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 17

Integration Matters: Rollout-Based Training for Constrained Diffusion Models

arXiv:2607. 14398v1 Announce Type: cross Abstract: Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution.

By Xiaoxuan Liang, Saeid Naderiparizi, Berend Zwartsenberg, Frank Wood

diffusion fine-tuning

arXiv AI

Jul 17

HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization

arXiv:2607. 14349v1 Announce Type: cross Abstract: While Large Language Models (LLMs) excel in many general NLP tasks, their formal reasoning capabilities are often compromised by content effects, demonstrating a measurable bias towards real-world plausibility.

By Abdullah Shaikh, Zain Naqi, Taha Zahid, Sandesh Kumar, Abdul Samad

llms nlp fine-tuning safety

arXiv AI

Jul 17

T^2MLR: Transformer with Temporal Middle-Layer Recurrence

arXiv:2607. 15178v1 Announce Type: cross Abstract: Transformer reasoning is limited by autoregressive decoding, which repeat edly compresses rich hidden computation through token space and makes it difficult for intermediate reasoning states to persist across time.

By Ziyang Cai, Xingyu Zhu, Yihe Dong, Yinghui He, Sanjeev Arora

llms fine-tuning

arXiv AI

Jul 17

Mixtures of SubExperts for Large Language Continual Learning

arXiv:2511. 06237v2 Announce Type: replace-cross Abstract: Enabling lifelong learning in LLMs demands resolving the stability-plasticity dilemma (i.

By Haeyong Kang, Hee Suk Yoon, Dahua Feng, Chang D. Yoo

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 17

Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning

arXiv:2607. 14367v1 Announce Type: new Abstract: Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable.

By Haobo Zhang, Jiankun Wang, Suraj Rajendran, Weishen Pan, Lam Tsoi, Yong Chen, Fei Wang, Jiayu Zhou

llms fine-tuning safety

arXiv Machine Learning

Jul 17

XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation

arXiv:2607. 14287v1 Announce Type: cross Abstract: Defect segmentation in additive manufacturing (AM) X-ray computed tomography (XCT) images remains challenging due to severe class imbalance and large distribution shifts across scan conditions.

By Md Mahedi Hasan, Md Mushfiqur Rahaman, Alan Pachkovskiy, Imtiaz Ahmed, Jeremy Dawson, Srinjoy Das

computer-vision fine-tuning benchmarks safety

arXiv Machine Learning

Jul 17

A vision foundation model for single-cell biology via spatial gene cartography

arXiv:2607. 14163v1 Announce Type: cross Abstract: Most single-cell foundation models are adapted from language models, representing each cell as a sequence of gene tokens.

By Ridvan Yesiloglu, Sakib Mostafa, James Zou, Ash Alizadeh, Jiajun Wu, Lei Xing, Ehsan Adeli, Md Tauhidul Islam

llms computer-vision fine-tuning

arXiv AI

Jul 17

Long-Context Fine-Tuning with Limited VRAM

arXiv:2607. 15105v1 Announce Type: new Abstract: Parameter-efficient fine-tuning reduces model and optimizer memory, but dense attention still makes long training sequences expensive.

By Vladimir Fedosov, Aleksandr Sazhin, Artemiy Grinenko, Frank Woernle

fine-tuning efficiency

arXiv AI

Jul 17

Stop Thinking, Start Looking: Efficient Post-Training for Multimodal Document Question Answering via Reasoning-Free Alignment

arXiv:2607. 14682v1 Announce Type: new Abstract: Efficient multimodal document question answering with explicit visual grounding, locating the precise document region that supports each answer remains an open challenge.

By Harikrishnan P M, Goutham Vignesh, Ganesh Parab, Saisubramaniam Gopalakrishnan, Vishal Vaddina, Varun V, Rohit Agrawal

nlp reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jul 17

Leveraging Instruction Tuning and Merging for Reasoning Model Adaptation

arXiv:2607. 14895v1 Announce Type: new Abstract: Reasoning language models (RLMs) have demonstrated impressive performance in domains such as mathematics and coding.

By Yu-Du Feng, Niels M\"undler-Sasahara, Mark Vero, Martin Vechev

llms nlp reinforcement-learning fine-tuning

arXiv AI

Jul 17

DialogueVPR: Towards Conversational Visual Place Recognition

arXiv:2607. 14115v1 Announce Type: new Abstract: Inspired by how humans communicate spatial information, language-guided geo-localization has gained significant traction for its intuitive and practical value.

By Yukun Song, Changwei Wang, Xingtian Pei, Shibiao Xu, Wenhao Xu, Shunpeng Chen, Yu Zhang, Ke Zhang, Rongtao Xu, Xuxiang Feng, Pengyang Wang

fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 17

GenTL: A General Transfer Learning Model for Building Thermal Dynamics

arXiv:2501. 13703v2 Announce Type: replace-cross Abstract: Transfer Learning (TL) is an emerging field in modeling building thermal dynamics.

By Fabian Raisch, Thomas Krug, Christoph Goebel, Benjamin Tischler

fine-tuning

arXiv Machine Learning

Jul 17

Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization

arXiv:2605. 21751v2 Announce Type: replace Abstract: Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coefficient, index, and parameter in the concrete problem data.

By Zhiqi Gao, Albert Ge, Alexander Berenbeim, Nathaniel D. Bastian, Frederic Sala

reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 17

WrAFT: a Modularized Automated Writing Evaluation System for Argumentative Essays

arXiv:2607. 14524v1 Announce Type: new Abstract: This study presents WrAFT, a Writing Assessment and Feedback Tool, that delivers both accurate and reliable scores and effective comprehensive feedback to argumentative essays.

By Adnan Labib, Yixuan Huang, Jiahui Wu, John Maurice Gayed, Zheng Yuan, Qiao Wang

llms fine-tuning benchmarks

arXiv AI

Jul 17

Multi-LLM Collaborative MRI Report Generation for Visual Instruction Tuning in Brain Oncology

arXiv:2607. 14581v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) and their extension to vision-language models (VLMs) have made it easier to combine text and images for tasks such as report generation.

By Sinyoung Ra, Jonghun Kim, Hyunjin Park

llms nlp fine-tuning multimodal

arXiv Machine Learning

Jul 17

Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields

arXiv:2607. 14486v1 Announce Type: cross Abstract: Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows.

By Sheng Bi, Yi-Ze Wang, Jun Cheng

fine-tuning

arXiv AI

Jul 17

Step-Level Preference Learning for Generative Agents in Social Simulations

arXiv:2607. 14485v1 Announce Type: new Abstract: Large language model (LLM)-based generative agents simulate human behavior through long-horizon decision-making processes that comprise intermediate steps such as planning, memory retrieval, reflection, and action selection.

By Wenchang Gao, Pingyue Sheng, Lanlan Qiu, Yunfei Ma, Jian Zhao, Baicheng Chen, Kangda Wang, Yuyang Tian, Shunqiang Mao, Tianxing He

llms agents fine-tuning

arXiv AI

Jul 17

Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers

arXiv:2607. 14984v1 Announce Type: new Abstract: Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect.

By Mahmoud Ibrahim, Bart Elen, Chang Sun, Gokhan Ertaylan, Michel Dumontier

diffusion fine-tuning safety

arXiv Machine Learning

Jul 17

Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion

arXiv:2607. 14371v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized AI services, but a critical tension emerges: while personalization improves model performance, it consumes scarce computational resources that users must share.

By Fengzhuo Zhang, Zhuoran Yang, Dirk Bergemann

llms fine-tuning

arXiv AI

Jul 17

Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening

arXiv:2607. 15047v1 Announce Type: cross Abstract: Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundaries.

By Javad Khoramdel, Farhad Hoseyni, Amirhossein Nikoofard

rag fine-tuning

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