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BENCHCOMPASS is introduced, a payment-domain benchmark whose construction pipeline builds scenario-grounded tasks from typed evidence packs, applies LLM-based quality checks, creates task-input attack variants, and reserves final item admission for domain experts.
Predicting targeted protein degradation on underexplored E3 ligases no longer requires dense labeled datasets: support-conditioned prototype estimation enables zero-fine-tuning transfer across ligases with as few as two samples per class.
Humanoid robots can now sprint at 6 m/s with zero-shot sim-to-real transfer, thanks to a surprisingly data-efficient approach using frequency-domain priors learned from just five human motion sequences.
By recombining subgraphs from sparse models without retraining, "model stitching" creates a diverse set of model variants that significantly improves the efficiency of multi-DNN inference on edge SoCs.
