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Graph foundation models aim to transfer across graphs, feature spaces, relational schemas, and prediction tasks, yet existing approaches typically generalize only within particular graph modalities or tasks. We propose Wander, a graph foundation model designed to operate across these settings within a single pretrained...
Recently, Drifting Models and Wasserstein Gradient Flows have attracted substantial attention because they move iterative distributional refinement to training and amortize it into a generator, enabling fast inference. However, existing formulations have been developed largely for continuous Euclidean domains, such as...
Looped flows are proposed, an approach that allows solving harder problems by spending more computation through a finer temporal grid and enables multiple valid predictions from different initial noise samples.
It is shown that MVRD makes visual representations more geometric while retaining language alignment, and generalizes to 3D scene understanding tasks such as object grounding, dense captioning, and question answering, while approaching feature fusion methods with considerably fewer added parameters and lower latency.
MUX, a simple method for high-bandwidth and compact reasoning based on distillation of discrete reasoning into continuous multiplexed tokens in a latent space, is proposed and suggests that lossless superposition as local learning targets constitutes a sufficient condition for achieving strong and efficient latent cont...
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