The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2608. 15976v1 Announce Type: new Abstract: A learning system can occupy execution states that are indistinguishable under every declared present-behavior readout yet respond differently to future training.
A learning system can occupy execution states that are indistinguishable under every declared present-behavior readout yet respond differently to future training. We formalize this through fiber fingerprints: controlled future-learning response laws restricted to present-behavior equivalence classes.
The paper presents a comprehensive framework for comparing parameter‑efficient fine‑tuning (PEFT) methods by treating each as a smooth map from a small parameter space into weight space, with a pretrained checkpoint as the base point. It identifies three key invariants—image near the base point, fibre dynamics, and base change—that explain expressivity, training dynamics, and initialization behavior. The authors prove numerous results about common PEFT techniques (e.g., LoRA, Householder adaptation) and classify 382 methods from 138 papers using seven coordinate descriptors.
arXiv:2607. 20887v1 Announce Type: cross Abstract: Model merging combines independently trained or fine-tuned models, but pairwise alignability does not imply globally consistent alignment.
The paper investigates the folded transonic flutter boundary of Isogai’s case A aerofoil, whose elastic axis is positioned half a chord ahead of the leading edge. Using coupled unsteady Euler simulations, a Gaussian‑process surrogate trained via batch active learning, and split conformal prediction, the authors map how the boundary changes as the elastic axis is moved, revealing that the fold exists only when the axis is more than ~0.4 chord ahead and closes between 0.39 and 0.33 chord ahead. Energy budget analyses explain the lower and upper branches of the fold, showing how shock phase and damping of the supersonic surface govern the flutter behavior.
HySPE introduces a hyperbolic symplectic positional encoding that uses dual shears to create a conformally symplectic contraction with two spectral decay rates per channel pair. It eliminates exponential drift by diagonalizing the operator and applying blockwise coordinate rebasing, ensuring length‑independent numerical bounds while matching the forward latency of RoPE. Experiments on TinyShakespeare and a 51M‑parameter Transformer show HySPE maintains stable perplexity at long sequence lengths, outperforming RoPE in extrapolation scenarios.