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LLMs can significantly boost forecasting accuracy, but their effectiveness is hampered by measurement limitations and sensitivity to input changes.
PertMind reveals that leveraging cellular perturbation data can significantly enhance LLMs' biological reasoning capabilities without extensive task-specific retraining.
Hard-assigned predictors in MoP-JEPA enable accurate planning in stochastic environments, achieving up to 42 times better performance than traditional methods.
UnityShots achieves unprecedented cross-shot coherence in multi-shot video generation, outperforming open-source benchmarks and rivaling top closed-source systems.