OTROPE uses optimal transport in semantic space to correct off-policy LLM evaluation without likelihoods or density ratios, yielding consistent doubly robust estimators that outperform baselines.
Vision transformers learn Gestalt-like figure-ground cues, surroundedness, convexity, and symmetry for uniform regions, from natural images, with linear probes generalizing zero-shot to artificial stimuli.
MARS replaces ratio-based trust regions with a multiplicatively symmetric geometric barrier to cut variance and prevent probability collapse in multi-agent policy optimization. Across 47 tasks it matches or exceeds MAPPO and MASPO, with gains from barrier geometry rather than flexible boundaries.
RegimeVGGT removes layer-wise spatial redundancy in VGGT via U-shaped cross-frame compression, yielding 6.7x speedup with preserved geometry and pose accuracy.