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arxiv:2602.05857

BABE: Biology Arena BEnchmark

Published on Feb 5
ยท Submitted by
taesiri
on Feb 6
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Abstract

BABE is a biology-focused benchmark designed to evaluate AI systems' ability to perform experimental reasoning and causal inference similar to practicing scientists.

AI-generated summary

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.

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Paper submitter

BABE is a biology benchmark that evaluates AI models' experimental reasoning across papers and real studies, stressing cross-scale causal inference and practical scientific reasoning.

arXivLens breakdown of this paper ๐Ÿ‘‰ https://arxivlens.com/PaperView/Details/babe-biology-arena-benchmark-356-0aa145d3

  • Executive Summary
  • Detailed Breakdown
  • Practical Applications

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