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The results show that alternative transcript diversity extensively enters translation-supported proteoform space and establish a systematic link between transcript variation and protein functional diversification.
This work investigates LLM-based forecasting agents, meaning systems in which a language model contributes to a scored prediction about a future or currently unobserved target, and organizes architectures into three groups.
Branch-JEPA is introduced, which replaces this point-valued transition with a context-weighted finite set of latent successors, and preserves more distinct futures, while full-set scoring improves the quality of the resulting predictive distribution.