arXiv AI

Relational Preference Encoding in Looped Transformer Internal States

arXiv:2604. 09870v2 Announce Type: replace-cross Abstract: We investigate how looped transformers encode human preference, training lightweight evaluator heads on frozen Ouro-2.

arXiv Machine Learning
Jun 2

Measuring the Symmetry--Data Exchange Rate

arXiv:2606. 01090v1 Announce Type: cross Abstract: Equivariance theory predicts that an architectural symmetry prior reduces sample complexity by a factor of |G|; this is widely cited but rarely measured as a scaling law with controls that separate the prior from its confounds.

By Ahmed M. Adly
arXiv AI
Jul 21

First-Order Predictable but Pairwise Fragile: Local Task Adaptation in Trained Transformers

arXiv:2607. 16821v1 Announce Type: cross Abstract: Task arithmetic, sequential fine-tuning, activation steering, and first-order random search all operate through relatively small perturbations around an already trained checkpoint, and they rely on different local approximations: individual perturbations should be first-order predictable, task updates should compose with controlled interference, useful tangent structure should be stable and possible to estimate, and weight edits should have counterparts in representation space.

By Irina Piontkovskaia, Sergey Nikolenko