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LLMs may excel at strategic thinking, but they lag behind specialized models in practical synthesis planning, highlighting a critical gap in current AI capabilities.
The first clean nonconvex convergence guarantee for pipeline parallelism reveals surprising trade-offs between methods depending on the training objective.
Forget scaling up: a smaller, purpose-trained Liquid Foundation Model (LFM), guided by the MMAI Gym, beats larger general-purpose models at drug discovery tasks.
