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 16

Temporally Consistent and Controllable Video Generation of 2D Cine CMR via Latent Space Motion Modeling

arXiv:2606. 14759v1 Announce Type: cross Abstract: Cine cardiac magnetic resonance is the gold standard for assessing cardiac function, but the scarcity of public datasets limits the development of advanced data-driven models.

By Yiheng Cao (SyCoIA - IMT Mines Al\`es), Gustavo Andrade-Miranda (SyCoIA - IMT Mines Al\`es), Jiatian Zhang, Guillaume Sall\'e, Xin Gao

rag diffusion fine-tuning safety

arXiv Machine Learning

Jun 16

Adaptive Kernel Density Estimation with Pre-training

arXiv:2605. 13092v2 Announce Type: replace-cross Abstract: Density estimation in high-dimensional settings is an important and challenging statistical problem.

By Ruitong Zhang, Ke Deng

fine-tuning

arXiv AI

Jun 16

CogGuard: Cognitive and Operational Profiling for Proactive Warning in Edge Intelligent Services

arXiv:2606. 15199v1 Announce Type: new Abstract: Proactive warning is an important capability for edge intelligent services, where the system predicts whether a subject will successfully complete an incoming task under strict latency and privacy constraints.

By Zhi Yao, Weihao Chen, Zhiqing Tang, Hanshuai Cui, Qianli Ma, Weijia Jia, Wei Zhao

llms fine-tuning efficiency safety

arXiv AI

Jun 16

Rational Sparse Autoencoder

arXiv:2606. 14990v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are standard tools for mechanistic interpretability, but current SAE families are constrained by fixed encoder nonlinearities such as ReLU, JumpReLU, and TopK.

By Naiyu Yin, Yue Yu

llms fine-tuning efficiency safety

arXiv Machine Learning

Jun 16

Greedy Coordinate Diffusion: Effective and Semantically Coherent Adversarial Attacks via Diffusion Guidance

arXiv:2606. 15531v1 Announce Type: new Abstract: Fine-tuning aligned language models on benign tasks (e.

By Bohdan Turbal, Blossom Metevier, Max Springer, Aleksandra Korolova

llms diffusion fine-tuning safety

arXiv Machine Learning

Jun 16

Multi-Modal Spatio-Temporal Graph Neural Network with Mixture of Experts for Soil Organic Carbon Prediction

arXiv:2606. 16580v1 Announce Type: new Abstract: Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning.

By Daniele Mos, Felipe Drummond, Anton Bossenbroek, Soufiane el Khinifri

rag fine-tuning multimodal

arXiv AI

Jun 16

Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection

arXiv:2606. 14081v2 Announce Type: replace-cross Abstract: Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance.

By Huong Binh Vu

computer-vision fine-tuning benchmarks

arXiv AI

Jun 16

G-Loss: Graph-Guided Fine-Tuning of Language Models

arXiv:2604. 25853v3 Announce Type: replace-cross Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for the global semantic structure.

By Aditya Sharma, Vinti Agarwal, Rajesh Kumar

llms rag nlp fine-tuning benchmarks

arXiv Machine Learning

Jun 16

KVEraser: Learning to Steer KV Cache for Efficient Localized Context Erasing

arXiv:2606. 17034v1 Announce Type: cross Abstract: Post-hoc context erasing over the KV cache is challenging because a local edit has a global consequence: once a span has been processed, its influence propagates into the cached states of all subsequent tokens.

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

llms fine-tuning efficiency

arXiv AI

Jun 16

The Truth Stays in the Family: Enhancing Contextual Grounding via Inherited Truthful Heads in Model Lineages

arXiv:2606. 15821v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have produced many specialized multimodal LLMs (MLLMs) that share common foundational LLMs, forming distinct model lineages.

By Miso Choi, Seonga Choi, Mincheol Kwon, Woosung Joung, Jinkyu Kim, Jungbeom Lee

llms fine-tuning multimodal safety

arXiv AI

Jun 16

Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning

arXiv:2510. 16882v4 Announce Type: replace-cross Abstract: Supervised fine-tuning (SFT) is a commonly used technique to adapt large language models (LLMs) to downstream tasks.

By Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji

llms rag fine-tuning benchmarks safety

arXiv AI

Jun 16

PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization

arXiv:2606. 16074v1 Announce Type: cross Abstract: Motivation: Patient-generated text contains critical information on patients' lived experiences, social context, and care engagement, but remains largely unstructured, limiting its use in patient-centered outcomes research.

By Samah Fodeh, Linhai Ma, Ganesh Puthiaraju, Srivani Talakokkul, Afshan Khan, Elyas Irankhah, Sreeraj Ramachandran, Ashley Hagaman, Sarah Lowe, Aimee Roundtree

llms fine-tuning benchmarks

arXiv AI

Jun 16

Intelligence Is Not the Bottleneck: Validating an LLM First-Pass Manuscript Score Against Peer-Review Outcomes

arXiv:2606. 15887v1 Announce Type: cross Abstract: Large language model (LLM) systems are increasingly proposed to assist peer review, yet most evaluations judge the prose of machine-generated review text, not the validity of the numeric score a system assigns.

By Costa Georgantas

llms fine-tuning

arXiv Machine Learning

Jun 16

Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts

arXiv:2606. 15625v1 Announce Type: new Abstract: The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation.

By Yijun Lu, Zihan Fang, Pengpeng Qiao, Zheng Lin, Jing Yang, Yuxin Zhang, Por Lip Yee, Zhe Chen, Jun Luo

llms fine-tuning safety

arXiv Machine Learning

Jun 16

AME: A Multi-Type Contributor Attribution Framework in Generative AI Markets

arXiv:2606. 16075v1 Announce Type: new Abstract: Generative AI enables value creation through multi-stage collaboration among heterogeneous contributors, including training data, base models, fine-tuning behaviors, and prompts.

By Yang Shi, Songwen Pei, Yang Gao, Bingxue Zhang

fine-tuning

arXiv AI

Jun 16

MedAI: Evaluating TxAgent's Therapeutic Agentic Reasoning in the NeurIPS CURE-Bench Competition

arXiv:2512. 11682v2 Announce Type: replace Abstract: Therapeutic decision-making in clinical medicine constitutes a high-stakes domain in which AI guidance interacts with complex interactions among patient characteristics, disease processes, and pharmacological agents.

By Tim Cofala, Christian Kalfar, Jingge Xiao, Johanna Schrader, Michelle Tang, Wolfgang Nejdl

llms rag agents fine-tuning benchmarks safety

arXiv AI

Jun 16

VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models

arXiv:2606. 16140v1 Announce Type: new Abstract: This technical report introduces VibeThinker-3B, a compact dense model with 3B parameters developed to investigate how far verifiable reasoning can be pushed within a strictly small-model regime.

By Sen Xu, Shixi Liu, Wei Wang, Jixin Min, Yingwei Dai, Zhibin Yin, Yirong Chen, Xin Zhou, Junlin Zhang

llms reinforcement-learning fine-tuning efficiency

arXiv AI

Jun 16

Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning

arXiv:2606. 15598v1 Announce Type: new Abstract: Text-to-SQL aims to translate natural language questions into executable SQL queries over structured databases, enabling non-expert users to access data intuitively.

By Feng Lyu, Jinfeng Cen, Sijing Duan, Hao Wu, Shucheng Li, Weixu Zhang, Haolun Wu

llms fine-tuning benchmarks data-engineering

arXiv AI

Jun 16

ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing

arXiv:2606. 15315v1 Announce Type: new Abstract: Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating diverse user preferences into routing algorithms.

By Tingting Yang, Chenhao Xue, Jun Chen

llms rag fine-tuning

arXiv AI

Jun 16

Beyond Correctness: Enhancing Architectural Reasoning in Code LLMs via Scalable Labeling with Agentic Judgment

arXiv:2606. 14948v1 Announce Type: cross Abstract: LLMs have substantially improved software engineering yet real-world development requires architectural understanding.

By Kirill Vasilevski (Justina), Ximing Dong (Justina), Benjamin Rombaut (Justina), Ruochen Deng (Justina), Jiahuei Lin (Justina), Arthur Leung, Dayi Lin, Boyuan Chen, Shaowei Wang, Ahmed E. Hassan

llms agents fine-tuning

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