SAGE-Loop is a new closed‑loop, self‑adaptive AutoML framework that uses large language models to generate and validate machine learning pipelines in multiple rounds, allowing trial‑and‑repair and adaptive ensemble selection for both supervised and unsupervised tasks. It addresses the lack of instant feedback and correction in existing AutoML by enabling process‑level recovery from failures and dynamic use of model diversity. Experiments on 20 public datasets show consistent improvements in performance and stability across classification, regression, and clustering, and demonstrate the system’s ability to recover from execution failures.
By Junquan Gu, Shibo Cui, Xiangfeng Luo, Hang Yu
arXiv:2606. 14900v1 Announce Type: new Abstract: Multi-source transfer learning faces a fundamental scalability bottleneck: existing approaches require either loading all K source models into memory simultaneously during parameter fusion, requiring O(K) memory, or deploying all models at inference time, making production deployment infeasible.
By Mary Isabelle Wisell, Nicholas Jacobs, Aayush Manandhar, Salimeh Yasaei Sekeh
The paper introduces TESS, a scalable data‑selection framework that replaces per‑sample weights with a selection network to improve transferability across datasets and model sizes. It identifies instability in existing meta‑learning for training‑data selection (MTS) due to weight suppression and overreliance on easy features, and proposes a Pointwise Value Matching objective to address these issues. Experiments on large language model safety and instruction tuning show strong transfer from subsets to full corpora and from smaller to larger models.
By Zilin Du, Bowen Yang, Boyang Albert Li
AutoOR is a scalable synthetic data generation and reinforcement learning pipeline that trains large language models to autoformalize operations research problems expressed in natural language across linear, mixed‑integer, and non‑linear categories. By generating verified training data from standard optimization forms and using solver execution feedback as a reward signal, AutoOR enables post‑training of an 8B model to achieve state‑of‑the‑art or competitive results on six established OR benchmarks, matching significantly larger frontier models. For non‑linear problems involving physical dynamics, a curriculum RL strategy bootstraps from limited initial data, making this class tractable for post‑training.
By Sumeet Ramesh Motwani, Chuan Du, Aleksander Petrov, Christopher Davis, Philip Torr, Antonio Papania-Davis, Weishi Yan
arXiv:2607. 27953v1 Announce Type: new Abstract: Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain.
By Shengda Gu, Kai Li, Xinyi Ke, Haobo Fu, Yifan Zhang, Jian Cheng
arXiv:2604.16804v4 Announce Type: replace-cross
Abstract: Optimization problems are central to decision-making in manufacturing, logistics, scheduling, and other industrial settings. Translating comp...
By Sumeet Ramesh Motwani, Chuan Du, Aleksander Petrov, Christopher Davis, Philip Torr, Antonio Papania-Davis, Weishi Yan
arXiv:2606. 17660v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) is compute-intensive and error-prone: model performance depends sensitively on data quality and hyperparameter choices, and na\"ive runs can even degrade model performance.
By Yuxiang Luo, Haonan Long, Chen Wang, Qiqi Duan, Xiaotian Lin, Yanwei Xu, Yuyu Luo, Weikai Yang, Nan Tang
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
The paper introduces Poodle, a prototype for just‑in‑time model replacement (JITR) that automatically swaps a large language model with a cheaper, task‑specific model when a recurring task is detected. Poodle reduces inference time by up to 7.5× and saves over $2,200 per 1 M requests compared to a flagship hosted LLM, while maintaining competitive accuracy. The authors argue that model search and transfer learning are essential for efficiently identifying and fine‑tuning these custom models.
By Nils Strassenburg, Boris Glavic, Tilmann Rabl
arXiv:2410. 13077v2 Announce Type: replace-cross Abstract: Transformer-based Large Language Models (LLMs) traditionally rely on final-layer loss for finetuning and final-layer representations for predictions, potentially overlooking the predictive power embedded in late layers.
By Haoyan Luo, Lucia Specia
arXiv:2607. 00958v1 Announce Type: new Abstract: Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self-supervised learning often depends on view and augmentation choices that encode domain-specific invariances.
By Alexander Chemeris, Ming Jin, Randall Balestriero
arXiv:2511. 08577v3 Announce Type: replace-cross Abstract: Improving the reasoning abilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications.
By Tianyu Fu, Yichen You, Zekai Chen, Guohao Dai, Huazhong Yang, Yu Wang