pith. sign in

arXiv preprint arXiv:2408.10920 , year =

4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it

years

2026 3 2025 1

verdicts

UNVERDICTED 4

representative citing papers

Predicting Where Steering Vectors Succeed

cs.LG · 2026-04-16 · unverdicted · novelty 6.0

The Linear Accessibility Profile predicts steering vector effectiveness and optimal layers with Spearman correlations of 0.86-0.91 using unembedding projections on intermediate states across multiple models and concepts.

There Will Be a Scientific Theory of Deep Learning

stat.ML · 2026-04-23 · unverdicted · novelty 2.0

A mechanics of the learning process is emerging in deep learning theory, characterized by dynamics, coarse statistics, and falsifiable predictions across idealized settings, limits, laws, hyperparameters, and universal behaviors.

citing papers explorer

Showing 4 of 4 citing papers.

  • Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior cs.LG · 2026-05-06 · unverdicted · none · ref 207

    Manifold steering along activation geometry induces behavioral trajectories matching the natural manifold of outputs, while linear steering produces off-manifold unnatural behaviors.

  • Predicting Where Steering Vectors Succeed cs.LG · 2026-04-16 · unverdicted · none · ref 3

    The Linear Accessibility Profile predicts steering vector effectiveness and optimal layers with Spearman correlations of 0.86-0.91 using unembedding projections on intermediate states across multiple models and concepts.

  • AR-KAN: Autoregressive-Weight-Enhanced Kolmogorov-Arnold Network for Time Series Forecasting cs.LG · 2025-09-03 · unverdicted · none · ref 11

    AR-KAN combines a pre-trained AR module with KAN to reduce redundancy while preserving temporal features, delivering lower probabilistic approximation error and stronger forecasting results on synthetic almost-periodic signals and real datasets.

  • There Will Be a Scientific Theory of Deep Learning stat.ML · 2026-04-23 · unverdicted · none · ref 221

    A mechanics of the learning process is emerging in deep learning theory, characterized by dynamics, coarse statistics, and falsifiable predictions across idealized settings, limits, laws, hyperparameters, and universal behaviors.