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 28

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications

arXiv:2607. 23124v1 Announce Type: new Abstract: Large language model agents have advanced rapidly, yet progress remains fragmented across domains, capabilities, task difficulty, and interaction settings.

By Hao Jiang, Gangtao Xin, Yingdi Huang, Guojie Zhu, Jiangshan Zhang, Xinyuan Lin, Yunkun Xu, Chengyu Shen, Wenlong Fei, Jiawei Li, Yujie Fu, Sichen Kang, Tingyu Xie, Yedi Hu, Jingren Zhang, Hongcheng Gao, Jianshu Zeng, Chong Chen, Chang Guo, Chao Feng, Feng Wang, Fulin Lin, Jinchao Ma, Lang Mei, Li Huang, Liyan Liu, Qing He, Shuting Tao, Siyu Mo, Xiangnan Chen, Xiaohan Yu, Xiaoyang Li, Yanheng Hou, Yanyu Wu, Zhihan Yang, Wentao Zhang, Yang Gao, Zhao Cao

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 28

ObsDriveBench: Benchmarking Multimodal Understanding under Adverse Weather with Observability Awareness

arXiv:2607. 23537v1 Announce Type: new Abstract: Autonomous driving under adverse weather remains a critical challenge, yet existing vision-language benchmarks mainly evaluate under standard conditions, synthetic corruptions, or single modality.

By Qiao Yan, Yihan Wang, Zhenghao Xing, Jiaqi Xu, Pheng-Ann Heng

llms agents reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 28

BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi

arXiv:2607. 23344v1 Announce Type: cross Abstract: Named Entity Recognition (NER) for low-resource languages such as Marathi remains a challenging task due to limited annotated resources and linguistic complexity.

By Hariom Ingle, Ronit Ghode, Ishwari Gondkar, Jidnyasa Harad, Raviraj Joshi

llms nlp fine-tuning

arXiv AI

Jul 28

Do Small Models Use the Law You Give Them? Context-Injected Fine-Tuning for Legal QA in Bangladesh

arXiv:2607. 23446v1 Announce Type: cross Abstract: A small language model can receive the governing statutory provision and still answer incorrectly.

By Moniruzzaman Mahadi, Abrar Mohammed Tanzim Alam, Sayma Siddika Monalisa, Mir Mohammad Asif Abdullah, Swakkhar Shatabda, Md Adnan Arefeen

llms fine-tuning

arXiv Machine Learning

Jul 28

Transfer learning for conflict and duplicate detection in software requirement pairs

arXiv:2301. 03709v3 Announce Type: replace-cross Abstract: Consistent and holistic expression of software requirements is important for the success of software projects.

By Garima Malik, Savas Yildirim, Mucahit Cevik

llms nlp fine-tuning

arXiv Machine Learning

Jul 28

Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning

arXiv:2607. 23837v1 Announce Type: new Abstract: Large language models generalize well to individual tasks but lack an inherent mechanism for learning them sequentially, leading to catastrophic forgetting.

By Reza Rahimi Azghan, Gautham Krishna Gudur, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh

llms rag fine-tuning benchmarks

arXiv Machine Learning

Jul 28

Controllable Diversity in Normalization-Based Implicit Ensembles via Softmax-Temperature Modulation

arXiv:2607. 23860v1 Announce Type: new Abstract: Deep ensembles provide the most reliable uncertainty estimates in deep learning, but their cost grows linearly with the number of members.

By Mihai Suteu, Ovidiu Serban

llms fine-tuning

arXiv AI

Jul 28

Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment

arXiv:2508. 02929v3 Announce Type: replace-cross Abstract: Scaling laws have been established for recommender systems, yet efficiently deploying foundation model (FM) across multiple recommendation surfaces remains a major unsolved challenge.

By Dai Li, Kevin Course, Wei Li, Hongwei Li, Jie Hua, Yiqi Chen, Zhao Zhu, Rui Jian, Xuan Cao, Bi Xue, Yu Shi, Jing Qian, Kai Ren, Matt Ma, Qunshu Zhang, Rui Li

rag fine-tuning efficiency

arXiv AI

Jul 28

TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning

arXiv:2509. 06278v4 Announce Type: replace Abstract: Table reasoning requires models to jointly perform comprehensive semantic understanding and precise numerical operations.

By Chuang Jiang, Mingyue Cheng, Xiaoyu Tao, Qingyang Mao, Jie Ouyang, Qi Liu

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 28

Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models

arXiv:2607. 22663v1 Announce Type: new Abstract: Block diffusion has emerged as the dominant paradigm for scaling discrete diffusion language models (dLLMs), because decoding text in fixed-size blocks preserves parallel generation within each block while keeping the quadratic attention cost tractable.

By Xingyu Mou, Zijin Huang, Tianze Zhang, Yuxin Ma, Lanning Wei, Zengfeng Huang, Da Zheng, Lun Du

llms diffusion fine-tuning efficiency benchmarks

arXiv AI

Jul 28

A scalable online machine learning approach for Stock Recommendation

arXiv:2607. 23120v1 Announce Type: cross Abstract: Stock recommendation systems face the dual challenge of adapting to rapidly changing market conditions while maintaining low-latency predictions for end users.

By Harsh Nagarkar

fine-tuning

arXiv Machine Learning

Jul 28

Shift-Aware Calibration for Fine-Tuned CLIP: Leveraging Image-Text Alignment

arXiv:2501. 19060v4 Announce Type: replace-cross Abstract: Vision-language models (VLMs), such as CLIP, adapt effectively to downstream tasks through prompt tuning, but fine-tuning can misalign predictive confidence and accuracy, particularly on unseen classes.

By Song-Lin Lv, Yu-Yang Chen, Zhi Zhou, Lan-Zhe Guo

llms fine-tuning multimodal safety

arXiv AI

Jul 28

Reconstructing Item Characteristic Curves using Fine-Tuned Large Language Models

arXiv:2601. 02580v2 Announce Type: replace-cross Abstract: Traditional methods for determining assessment item parameters, such as difficulty and discrimination, rely heavily on expensive field testing to collect student performance data for Item Response Theory (IRT) calibration.

By Christopher Ormerod

llms fine-tuning

arXiv Machine Learning

Jul 28

Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization

arXiv:2607. 23404v1 Announce Type: new Abstract: Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predictive surrogate at its core.

By Jaewook Lee, Ethan Errington, Christian D. Lorenz, Miao Guo

robotics fine-tuning benchmarks

arXiv AI

Jul 28

Moral Hazard in Multi-Agent Language Models

arXiv:2607. 23982v1 Announce Type: cross Abstract: Cooperation can fail when socially valuable effort is costly, weakly observable, and mainly benefits others.

By Dane Malenfant

llms agents fine-tuning safety

arXiv AI

Jul 28

LuxInstruct: A Cross-Lingual Instruction Tuning Dataset For Luxembourgish

arXiv:2510. 07074v2 Announce Type: replace-cross Abstract: Instruction tuning has become a key technique for enhancing the performance of large language models, enabling them to better follow human prompts.

By Fred Philippy, Laura Bernardy, Siwen Guo, Jacques Klein, Tegawend\'e F. Bissyand\'e

llms nlp fine-tuning safety

arXiv AI

Jul 28

ConsistencyGate: Preventing Memory Contamination in LLM Agents via Self-Consistency Admission Control

arXiv:2607. 22962v1 Announce Type: new Abstract: LLM agents that operate over many turns accumulate facts in an external memory store and reuse them as premises for downstream reasoning.

By Yan Zhang, Shibo Li

llms agents fine-tuning benchmarks

arXiv Machine Learning

Jul 28

Music-Source-Separation-Training (MSST): A Unified Framework for Training and Evaluating Music Demixing Models

arXiv:2607. 23395v1 Announce Type: cross Abstract: Music Source Separation (MSS), the task of recovering individual sound components (stems) from a polyphonic mixture, is central to applications ranging from karaoke and remixing to audio restoration and content production.

By Roman Solovyev, Ilya Kiselev, Alexander Stempkovskiy, Tatiana Gabruseva

fine-tuning benchmarks

arXiv AI

Jul 28

Continual Knowledge Consolidation LORA for Domain Incremental Learning

arXiv:2510. 16077v2 Announce Type: replace-cross Abstract: Domain Incremental Learning (DIL) is a sub-branch of continual learning that aims to address the never-ending arrival of new domains without catastrophic forgetting.

By Naeem Paeedeh, Mahardhika Pratama, Weiping Ding, Jimmy Cao, Wolfgang Mayer, Ryszard Kowalczyk, Ary Shiddiqi

fine-tuning benchmarks safety

arXiv AI

Jul 28

Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models

arXiv:2601. 21003v3 Announce Type: replace Abstract: Large Language Models usually put more emphasis on accuracy and therefore, will guess even when not certain about the prediction, which is especially severe when fine-tuned on small datasets due to the inherent tendency toward miscalibration.

By Moule Lin, Shuhao Guan, Andrea Patane, David Gregg, Goetz Botterweck

llms fine-tuning benchmarks

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