A gated multi-task architecture with outcome taxonomy achieves state-of-the-art legal outcome prediction on 13,937 UK Employment Tribunal cases by disentangling judge identity, using far fewer parameters than generative fine-tuning of a 26B model.
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Mixture-of-experts fusing multiple pretrained forecasters achieves strongest performance on influenza time series, with pretraining gains largest at longer horizons when domain-aligned and LLM methods underperforming.
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Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning
A gated multi-task architecture with outcome taxonomy achieves state-of-the-art legal outcome prediction on 13,937 UK Employment Tribunal cases by disentangling judge identity, using far fewer parameters than generative fine-tuning of a 26B model.
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Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting
Mixture-of-experts fusing multiple pretrained forecasters achieves strongest performance on influenza time series, with pretraining gains largest at longer horizons when domain-aligned and LLM methods underperforming.