Researchers present a multi-head feed-forward neural network that jointly estimates conditional average treatment effects and the full distribution of those effects. The approach is framed as a generative learner for distributional causal effects, aimed at capturing heterogeneity beyond single-point estimates. It is a preprint posted to arXiv.
A new arXiv paper proposes pairing an energy-based model with an autoencoder in a cooperative training setup. The method alternates between refining the learned energy landscape and projecting samples back onto a data manifold, aiming to overcome common training difficulties in energy-based generative models. The authors report the framework assigns low energy to realistic samples and higher energy to unlikely ones.
A new arXiv preprint proposes a diffusion-based multiple-shooting method for indirect optimal control, aimed at generating fuel-efficient spacecraft trajectories. The authors argue that prior diffusion-based control work has largely neglected optimality guarantees, which their approach seeks to address. The work sits at the intersection of generative modeling, robotics-style control, and aerospace trajectory design.
A new arXiv preprint argues that continuous and discrete flow matching, typically developed as separate frameworks, are dual formulations of the same underlying construction. The authors show that applying a position-wise argmax to continuous convex-interpolant flows with one-hot targets recovers the discrete counterpart, and they examine how the choice of source distribution shapes categorical generation. A revised version of the submission has replaced the initial preprint.
A machine learning preprint on arXiv introduces a generative transport method aimed at datasets that concentrate near a low-dimensional structure embedded in a high-dimensional space. The approach seeks to limit probability mass leaking away from the data-supporting manifold while staying computationally practical, in contrast to the iterative sampling used by diffusion models. The paper was updated as a v2 cross-list replacement.