BioXArena: Benchmarking LLM Agents on Multi-Modal Biomedical Machine Learning Tasks

Large language model (LLM) agents can now automate parts of machine-learning model building, but biomedical benchmarks still either emphasize question answering, reasoning, and tool use, or cover only narrow slices of biomedical ML coding. We introduce BioXArena, a biomedical machine learning (BioML) coding benchmark that evaluates whether agents can create task-specific model-building code for heterogeneous, often multi-modal biomedical datasets. It contains 76 end-to-end tasks across 9 domains: sequence, single-cell, structure, network biology, chemical biology, perturbation dynamics, phenotype--disease, imaging, and text-integrated tasks. Each task is curated from primary sources into a unified public capsule with hidden labels, held-out graders, and biology-aware metrics on a common 0-to-1 scale; agents must write runnable code, train models, and submit predictions for private test samples. BioXArena emphasizes realistic data interfaces: most tasks combine multiple input sources, and more than half are multi-modal, spanning tables, images, text, molecular sequences, omics matrices, and protein structures. We evaluate 11 agent configurations, including general coding LLMs, biomedical agents, and ML coding agents, in a shared 2-hour, single-GPU sandbox. MLEvolve with Gemini-3.1-Pro obtains the highest average score of 0.666, followed by GPT-5.4 with an average score of 0.636; no agent dominates across all domains. Beyond the main leaderboard, we conduct extensive ablation studies, robustness checks, scaling analyses, cost analyses, and failure-mode analyses to characterize how backbones, scaffolds, budgets, and domains affect BioML coding performance. We will release all tasks, graders, runner scripts, leaderboard results, and agent traces.