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LLMs can beat traditional time-series models by orchestrating specialized agents in a dynamic workflow, iteratively refining forecasts with memory and ensemble methods.
LLMs get a reasoning boost by treating information extraction not as a one-off task, but as a dynamic cache that persists and filters information across multiple steps.
Asymmetric encoders, trained with a novel two-stage approach, can beat symmetric LLM-based models in Chinese medical text retrieval while maintaining low latency.
