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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.

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arXiv Machine Learning

Aug 12, 2026

SPIN Processed News Frame: The Hype

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.

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arXiv Machine Learning

Published Jul 3, 2026 · Analyzed Jul 6, 2026

SPIN Processed News Frame: The Hype

SNAP-FM: Sparse Nonlinear Accelerated Projection for Physics-Constrained Generative Modeling

Researchers propose a new method to accelerate physics-constrained generative modeling.

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arXiv Machine Learning

Published Jul 2, 2026 · Analyzed Jul 5, 2026