lab:** Hugging Face · **Published:** 10 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.13994) · [PDF](https://arxiv.org/pdf/2611.13994) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.13994.html)

## Key numbers

- **64**: 64 research-level modelling tasks from eight biomedical domains

## Abstract

Coding agents are increasingly proposed as autonomous builders of biomedical models, but existing benchmarks are small, near saturation or open to leaked information. We present BioXArena-Pro, 64 research-level modelling tasks from eight biomedical domains, each rebuilt from a published study; most hold out whole biological units such as species or cohorts. Across 22 agent configurations, the best scores 55.1 of 100, and the scaffold matters almost as much as the model. We re-ran a published method for every task: where the published recipe applies unchanged, the best agent and the methods tie; on rebuilt tasks the methods trail by 8.2 points, suggesting that agents gain by adapting to new data. Pretrained models (including biological foundation models) matter where the strongest agent uses them: forbidding them decreases its score on those tasks by 9 points. Every task was audited in several AI-assisted rounds and a final human review.

---

Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.