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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.05857 |
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| _version_ | 1866910013045342208 |
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| author | Zhou, Junting Chen, Jin Hao, Linfeng Cao, Denghui Wang, Zheyu Chen, Qiguang Fu, Chaoyou Chen, Jiaze Wu, Yuchen Zhang, Ge Wang, Mingxuan Huang, Wenhao Yang, Tong |
| author_facet | Zhou, Junting Chen, Jin Hao, Linfeng Cao, Denghui Wang, Zheyu Chen, Qiguang Fu, Chaoyou Chen, Jiaze Wu, Yuchen Zhang, Ge Wang, Mingxuan Huang, Wenhao Yang, Tong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05857 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BABE: Biology Arena BEnchmark Zhou, Junting Chen, Jin Hao, Linfeng Cao, Denghui Wang, Zheyu Chen, Qiguang Fu, Chaoyou Chen, Jiaze Wu, Yuchen Zhang, Ge Wang, Mingxuan Huang, Wenhao Yang, Tong Artificial Intelligence 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. |
| title | BABE: Biology Arena BEnchmark |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.05857 |