BABE: Biology Arena BEnchmark

2026-02-05

AI Summary

Existing biological benchmarks struggle to evaluate the capacity of AI systems to perform scientific reasoning by integrating experimental results with background knowledge. To address this gap, this work introduces a comprehensive evaluation benchmark grounded in real peer-reviewed publications and biological research data, uniquely centered on causal reasoning and cross-scale inference within authentic scientific contexts. Leveraging large language models, the framework designs multi-task reasoning challenges that systematically assess AI’s ability to synthesize experimental observations with prior knowledge in a manner akin to scientific reasoning. This study fills a critical void in the assessment of scientific reasoning capabilities within biological AI and establishes an evaluation framework more closely aligned with the demands of real-world scientific inquiry.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

The rapid evolution of large language models (LLMs) has expanded their capabilities from basic dialogue to advanced scientific reasoning. However, existing benchmarks in biology often fail to assess a critical skill required of researchers: the ability to integrate experimental results with contextual knowledge to derive meaningful conclusions. To address this gap, we introduce BABE(Biology Arena BEnchmark), a comprehensive benchmark designed to evaluate the experimental reasoning capabilities of biological AI systems. BABE is uniquely constructed from peer-reviewed research papers and real-world biological studies, ensuring that tasks reflect the complexity and interdisciplinary nature of actual scientific inquiry. BABE challenges models to perform causal reasoning and cross-scale inference. Our benchmark provides a robust framework for assessing how well AI systems can reason like practicing scientists, offering a more authentic measure of their potential to contribute to biological research.

Problem

• biological reasoning
• experimental reasoning
• scientific benchmark
• causal reasoning
• cross-scale inference

Innovation

• experimental reasoning
• causal reasoning
• cross-scale inference
• biological benchmark
• scientific reasoning