IRIS unifies self-play fine-tuning via adjustable Rényi divergence with adaptive schedules, surpassing supervised fine-tuning with fewer annotations across benchmarks.
AlloSpatial is an agentic framework that converts egocentric observations into allocentric spatial priors via cognitive mapping and reasoning harnesses, improving spatial reasoning by 5%-18% and outperforming larger general-purpose models.
FTC-Seg uses orthogonal prototype reconstruction and adaptive threshold calibration to break pseudo-label degradation cycles between imaging noise and long-tail class imbalance in semi-supervised semantic segmentation.