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

Oct 27

Aerial-R1: Reinforcing Aerial Reasoning and Segmentation in MLLMs

arXiv:2610.33056v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) have revolutionized general visual perception, existing methods suffer from severe hallucination in th...

By Jisheng Dang, Huaiyuan Peng, Yixuan Wang, Kun Wang, Zhang Li, Tat-Seng Chua

llms agents computer-vision fine-tuning multimodal benchmarks safety

arXiv AI

Oct 27

ProMo3D: Probing Motion Cues in Frozen 3D Foundation Models

arXiv:2610.33802v1 Announce Type: cross Abstract: Feed-forward 3D foundation models have recently reshaped multi-view reconstruction, yet dynamic 4D understanding still often relies on large-scale mo...

By Xiaoang Zhang, Mert Kiray, Yordanka Velikova, Manoj Biswanath, Benjamin Busam

fine-tuning efficiency benchmarks

arXiv AI

Oct 27

Xing USP: A Cross-Family Semantic Contract for Information Extraction and Intent-Slot Parsing

arXiv:2610.34343v1 Announce Type: cross Abstract: Structured semantic parsing is typically judged by whether the correct category is assigned, which leaves value recovery and instance-level associati...

By Zhongqiu Li, Boyang Wang, Ruiyu Fang, Shiquan Wang, Di Wu, Jiayi Liang, Feilong Zhang, Hongfeng Du, Xiaomeng Huang, Mengjiao Bao, Luwen Pu, Xuewei He, Yu Zhao, Chao Wang, Kaidong Yu, Bingkai Yang, Qingjun Zhu, Shaoyi Du, Shuangyong Song, Yongxiang Li

fine-tuning

arXiv AI

Oct 27

In-Context Adapter Generation for Compositional Generalization in Large Audio Language Models

arXiv:2610.34413v1 Announce Type: cross Abstract: Large audio language models (LALMs) have achieved strong performance on standard speech and audio understanding tasks, yet their ability to systemati...

By Yiwei Guo, Siqi Huang, Bohan Li, Jing Peng, Hankun Wang, Zhihan Li, Kai Yu

llms rag fine-tuning

arXiv AI

Oct 27

GRASP: Group-Adaptive Hybrid RL-SFT Post-Training for Medical VQA without Reasoning Annotations

arXiv:2610.34561v1 Announce Type: cross Abstract: Adapting general-purpose vision-language models (VLMs) to medical visual question answering (VQA) is valuable for helping clinicians read images and...

By Wanqi Dong, Wanyue Dong, Jingze Ge, Mehul Motani

llms nlp reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Oct 27

CA-HNM: Constraint-Aware Hard Negative Mining for Dense Retrieval with Structured Domain Models

arXiv:2610.35045v1 Announce Type: cross Abstract: Dense retrievers are commonly trained with hard negatives selected from lexical or dense retrieval results. Retrieval proximity, however, does not in...

By Ngoc Luyen Le, Marie-H\'el\`ene Abel, Bertrand Laforge

fine-tuning

arXiv AI

Oct 27

TrySee: Modular Perception-Enhanced VLM Grounding for Closed-Loop Robot Task Planning in Specialized Domains

arXiv:2610.35687v1 Announce Type: cross Abstract: Vision Language Models (VLMs) have shown impressive capabilities in high-level reasoning and task planning for robots. However, specialized domains r...

By Elias Hofele, Thomas Trapp, David Timmermann, Tristan Schnell, Georg Heppner, Arne Roennau

llms robotics fine-tuning multimodal

arXiv AI

Oct 27

Reva: Real-Time Vulnerability Detection through Contextual Probing of Large Language Models

arXiv:2610.35829v1 Announce Type: cross Abstract: A vulnerability costs more to fix the later it is discovered. As coding agents generate code faster than developers can review it, software needs a f...

By Amine Lbath, Massih-Reza Amini

llms agents fine-tuning benchmarks

arXiv AI

Oct 27

Hierarchical Alignment: Block-Level LoRA-DPO for Preference Tuning in Large Language Models

arXiv:2510.12044v2 Announce Type: replace-cross Abstract: Preference tuning typically distributes adapter updates across every Transformer layer. We study how the placement of a fixed LoRA budget aff...

By Yishen Chen, Qi Dong, Yukun Zhang

llms reinforcement-learning fine-tuning safety

arXiv AI

Oct 27

OmicsLM: A Multimodal Large Language Model for Multi-Sample Omics Reasoning

arXiv:2605.06728v2 Announce Type: replace-cross Abstract: Large language models can reason over biological knowledge, but they are not naturally equipped to work with quantitative omics measurements....

By Maciej Sypetkowski, {\L}ukasz Smoli{\'n}ski, Remigiusz Kinas, Joanna Krawczyk, Bart{\l}omiej Hofman, Aleksandra Mo{\.z}wi{\l}{\l}o, Micha{\l} Pikusa, Tomasz Jetka, Rafa{\l} Powalski

llms rag fine-tuning multimodal benchmarks

arXiv AI

Oct 27

MicroWorld: Empowering Multimodal Large Language Models to Bridge the Microscopic Domain Gap with Multimodal Attributed Property Graph

arXiv:2605.10120v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) show remarkable potential for scientific reasoning, yet their performance in specialized domains suc...

By Manyu Li, Ruian He, Chenxi Ma, Weimin Tan, Bo Yan

llms rag fine-tuning multimodal

arXiv AI

Oct 27

SAMPA: Transformer-based segmentation of prosodic boundaries in Brazilian Portuguese

arXiv:2607.07408v3 Announce Type: replace-cross Abstract: Automatic prosodic segmentation identifies boundaries between speech units based on acoustic and linguistic evidence. Although recent deep le...

By Rodrigo de Freitas Lima, Julio Cesar Galdino, Marcos Vinicius Treviso

llms computer-vision fine-tuning multimodal

arXiv AI

Oct 27

Content-Aware Adaptive Token Pruning for Video-Language Models

arXiv:2608.03112v2 Announce Type: replace-cross Abstract: Vision-language models excel at image and video understanding but suffer from high inference latency due to the need to process thousands of...

By Paribesh Regmi, Qingshuang Chen, Chi Zhang, Heba Aly, Yelin Kim, Hongda Mao

llms rag fine-tuning efficiency multimodal benchmarks

arXiv AI

Oct 27

What Does a Harness Repair? A Preregistered Study of Visibility, Baseline Adequacy and Evaluation Defects

arXiv:2610.05533v2 Announce Type: replace-cross Abstract: Harness search keeps a change to the prompts, token budgets or parsers around a frozen model if the change raises a score. Such a gain can co...

By Bowen Xu, Boyu Chen

fine-tuning benchmarks

arXiv Machine Learning

Oct 27

Sharpness-aware Orthogonalized Momentum Algorithms

arXiv:2610.32465v1 Announce Type: new Abstract: Adversarial sharpness penalty per iteration of a base optimizer, known as sharpness-aware minimization (SAM), is an accepted technique to improve gener...

By Geetika, Shivani Singh Chauhan, Gauranshi Gupta, Anant Jain, Kaushik Kalyanaraman, Bapi Chatterjee

llms fine-tuning efficiency safety

arXiv Machine Learning

Oct 27

DT-JEPA: Learning to Predict Future Representations for Decision-Making

arXiv:2610.32657v1 Announce Type: new Abstract: Decision Transformer (DT) has demonstrated strong performance in various offline reinforcement learning (RL) tasks by conditioning action generation on...

By Changxu Ni, Yiyang Ni, Jun Li, Feiran You, Hongyang Du, Haitao Zhao, Shi Jin

llms rag diffusion reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Oct 27

Democratizing LLM Training with the Edge: Challenges, Systems, and the Road Ahead

arXiv:2610.32215v2 Announce Type: cross Abstract: The growing computational power of modern GPU data centers has let a few industrial giants train and deploy large language models (LLMs) on massive a...

By Ziyue Luo, Jiaxuan Cai, Cedric LeDenmat, Srijith Nair, Fatemeh Nourzad, Rohith Krishnan, Qinhang Wu, Jifan Zhang, Sungjae Lee, Zhe Li, Peiwen Qiu, Siddharth Shah, Rishabh Sharma, Sundararajan Srinivasan, Yinglun Xia, Xue Zheng, Zidong Liu, Bicheng Ying, Kaushik Chowdhury, Gauri Joshi, Yingbin Liang, Robert Nowak, Srinivasan Parthasarathy, Saurav Prakash, Balaraman Ravindran, Sanjay Shakkottai, Ness B. Shroff, Haibo Yang, Aylin Yener, Jia Liu

llms fine-tuning safety

arXiv Computation and Language

Oct 27

Preference Optimization Improves Definition Generation: Semantic Change in EU Institutional Sustainable Finance Disclosures

arXiv:2610.33357v1 Announce Type: new Abstract: Dictionary-like definition generators are known to be useful for lexical semantic change detection (LSCD). However, they suffer from overly granular se...

By Mariia Fedorova, Andrey Kutuzov, Gustavo Cadenas Delascio

reinforcement-learning fine-tuning benchmarks

arXiv Computation and Language

Oct 27

How Does Post-Training Shape LLM Value Preferences?

arXiv:2610.33371v1 Announce Type: new Abstract: During post-training, large language models (LLMs) learn value preferences from corpora that may contain imbalanced proportions of opinions associated...

By Yang Liu, Shaoyang Xu, Masahiro Kaneko, Chengzhi Zhong, Wenxuan Zhang, Chenhui Chu

llms nlp reinforcement-learning fine-tuning efficiency

arXiv Computation and Language

Oct 27

Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool

arXiv:2610.33464v1 Announce Type: new Abstract: Propagandistic content increasingly circulates through online news and social media, where readers often encounter it with limited scrutiny, highlighti...

By Thomas Kimmeth, Avijit Roy, Vivek Sharma

llms fine-tuning

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