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 AI

Jul 7

Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL

arXiv:2607. 03833v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have achieved remarkable success in Text-to-SQL tasks, their deployment in real-world environments is hindered by latent reliability issues.

By Hanqing Wang, Yongdong Chi, Jian Yang, Lei Yang, Jiehui Zhao, Yun Chen, Guanhua Chen

llms fine-tuning benchmarks data-engineering

arXiv Machine Learning

Jul 7

DIRA-SS:Dynamic Domain Incremental Regularised Adaptation -- Self-Supervised

arXiv:2311. 07461v3 Announce Type: replace Abstract: Autonomous systems (AS) often rely on Deep Neural Network (DNN) classifiers to operate in complex and dynamically changing environments.

By Abanoub Ghobrial, Kerstin Eder

agents fine-tuning

arXiv AI

Jul 7

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting

arXiv:2607. 02637v1 Announce Type: cross Abstract: Recent generative models can produce high-quality synthetic images, offering scalable training training data for data-hungry models.

By Disheng Liu, Tuo Liang, Chaoda Song, Yu Yin

llms computer-vision fine-tuning benchmarks safety

arXiv AI

Jul 7

CGGS: Consistency-Augmented Geometric Gaussian Splatting for Ego-centric 3D Scene Generation

arXiv:2607. 03819v1 Announce Type: cross Abstract: Challenges remain in ego-centric 3D scene generation due to limited view overlap and the dominant influence of individual perspectives on scene interpretation.

By Zhenyu Sun, Xiaohan Zhang, Qi Liu, Huan Wang

diffusion computer-vision fine-tuning

arXiv AI

Jul 7

Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales

arXiv:2607. 04972v1 Announce Type: cross Abstract: Deploying robot teams in the real world requires simultaneous adaptation to unseen environments, unknown partners, and varying team sizes, yet existing approaches often address these challenges in isolation under the closed-world assumption of fixed teammates.

By Yang Li, Feng Xue, Fan Mo, Yunhao Liu, Jianhong Wang, Ying Wen, Qingrui Zhang, Shaoshuai Mou, Wei Pan

agents robotics fine-tuning

arXiv Machine Learning

Jul 7

Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions

arXiv:2604. 11730v4 Announce Type: replace-cross Abstract: Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habits that improve medical outcomes.

By Manuela Gonz\'alez-Gonz\'alez, Soufiane Belharbi, Muhammad Osama Zeeshan, Masoumeh Sharafi, Muhammad Haseeb Aslam, Lorenzo Sia, Nicolas Richet, Marco Pedersoli, Alessandro Lameiras Koerich, Simon L Bacon, Eric Granger

llms fine-tuning multimodal

arXiv Machine Learning

Jul 7

ProbeLogits: Kernel-Level LLM Inference Primitives for AI-Native Operating Systems

arXiv:2604. 11943v3 Announce Type: replace-cross Abstract: An OS kernel that runs LLM inference internally can read the model's own next-token logit distribution before any text is generated, and act on it as a governance primitive.

By Daeyeon Son

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Jul 7

The Bayesian Approach to Continual Learning: An Overview

arXiv:2507. 08922v3 Announce Type: replace-cross Abstract: Continual learning is an online paradigm where a learner continually accumulates knowledge from different tasks encountered over sequential time steps.

By Tameem Adel

fine-tuning benchmarks

arXiv Machine Learning

Jul 7

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms

arXiv:2508. 02641v2 Announce Type: replace-cross Abstract: Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics.

By Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque, Daniel S. Levine, Sushree Jagriti Sahoo, Brandon M. Wood, Kyle Michel, Muhammed Shuaibi, Gregory J. O. Beran, Viachaslau Bernat, Misko Dzamba, Xiang Fu, Meng Gao, Xingyu Liu, Benjamin K. Miller, Keian Noori, Lafe J. Purvis, Tingling Rao, Ammar Rizvi, Matt Uyttendaele, Andrew J. Ouderkirk, Chiara Daraio, C. Lawrence Zitnick, Arman Boromand, Noa Marom, Zachary W. Ulissi, Anuroop Sriram

fine-tuning efficiency

arXiv AI

Jul 7

GenShin: Guiding Rational Liposome Design by Ranking Liposomal Protein Corona through a Docking-Pose-Free GNN

arXiv:2504. 13853v2 Announce Type: replace-cross Abstract: Rational design of lipid nanoparticles (LNPs) for tissue-specific delivery critically depends on predicting the composition of the protein corona that forms on the lipid surface after intravenous administration.

By Pingfei Zhu, Hongyi Liu, Xueyan Liu, Zhenjun Yang, Bo Yang

fine-tuning benchmarks

arXiv Machine Learning

Jul 7

Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs

arXiv:2605. 07961v2 Announce Type: replace Abstract: Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs).

By Hanlin Cai, Kai Li, Houtianfu Wang, Haofan Dong, Yichen Li, Falko Dressler, Ozgur B. Akan

llms agents robotics fine-tuning safety

arXiv AI

Jul 7

InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem

arXiv:2512. 05672v2 Announce Type: replace-cross Abstract: Recent approaches in controllable novel view video generation often rely on fine-tuning pre-trained Video Diffusion Models (VDMs).

By Yeobin Hong, Suhyeon Lee, Hyungjin Chung, Jong Chul Ye

diffusion fine-tuning

arXiv Machine Learning

Jul 7

ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution

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

llms agents fine-tuning benchmarks

arXiv Machine Learning

Jul 7

XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control

arXiv:2607. 04171v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong multimodal understanding and spatial grounding, but their computational cost limits real-time robotic control.

By Lei Iok Tong, Qingchen Xie, Wei Huang, Ying Jie Yap, Yujie Zhang, Qianzhi Li, Xiaolong Liu, Zhidong Deng

llms diffusion robotics fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Jul 7

Provable Pruning for Efficient 3D Gaussian Splatting via Coresets

arXiv:2607. 02721v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) enables high-quality real-time novel-view synthesis, but practical scenes often contain millions of Gaussians, making compression essential for deployment on limited hardware.

By Waseem Mousa, Alaa Maalouf

fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 7

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach

arXiv:2607. 05271v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) encounter ill-posed optimization, loss competition, and parameter compensation in partial differential equation (PDE) inverse problems.

By Qian Hu, Bin Fan, Yao Xiao, Zhicheng Lin, Meixin Xiong

diffusion fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 7

Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning

arXiv:2607. 04577v1 Announce Type: new Abstract: Code models strictly prioritize functional correctness, leaving software energy efficiency as an unoptimized byproduct.

By Saurabhsingh Rajput, Tushar Sharma

reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Jul 7

Do ECG Foundation Models Transfer to Rare Cardiac Diseases? Evidence from Brugada Syndrome Detection

arXiv:2607. 03009v1 Announce Type: new Abstract: Background: Foundation models (FMs) trained on large-scale unlabeled physiological data have emerged as a promising paradigm for medical artificial intelligence.

By Beatrice Zanchi, Giuliana Monachino, Alvise Dei Rossi, Luigi Fiorillo, Georgia Sarquella-Brugada, Giulio Conte, Francesca Dalia Faraci

fine-tuning benchmarks safety

arXiv Machine Learning

Jul 7

Reward Granularity in RLVR: Comparing Process and Outcome Reward Structures for Mathematical Reasoning in Small Language Models

arXiv:2607. 02869v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving mathematical reasoning in language models.

By Anagha Radhakrishna Palandye, Rebecca Glick, Osheen Kaul

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 7

Weighted Conformal Prediction for Lab-to-Track Thermal Transfer in EV Motorsport Powertrains

arXiv:2607. 02722v1 Announce Type: new Abstract: Predicting thermal volatility in high-performance EV powertrains is difficult as internal temperatures are rarely observable outside the lab, and models calibrated on lab drive cycles fail when deployed against real-world loads.

By Varshith Roy Kotla

fine-tuning

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