arXiv:2607. 16568v1 Announce Type: new Abstract: Function-preserving network growth techniques such as Net2Net and progressive stacking expand a model's capacity without destroying its learned function, but existing formulations either tolerate numerical perturbations or require a full rebuild of the training program.
By Abdallah Khemais (ISITCOM, University of Sousse)
arXiv:2607. 20594v1 Announce Type: cross Abstract: When does a weight-tied looped transformer -- one block applied T times -- implement an actual algorithm?
By Tong Zhang, Junhao Hu, Yun Peng, Tao Xie
arXiv:2603.21676v2 Announce Type: replace-cross
Abstract: Standard Transformers have a fixed computational depth, limiting their ability to generalize to tasks that require variable-depth reasoning....
By Hung-Hsuan Chen
Spectral-Guided Diffusion introduces a method to accelerate diffusion inference by identifying and reusing residual branches that need not be recomputed during the trajectory. The approach uses a Spectral Concentration Ratio (SCR) combined with Frobenius magnitude to create an offline sensitivity proxy and deterministic lifetime for each scheduled unit, eliminating the need for routers or input-dependent searches. Experiments on models such as LLaDA-8B, DiT-XL/2, U-ViT-L, and SDXL show that this scheduling preserves quality better than several baselines and achieves up to a 3.0× wall‑clock speedup over eager inference.
By Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Anuj Sharma
arXiv:2603. 02510v2 Announce Type: replace Abstract: The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning curve of concurrent programming.
By Liu Yang, Zeyu Nie, Andrew Liu, Felix Zou, Deniz Altinb\"uken, Amir Yazdanbakhsh, Quanquan C. Liu
The paper introduces EMR‑HyperNEAT, an eager multi‑resolution grid approach that replaces the recursive quadtree subdivision of ES‑HyperNEAT with a parallelizable evaluation of all grid positions followed by a variance‑based filter. This reformulation removes sequential dependencies, enabling efficient batching across cores and population members, and reduces computational complexity from <O(4^D)> to <O(4^D/P)>. Experiments show 12–34× GPU speedups at depths 5–7 on XOR and higher solve rates across benchmarks.
By Romain Claret, Michael O'Neill, Paul Cotofrei, Kilian Stoffel