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

Jun 15

GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging

arXiv:2606. 13919v1 Announce Type: cross Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of older adults, with prevalence expected to rise significantly in the coming years.

By Chen Zhao, Huan Huang, Yixin Xie, Jiajing Huang, Weihua Zhou, Nandakumar Narayanan

fine-tuning benchmarks

arXiv AI

Jun 15

STaR-DRO: Stateful Tsallis Reweighting for Group-Robust Structured Prediction

arXiv:2604. 09737v2 Announce Type: replace-cross Abstract: Structured prediction with large language models requires outputs that are label-accurate, ontology-constrained, structurally valid, and evidence-grounded under label imbalance and heterogeneous group difficulty.

By Samah Fodeh, Ganesh Puthiaraju, Elyas Irankhah, Afshan Khan, Sreeraj Ramachandran, Linhai Ma, Srivani Talakokkul, Sarah Schellhorn

llms fine-tuning safety

arXiv Machine Learning

Jun 15

Shuttling Compiler for Trapped-Ion Quantum Computers Based on Large Language Models

arXiv:2512. 18021v3 Announce Type: replace-cross Abstract: We present the first shuttling compiler based on large language models (LLMs) for trapped-ion quantum computers, where qubits are shuttled between segments for gate execution and qubit storage.

By Fabian Kreppel, Reza Salkhordeh, Ferdinand Schmidt-Kaler, Andr\'e Brinkmann

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 15

When to Write and When to Suppress: Route-Specialized Dual Adapters for Memory-Assisted Knowledge Editing

arXiv:2606. 14668v1 Announce Type: new Abstract: Knowledge editing systems must update selected facts while preserving nearby but irrelevant behavior.

By Yining Huang

llms rag fine-tuning benchmarks

arXiv Machine Learning

Jun 15

NeST: Neuron Selective Tuning for LLM Safety

arXiv:2602. 16835v2 Announce Type: replace-cross Abstract: Safety alignment is essential for the responsible deployment of Large Language Models (LLMs).

By Sasha Behrouzi, Lichao Wu, Mohamadreza Rostami, Ahmad-Reza Sadeghi

llms fine-tuning multimodal safety

arXiv AI

Jun 15

No Accidental Software Agent First Canonical Code for Human Code Entropy Reduction and 30 to 500 times Lower Frontier Model Requirements

arXiv:2606. 14357v1 Announce Type: cross Abstract: Frontier coding models may spend substantial capacity learning not only program behavior, but also accidental entropy in human repositories.

By Jepson Taylor

agents fine-tuning

arXiv AI

Jun 15

A Benchmark and Framework for Evaluating Next Action Predictions in Spreadsheets

arXiv:2606. 13802v1 Announce Type: cross Abstract: Predictive code completion greatly accelerates how quickly developers work.

By Tejas Agrawal, Vu Le, Sumit Gulwani, Gust Verbruggen

llms fine-tuning benchmarks

arXiv AI

Jun 15

Refusal Beyond a Single Direction: A Preliminary Comparison of Diff-in-Means and INLP

arXiv:2606. 13720v1 Announce Type: new Abstract: Arditi et al.

By Elisabetta Rocchetti, Alfio Ferrara

fine-tuning benchmarks safety

arXiv AI

Jun 15

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack

arXiv:2606. 14409v1 Announce Type: cross Abstract: In this report, we present Hy-Embodied-0.

By He Zhang, Lingzhu Xiang, Haitao Lin, Zeyu Huang, Minghui Wang, Dingyan Zhong, Yubo Dong, Yihao Wu, Yongming Rao, Dongsheng Zhang, Wanjia He, Ling Chen, Kai Huang, Jiahao Chen, Sichang Su, Xumin Yu, Ziyi Wang, Chengwei Zhu, Xiao Teng, Yuchun Guo, Yufeng Zhang, Yuandong Liu, Rui Wang, Zisheng Lu, Han Hu, Zhengyou Zhang

reinforcement-learning robotics fine-tuning multimodal

arXiv Machine Learning

Jun 15

Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models

arXiv:2606. 14222v1 Announce Type: new Abstract: The rapid evolution of Time Series Foundation Models (TSFMs) has advanced zero-shot forecasting across diverse domains.

By Xilin Dai, Yiding Liu, Hongjie Xia, Yifan Hu, Zewei Dong, Jiang-Ming Yang, Qiang Xu

llms fine-tuning benchmarks

arXiv AI

Jun 15

A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction

arXiv:2606. 14498v1 Announce Type: cross Abstract: Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbitals, energy levels, and electronic-structure observables that energy-only surrogates cannot resolve.

By Yunhong Lou, Xihang Yue, Xinran Wei, Tianqi Deng, Linchao Zhu

fine-tuning benchmarks

arXiv AI

Jun 15

Dense Coordinate-List Fine-Tuning Induces a Controllable Interference Surface in Vision-Language Models

arXiv:2606. 14507v1 Announce Type: new Abstract: Fine-tuning vision-language models to emit dense coordinate lists improves visual grounding but also changes how models serialize, repeat, and terminate structured outputs.

By Chenyu Zhou, Qiliang Jiang, Boguang Pan

llms fine-tuning multimodal

arXiv AI

Jun 15

Towards Direct Latent-Space Synthesis for Parallel Branches in LLM-Agent Workflows

arXiv:2606. 14672v1 Announce Type: new Abstract: Large language models increasingly serve as execution engines for agentic systems, yet they still consume context through a sequential text interface.

By Shikun Liu, Mufei Li, Dongqi Fu, Haoyu Wang, Yinglong Xia, Hong Li, Hong Yan, Pan Li

llms agents fine-tuning

arXiv AI

Jun 15

FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA

arXiv:2602. 23638v3 Announce Type: replace-cross Abstract: Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data.

By Haoran Zhang, Dongjun Kim, Seohyeon Cha, Haris Vikalo

llms fine-tuning safety

arXiv AI

Jun 15

UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities

arXiv:2504. 20734v5 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries.

By Woongyeong Yeo, Kangsan Kim, Soyeong Jeong, Jinheon Baek, Sung Ju Hwang

rag fine-tuning benchmarks

arXiv AI

Jun 15

Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost

arXiv:2602. 03120v2 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune.

By Yinggan Xu, Kajetan Schweighofer, Risto Miikkulainen, Xin Qiu

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jun 15

Token-Level LLM Collaboration via FusionRoute

arXiv:2601. 05106v5 Announce Type: replace Abstract: Large language models (LLMs) exhibit strengths across diverse domains.

By Nuoya Xiong, Yuhang Zhou, Hanqing Zeng, Zhaorun Chen, Furong Huang, Shuchao Bi, Lizhu Zhang, Zhuokai Zhao

llms fine-tuning benchmarks

arXiv AI

Jun 15

Unsupervised Learning of Efficient Exploration: Pre-training Adaptive Policies via Self-Imposed Goals

arXiv:2601. 19810v2 Announce Type: replace-cross Abstract: Unsupervised pre-training can equip reinforcement learning agents with prior knowledge and accelerate learning in downstream tasks.

By Octavio Pappalardo

agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 15

3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding

arXiv:2603. 04976v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards ( RLVR ) has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models ( LLMs), yet its potential in 3D scene understanding remains under-explored.

By Xiongkun Linghu, Jiangyong Huang, Baoxiong Jia, Siyuan Huang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 15

Towards Steering without Sacrifice: Principled Training of Steering Vectors for Prompt-only Interventions

arXiv:2605. 05983v2 Announce Type: replace Abstract: Recently, steering vectors (SVs) have emerged as an effective and lightweight approach to steer behaviors of large language models (LLMs), among which fine-tuned SVs are more effective than optimization-free ones.

By Yuntai Bao, Qinfeng Li, Xinyan Yu, Ge Su, Wenqi Zhang, Liu Yan, Haiqin Weng, Jianwei Yin, Xuhong Zhang

llms fine-tuning safety

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