The study investigates whether a curated same-family neural network experiment can guide large language model (LLM)-based improvements for a low-performing target model under equal generation and evaluation budgets. Using TuneNNGen, an extension of NNGPT, the authors compare source-guided generation with target-only generation on CIFAR-10, SVHN, Imagenette, and CIFAR-100 datasets, achieving significant accuracy gains across these benchmarks. The results demonstrate that the benefits depend on source-target compatibility and LLM adaptation, rather than merely on stored source accuracy.
By Kabir Dev Paul Baghel, Radu Timofte, Dmitry Ignatov
arXiv:2609.10002v1 Announce Type: new
Abstract: Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and t...
By Rafael M. Mamede, Pedro C. Neto, Ana F. Sequeira
The paper investigates how different optimizers acquire distinct generalizing relations in single‑hidden‑layer ReLU networks. By manipulating nuisance‑connected weights and performing a strict‑parity sweep, the authors show that training conditions reshape both shared failures and relative optimizer advantages, with specific interventions improving accuracy on hard stages. The study also compares optimizer behavior across tasks and an external architecture (CIFAR‑10).
By Gongyue Zhang, Honghai Liu
The paper introduces a new task, Intra-Architecture Source Attribution for Stable Diffusion (IntrAA‑SD), which focuses on distinguishing between fine‑tuned versions of the same Stable Diffusion XL architecture. It presents a 140K‑image benchmark dataset with in‑domain and out‑of‑distribution prompts, and proposes a baseline method using a style‑conditioned Mixture‑of‑Experts network that leverages a frozen CLIP backbone and a LoRA‑adapted DINOv2 network. The authors evaluate their approach on attribution accuracy and conduct ablation studies, providing code and data publicly on GitHub.
By Ashutosh Dayal, Mallika Garg, Debashis Sen
LESS (Lightweight Evolutionary Supernet Search) is a data‑driven NAS method that uses a brief hard‑path warm‑up and CMA‑ES to evaluate candidate architectures as decoded hard genotypes after six supernet updates. On NAS‑Bench‑201, LESS attains 93.189 % CIFAR‑10 accuracy in just 409.1 seconds, nearly matching FairNAS while using only about 1/24 of its search time. The approach also transfers well to CIFAR‑100, ImageNet16‑120, and the larger DARTS space, achieving high accuracies with searches completed in roughly 43.5 minutes on a single GPU.
By Aviral Gandhi, Jinglue Xu, Jialong Li, Hitoshi Iba
The paper introduces a schema‑adaptive action‑conditioned Joint‑Embedding Predictive Architecture (SAAC‑JEPA) for cross‑machine CNC transfer when only a subset of sensors overlap between source and target machines. Experiments show that pretraining does not improve source‑only forecasting, but a carefully selected action‑conditioned JEPA model achieves a zero‑shot RMSE of 0.546 on the target, outperforming persistence but falling short of certain baseline models. Ablation studies reveal that adding RevIN improves RMSE but harms calibration, and limited post‑lock adaptation can further reduce error.
By Ayoub Louaye Bouaziz, Matthieu Ostertag, Anton Demasles