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 30

Mechanistic Personality Analysis of LLMs Steering Personality via Latent Feature Interventions

arXiv:2606. 28770v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated the ability to simulate human-like OCEAN personality traits in generated text.

By David Courtis, Ting Hu

llms fine-tuning benchmarks safety

arXiv AI

Jun 30

TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech

arXiv:2606. 30543v1 Announce Type: cross Abstract: With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important.

By Sathvik Manikantan Napa Ugandhar, Hao Zhang, Alison Gunzler, Yuzhe Wang, Thomas Thebaud, Georgi Tinchev, Venkatesh Ravichandran, Laureano Moro-Vel\'azquez

rag agents fine-tuning multimodal

arXiv AI

Jun 30

Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models

arXiv:2511. 14900v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis.

By Zehao Liu, Weijieying Ren, Jipeng Zhang, Tianxiang Zhao, Jingxi Zhu, Xiaoting Li, Vasant G Honavar

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 30

Internal-State Probes Read the Situation, Not the Action: Three Negative Results for Pre-Action Misalignment Monitoring

arXiv:2606. 30449v1 Announce Type: new Abstract: Probes on model internals could help monitor agentic systems if they identify harmful text or tool actions before those actions are generated.

By Max Fomin, Elad David, Amit LeVi

llms agents fine-tuning

arXiv Machine Learning

Jun 30

FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization

arXiv:2606. 28933v1 Announce Type: cross Abstract: Venture capital (VC) investment decisions face distinct challenges, such as multi-source heterogeneous data, non-stationary time series, and the demand for explainable predictions in high-stakes, low-data settings.

By Junyan Tan, Yifan Li, Minghao Wang, Zihan Chen, Haoyu Zhang

fine-tuning benchmarks safety

arXiv AI

Jun 30

KnowsTFM: Knowledge-Informed Fine-Tuning of Small Tabular Foundation Models

arXiv:2606. 30258v1 Announce Type: cross Abstract: Tabular foundation models have advanced deep learning for tabular data by delivering strong default performance across many small and medium tasks.

By Boshko Koloski, Xiangjian Jiang, Senja Pollak, Bla\v{z} \v{S}krlj, Mateja Jamnik, Nikola Simidjievski

fine-tuning

arXiv AI

Jun 30

Decomposing Memorization Reduction in Privacy-Preserving Fine-Tuning of SLMs for CSIRTs

arXiv:2606. 28479v1 Announce Type: cross Abstract: CSIRTs increasingly fine tune language models on vulnerability scan records, but these records expose internal network topology and create privacy risks under regulations such as GDPR and LGPD.

By Cristhian Kapelinski, Diego Kreutz

llms fine-tuning safety

arXiv AI

Jun 30

Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation

arXiv:2606. 30190v1 Announce Type: cross Abstract: Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity.

By Naeem Paeedeh, Mahardhika Pratama, Wolfgang Mayer, Mukesh Prasad, Weiping Ding, Yew-Soon Ong

llms diffusion fine-tuning multimodal benchmarks

arXiv AI

Jun 30

Early Cue Precision Shapes Visual Shortcut Learning in Controlled Cue-Manipulation Benchmarks

arXiv:2606. 30344v1 Announce Type: cross Abstract: Visual classifiers can achieve high matched-distribution accuracy while relying on low-level cues that fail under conflict or suppression.

By Chanho Park, Woochan Lee, Janyeong Oh, Geongho Gong, Minshu Kim, Yeachan Kwak, Seongim Choi

robotics fine-tuning benchmarks

arXiv AI

Jun 30

To Reason or to Fabricate: Reasoning Without Shortcuts via Hint-Anchored Pairwise Aggregation

arXiv:2606. 29481v1 Announce Type: cross Abstract: While reinforcement learning (RL) significantly enhances LLM reasoning, its efficacy is severely undermined by Pre-RL data overlap, where RL datasets overlap with pretraining or SFT corpora, causing models to exploit shortcuts by memorizing correct answers and fabricating post-hoc reasoning.

By Jiuheng Lin, Chen Zhang, Yansong Feng

llms reinforcement-learning fine-tuning

arXiv AI

Jun 30

Symbolic Mechanistic Data Attribution: Tracing Training Influence to Learned Behavioral Policies

arXiv:2606. 29171v1 Announce Type: cross Abstract: While existing data attribution methods can identify which training examples build specific mechanistic circuits, they cannot explain how training data shapes the high-level behavioral decisions a model learns to make.

By Reza Habibi, Darian Lee, Magy Seif El-Nasr

llms fine-tuning safety

arXiv AI

Jun 30

ReMAP-PET: Beyond Visual Understanding -- Learning Region-Guided Metabolic Alignment Semantics from Brain PET

arXiv:2606. 29577v1 Announce Type: cross Abstract: Positron Emission Tomography (PET) reveals brain metabolism and is clinically central to neurodegenerative disease assessment, yet existing 3D brain foundation models treat PET as generic volumetric data, missing the structured regional metabolic information that distinguishes it from structural neuroimaging.

By Dasen Dai, Yanteng Zhang, Shuoqi Li, Yuxiang Wei, Hongjie Yu, Qingxin Zhang, Qizhen Lan, Jagath C. Rajapakse, Vince D. Calhoun

rag fine-tuning safety

arXiv AI

Jun 30

Unlocking the Visual Record of Materials Science: A Large-Scale Multimodal Dataset from Scientific Literature

arXiv:2606. 29667v1 Announce Type: cross Abstract: The materials science literature encodes decades of experimental knowledge in figures, yet this visual record remains locked away and inaccessible to AI at scale.

By Subham Ghosh, Shubham Tiwari, Mohammad Ibrahim, Abhishek Tewari

llms fine-tuning multimodal safety

arXiv AI

Jun 30

Neural Subspace Reallocation: Continual Learning as Retrieval-Based Subspace Memory Management

arXiv:2606. 30067v1 Announce Type: cross Abstract: We introduce Neural Subspace Reallocation (NSR), which reframes continual learning as memory management over parameter subspaces.

By Byeong Hoon Yoon

rag fine-tuning efficiency benchmarks

arXiv AI

Jun 30

Field Order Should Not Matter: Permutation-Invariant Embedding Model Fine-Tuning for Structured Metadata Retrieval

arXiv:2606. 30473v1 Announce Type: cross Abstract: We study retrieval over catalogs of structured metadata, where each record is a small schema whose fields answer different kinds of query.

By Aivin V. Solatorio, Olivier Dupriez, Rafael Macalaba

llms rag agents fine-tuning benchmarks

arXiv AI

Jun 30

Assessing the Business Process Modeling Competences of Large Language Models

arXiv:2601. 21787v2 Announce Type: replace-cross Abstract: The creation of Business Process Model and Notation (BPMN) models is a complex and time-consuming task requiring both domain knowledge and proficiency in modeling conventions.

By Chantale Lauer, Peter Pfeiffer, Alexander Rombach, Nijat Mehdiyev

llms fine-tuning benchmarks

arXiv AI

Jun 30

Demonstration-Free Robotic Control via LLM Agents

arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.

By Brian Y. Tsui, Alan Y. Fang, Tiffany J. Hwu

llms agents robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 30

BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis

arXiv:2606. 29200v1 Announce Type: new Abstract: Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneity, demographics, and site-specific acquisition protocols.

By Kunyu Zhang, Tianxiang Xu

fine-tuning benchmarks safety

arXiv Machine Learning

Jun 30

Prototype Latent World Model Replay for Class-Incremental Learning

arXiv:2606. 29465v1 Announce Type: new Abstract: Class-incremental learning requires a model to learn new classes while preserving decision regions for old ones.

By Weizhi Nie, Hui Wang, Weijie Wang, Yuting Su

fine-tuning benchmarks

arXiv Machine Learning

Jun 30

LoRAShield: Data-Free Editing Alignment for Secure Personalized LoRA Sharing

arXiv:2507. 07056v2 Announce Type: replace-cross Abstract: The proliferation of Low-Rank Adaptation (LoRA) models has democratized personalized text-to-image generation, enabling users to share lightweight models (e.

By Jiahao Chen, Junhao Li, Yiming Wang, Yong Yang, Yi Jiang, Chunyi Zhou, Qingming Li, Tianyu Du, Shouling Ji

llms diffusion fine-tuning safety

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