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Most biological reasoning models fail to utilize their biological foundation model inputs, instead relying on textual data even when presented with conflicting biological information.
Post-training strategies can reshape biological reasoning models in unexpected ways, revealing that more supervision doesn't always mean better generalization.
Current AI agents excel in structured tasks but falter at generating novel insights and tackling open-ended scientific challenges.
