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Fine-tuning and adaptation

LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.

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arXiv Machine Learning
Sep 22

ZoAQ: Adaptive Zeroth-Order Querying via Query-Reuse Coupling

ZoAQ is an adaptive zeroth-order optimization method that reuses past function evaluations to inform both the next update and the decision to query further, eliminating the need for extra validation queries. The approach achieves significant query savings—43–48% fewer queries than fixed-budget baselines on synthetic tasks—and demonstrates strong performance in black-box adversarial attacks, achieving 100% success with 320 and 625 average queries on MNIST and CIFAR-10. In fine-tuning experiments across four settings, ZoAQ reduces forward evaluations by 43–46% while maintaining accuracy within ±0.018 to +0.010.

By Yangyang Feng, Yao Shu
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arXiv Machine Learning
Sep 22

The Effect of Quantization on Clinical Benchmarks: Accuracy and Safety Across Model Families

The study evaluates how quantization affects accuracy and safety of five 7‑8B language models on clinical benchmarks. INT8 GPTQ shows minimal degradation (≤1.9%) across tasks, while INT4 causes substantial, model‑dependent drops, especially in high‑risk scenarios and safety metrics. Recovery methods such as clinical calibration substitution and QLoRA fine‑tuning yield mixed results, underscoring the need for task‑specific validation.

By Leonard Twagirayezu, Prasenjit Mitra
llmsfine-tuningefficiencybenchmarkssafety
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arXiv Machine Learning
Sep 22

Strategy Accumulation and Guided Execution for Automated LLM Fine-Tuning

The paper introduces Strategy Accumulation and Guided Execution (SAGE), a two-stage framework that makes automated fine-tuning of large language models cumulative. In the first stage, a multi-agent pipeline uses Monte Carlo Tree Search to explore training strategies while a Distillation Agent records task-specific insights and cross-task confidence scores into a structured repository. In the second stage, SAGE retrieves relevant experience from this repository to guide training on new tasks, achieving a 12.4‑percentage‑point improvement over a baseline pipeline without accumulated experience on nine unseen tasks.

By Haoran Zhao, Wei Du, Dingwen Yang, Jixuan Huang, Junlin Shang, Lingyong Fang, Ya Guo, Tao Gui, Qi Zhang, Xuanjing Huang
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arXiv Machine Learning
Sep 22

Detecting Agitation Before Behavioral Escalation in Autistic Youth Through Multimodal Wearable Sensing

The study reports that agitation—an early, subtle sign of distress—can be detected in autistic youth using multimodal wearable sensors. By fusing upper‑body movement, wrist‑worn physiological data, and vocalizations through pretrained foundation models, the authors achieved an AUC of 0.724 at the clinician‑annotated onset of agitation, with audio contributing the most signal. The approach works across 15 participants, showing that individualized agitation is detectable even before the onset and without needing a separate model per child.

By Nibraas Khan, Abigale Plunk, John Staubitz, Ingrid Shragge, Jordan Brooks, Suzanne Wright, Alec Brewer, James Dieffenderfer, Alper Bozkurt, Amy Weitlauf, Nilanjan Sarkar
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arXiv Machine Learning
Sep 22

Muon Can Outperform Dedicated Continual Learning Methods

arXiv:2609.24678v1 Announce Type: new Abstract: Continual learning with Low-Rank Adapters (LoRA) typically mitigates forgetting by penalizing the overlap between a new update and the accumulated past...

By Sebastian George Sincari (Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania), Bogdan Alexandru Gheorghe (Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania), Antonio Barbalau (Bitdefender, Bucharest, Romania)
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arXiv Machine Learning
Sep 22

MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis

arXiv:2609.24736v1 Announce Type: cross Abstract: Mitotic figure (MF) analysis supports tumor grading and prognostic assessment, but automated models remain sensitive to differences in tissue type an...

By Trinh T. L. Vuong, Simon Graham, Quoc Dang Vu, Phat T. H. Ho, Jeewoo Lim, Mostafa Jahanifar, Nasir Rajpoot, Jin T. Kwak
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