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 Computation and Language

Sep 22

Closing the Speech-Text Gap with Limited Audio for Effective Domain Adaptation in LLM-Based ASR

arXiv:2604.06487v2 Announce Type: replace Abstract: Conventional end-to-end automatic speech recognition (ASR) systems rely on paired speech-text data for domain adaptation. Recent LLM-based ASR arch...

By Thibault Ba\~neras-Roux, Sergio Burdisso, Esa\'u Villatoro-Tello, Dairazalia S\'anchez-Cort\'es, Shiran Liu, Severin Baroudi, Shashi Kumar, Hasindri Watawana, Manjunath K E, Kadri Hacioglu, Petr Motlicek, Andreas Stolcke

llms fine-tuning multimodal safety

arXiv Computation and Language

Sep 22

English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training

arXiv:2604.13286v2 Announce Type: replace Abstract: Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing...

By Mehak Dhaliwal, Shashwat Chaurasia, Yao Qin, Dezhi Hong, Thomas Butler

llms fine-tuning

arXiv Computation and Language

Sep 22

Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation

arXiv:2606.18389v2 Announce Type: replace Abstract: Large language models (LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated dat...

By Jan Cegin, Daniil Gurgurov, Yusser Al Ghussin, Simon Ostermann

llms fine-tuning

arXiv Computation and Language

Sep 22

A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents

arXiv:2601.12538v2 Announce Type: replace-cross Abstract: Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) d...

By Tianxin Wei, Ting-Wei Li, Zhining Liu, Xuying Ning, Ze Yang, Jiaru Zou, Zhichen Zeng, Ruizhong Qiu, Xiao Lin, Dongqi Fu, Zihao Li, Mengting Ai, Duo Zhou, Wenxuan Bao, Yunzhe Li, Gaotang Li, Cheng Qian, Yu Wang, Xiangru Tang, Yin Xiao, Liri Fang, Hui Liu, Xianfeng Tang, Yuji Zhang, Chi Wang, Jiaxuan You, Heng Ji, Hanghang Tong, Jingrui He

llms agents reinforcement-learning robotics fine-tuning benchmarks

arXiv Computer Vision

Sep 22

Performance vs Consistency: Evaluating a Foundation Model in Lung-RADS Screening

arXiv:2609.22281v1 Announce Type: new Abstract: Foundation models have recently demonstrated strong capabilities across a wide range of medical imaging tasks. However, their performance in structured...

By Benjamin Renoust, Pierre Baudot, Tiffany Foriel, Yousra Haddou, Charles Voyton, Pierre-Henri Siot, Ezequiel Geremia, Danny Francis, Jean-Christophe Brisset, Val\'erie Bourd\`es, Sylvain Bodard, Benoit Huet

llms fine-tuning

arXiv Computer Vision

Sep 22

X-Beat: An Explainable Framework for ECG Image Classification

arXiv:2609.22631v1 Announce Type: new Abstract: Accurate automated interpretation of electrocardio- grams (ECGs) is essential for early detection of cardiac condi- tions such as myocardial infarction...

By Mohammad Sadman Tahsin, Haitham Y. Adarbah, Afzel Noore

computer-vision fine-tuning benchmarks

arXiv Computer Vision

Sep 22

Combining Foundation Model Confidence and Monocular Depth for Training-Free Out-of-Distribution Segmentation

arXiv:2609.22896v1 Announce Type: new Abstract: Autonomous vehicles operating in open-world scenarios are inevitably confronted with previously unknown objects, such as exotic animals or loose cargo....

By Serin Varghese, Fabian H\"uger, Kira Maag

agents computer-vision fine-tuning benchmarks

arXiv Computer Vision

Sep 22

CrowdCue: Specialist-Cue Conditioning for Vision-Language Crowd Counting

arXiv:2609.23012v1 Announce Type: new Abstract: Generative vision-language models (VLMs) offer a counting paradigm in which one model produces both a count and a natural-language account of the scene...

By Moshiur Farazi, Bekir Ciftler, Abdulhalim Dandoush, Reda Bendraou

llms fine-tuning multimodal benchmarks safety

arXiv Computer Vision

Sep 22

SPACE: Semantic Projection and Alignment of CLIP Embeddings for Domain Adaptation

arXiv:2609.23248v1 Announce Type: new Abstract: A fundamental challenge in deploying vision models is domain shift, which arises when training and test data follow different distributions, leading to...

By Jo\~ao Renato Ribeiro Manesco, Danilo Samuel Jodas, Douglas Rodrigues, Leandro Aparecido Passos, Jo\~ao Paulo Papa

rag fine-tuning multimodal safety

arXiv Computer Vision

Sep 22

Accurate Motion Estimation with B\'ezier Control Point for Efficient Frame Interpolation

arXiv:2609.23408v1 Announce Type: new Abstract: In frame interpolation tasks, motion ambiguity in the training set causes models to generate blurry intermediate frames. Moreover, the assumption of un...

By Shuhao Han, Chenyang Wu, Chun-Le Guo, Zheng-Peng Duan, Zhen Li, Ming-Ming Cheng, Chongyi Li

computer-vision fine-tuning benchmarks

arXiv Computer Vision

Sep 22

An Efficient and Effective Watermarking Scheme for the Protection of the Intellectual Property Rights of Video Generative Models

arXiv:2609.23586v1 Announce Type: new Abstract: The rapid development of video generative models (VGMs) has enabled the generation of highly realistic synthetic videos, raising concerns about the int...

By Wenhong Huang, Jianwei Fei, Benedetta Tondi, Bin Ma, Fangjun Huang

diffusion fine-tuning

arXiv Computer Vision

Sep 22

MaskVLA: Visual Masking Against Trajectory Overfitting of Vision-Language-Action Model

arXiv:2609.23565v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models integrate vision-language understanding with executable robot actions, enabling end-to-end learning for robot contr...

By Yuxuan Jiang, Jiaying Huang, Ge Wang, Shenhao Yan, Jiahao Yang, Chengsi Yao, Qi Liu, Qing Zhao, Shuguang Cui, Yiming Zhao, Yatong Han, Zhen Li

robotics fine-tuning multimodal

arXiv Computer Vision

Sep 22

Reassessing Global Gradient-Norm Imbalance in BLIP Fine-Tuning Across Physical Domains

arXiv:2609.23655v1 Announce Type: new Abstract: Imbalanced gradient magnitudes between the visual and language pathways of a vision-language model are often treated as a defect to be corrected. We te...

By Kiran Naseer, Samreen Azhar, Dwarikanath Mahapatra

llms fine-tuning multimodal

arXiv Computer Vision

Sep 22

An Unexpected Robot Policy: Early Evaluations of GPT-6 Astra on RoboDojo and Beyond

arXiv:2609.24170v1 Announce Type: new Abstract: Embodied AI systems are often organized into System 1 and System 2. System 1 is typically a pretrained policy that generates actions at high frequency,...

By Wenbo Zhang, Kaixuan Wang, Yutao Ouyang, Xiaoyu Huang, Liyang Li, Kailun Su, Weiyang Jin, Wenhao Chai, Haotian Liang, Zhiyang Dou, Yue Chen, Tianxing Chen

llms robotics fine-tuning

arXiv Computer Vision

Sep 22

Evaluating Transformation Models for pCLE Mosaic Registration

arXiv:2609.24560v1 Announce Type: new Abstract: Confocal Laser Endomicroscopy (CLE) provides real-time, cellular-resolution optical biopsy but has a narrow field of view, which image mosaicing can ex...

By Ahmed Aboelela, Johannes Barcsay, Jana Friedhof, Miguel Gon\c{c}alves, Alexander Hann, Katharina Breininger

fine-tuning safety

arXiv Computer Vision

Sep 22

HyperCLIP++: Fine-tuning CLIP forOpen-vocabulary Semantic Segmentation in Hyperbolic Space

arXiv:2609.24564v1 Announce Type: new Abstract: CLIP, a foundational vision-language model, has emerged as a powerful tool for open-vocabulary semantic segmentation. While freezing CLIP's text encode...

By Zelin Peng, Zhengqin Xu, Changsong Wen, Yu Huang, Yaoming Wang, Xiaokang Yang, Wei Shen

llms rag computer-vision fine-tuning multimodal benchmarks safety

arXiv Computer Vision

Sep 22

Multicentre Bi-atrial Segmentation from LGE-MRI for Atrial Fibrillation with a 2D and 3D Framework

arXiv:2609.22398v1 Announce Type: cross Abstract: Accurate delineation of bi-atrial structures from late gadolinium enhancement MRI (LGE-MRI) is an important prerequisite for structural analysis and...

By Malitha Gunawardhana, Gregory B. Sands, Mark L. Trew, Jichao Zhao

llms computer-vision fine-tuning benchmarks

arXiv Computer Vision

Sep 22

What do VLM-Based Vision-Language Navigation Models Rely on: Interpreting and Steering Policy Behavior

arXiv:2609.24576v1 Announce Type: cross Abstract: Modern Vision-Language Navigation (VLN) models rely mostly on pre-trained large Vision-Language Models (VLMs) to predict navigation actions. While th...

By D\'ebora Oliveira Makowski, Samiran Gode, Abhijeet Nayak, Marco Hutter, Cordelia Schmid, Lukas Rosenberger Schmid, Wolfram Burgard

llms agents fine-tuning multimodal safety

arXiv Computer Vision

Sep 22

RefAM: Attention Magnets for Zero-Shot Referral Segmentation

arXiv:2509.22650v3 Announce Type: replace Abstract: Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models,...

By Anna Kukleva, Enis Simsar, Alessio Tonioni, Muhammad Ferjad Naeem, Federico Tombari, Jan Eric Lenssen, Bernt Schiele

llms diffusion computer-vision fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 22

From Priors to Perception: Grounding Video-LLMs in Physical Reality

arXiv:2605.04515v2 Announce Type: replace Abstract: Video Large Language Models (Video-LLMs) excel in general video understanding but often base physical judgments on event expectations rather than o...

By Zicheng Zhao, Chaofan Gan, Shijie Li, Weiyao Lin

llms fine-tuning benchmarks safety

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