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0 results for “generative modeling”
ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models
ChronoSSM is a new autoregressive State Space Model that jointly trains on both event tokens and timestamps to improve temporal reasoning in sequence modeling, addressing a gap where timing is typically treated as secondary to event prediction.
Aug 12, 2026
From Approximation to Emergence: A Theory of Deep Learning
A new arXiv monograph proposes a unified theoretical framework for deep learning, positioning emergence—not just approximation—as the central organizing principle of modern AI theory.
Published Jul 3, 2026 · Analyzed Jul 6, 2026
SNAP-FM: Sparse Nonlinear Accelerated Projection for Physics-Constrained Generative Modeling
Researchers propose a new method to accelerate physics-constrained generative modeling.
Published Jul 2, 2026 · Analyzed Jul 5, 2026