BABE: Biology Arena BEnchmark
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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Junting Zhou
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Jin Chen
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Linfeng Hao
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Denghui Cao
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Zheyu Wang
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2026-02-09T08:35:54.490Z
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Zheyu Wang
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Qiguang Chen
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Chaoyou Fu
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Jiaze Chen
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Yuchen Wu
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Ge Zhang
claimed_verified
2026-02-06T18:51:54.380Z
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Ge Zhang
zhangysk
user
zhangysk
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Mingxuan Wang
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Wenhao Huang
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Tong Yang
BABE: Biology Arena BEnchmark
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.
BABE is a biology-focused benchmark designed to evaluate AI systems' ability to perform experimental reasoning and causal inference similar to practicing scientists.
large language models biological AI systems experimental reasoning causal reasoning cross-scale inference peer-reviewed research papers real-world biological studies