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Shanghai Artificial Intelligence Laboratory
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Foundation models struggle to leverage expanded biological context for cellular perturbation reasoning, with accuracy gains often negated by evidence composition or assignment shifts.
Tree search methods can significantly enhance reasoning in LLMs, revealing that traditional decoding approaches may be holding back their potential.
SciReasoner achieves a remarkable 31% improvement in Cellular Component annotation for low-homology proteins, showcasing the power of structural reasoning in AI.