arXiv:2506. 11042v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific updates $\Delta W$.
By Guangning Xu, Baoquan Zhang, Michael. K. Ng
arXiv:2506. 14126v2 Announce Type: replace-cross Abstract: Modern deep learning is increasingly characterized by the use of open-weight foundation models that can be fine-tuned on specialized datasets.
By Stefan Horoi, Guy Wolf, Eugene Belilovsky, Gintare Karolina Dziugaite
The paper introduces CATTLE, a transfer learning framework for disjoint tabular datasets that eliminates the need for shared features by leveraging generalized context learned through transformer projection weights. By using key, value, and query weights from source and target domains, CATTLE performs cross‑domain attention transfer in a data‑agnostic manner. Experiments on ten source‑target pairs demonstrate that CATTLE outperforms nine state‑of‑the‑art baselines, achieving the best average rank (2.9) and a 3.7% AUROC improvement.
By Kazi F. Akhter, Ibna Kowsar, Manar D. Samad
arXiv:2603. 02462v2 Announce Type: replace-cross Abstract: A key challenge in developing unified neural solvers for combinatorial optimization (CO) is the efficient generalization of models from a given set of tasks to new tasks unseen during initial training.
By Semih Cant\"urk, Thomas Sabourin, Frederik Wenkel, Michael Perlmutter, Guy Wolf
arXiv:2608. 20061v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures significantly expand model capacity without a proportional increase in computational cost.
By Nayeon Kim, Hojin Lee, Yunju Bak, Jaesun Park, Boseop Kim
arXiv:2609.38149v1 Announce Type: new
Abstract: Transformer language models (LMs) are feed-forward: deep-layer representations are never fed back to shallower layers, and the only pathway for informa...
By Dor Tirosh, Ido Amos, Mor Geva