Cross-trajectory negative sampling in contrastive predictive objectives causes encoding of slow noise over dynamics; intra-trajectory sampling eliminates the shortcut and recovers dynamical variables even under strong noise.
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4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
JEPA-style objectives discard exogenous control-relevant features because they optimize temporal predictability; reward grounding recovers them with as little as 2% labeled data.
A training-free pipeline combining SAM3 for masks and transformed DINOv3 embeddings for prototype matching delivers the first reported baseline for fine-grained fungi segmentation across one-shot to few-hundred-shot regimes.
AdaJEPA performs closed-loop test-time adaptation of latent world models during MPC by executing an action chunk, observing the transition, and taking one gradient step on the model before replanning, yielding higher goal-reaching success.
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Training-Free Fine-Grained Semantic Segmentations in Low Data Regimes: A FungiTastic Baseline
A training-free pipeline combining SAM3 for masks and transformed DINOv3 embeddings for prototype matching delivers the first reported baseline for fine-grained fungi segmentation across one-shot to few-hundred-shot regimes.