Adapter Tuning

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5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 56

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EHRAdapt: Adapting Pretrained Language Models to Electronic Health Records with Semantic Priors for Rare Clinical Events
Sep 27, 2026Andre R Goncalves, Vincent Liu, Priyadip RayLong-Tail Learning Adapter Tuning
Lawrence Livermore National Laboratory · Kaiser Permanente
TaskAnchor: Grounding Task State in Reactive VLAs for Long-Horizon Manipulation
Sep 20, 2026Hengyan Liu, Wenlve Zhou, Bo Yue +7Memory-Augmented VLMs Long-Horizon Robotic Manipulation
The Chinese University of Hong Kong, Shenzhen · Foshan University · DexForce +1
Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs
Sep 16, 2026Mohan Shi, Zilai Wang, Natarajan Balaji Shankar +3Domain Adaptation for ASR Adapter Tuning
Department of Electrical and Computer Engineering University of California Los Angeles Los Angeles, USA
Multi-modal Knowledge Preserving Adapter for Embedding Backward Compatibility
Sep 15, 2026Jaeseok Byun, Gukyeong Kwon, Han-Kai Hsu +4Multimodal Large Language Models Adapter Tuning
1Seoul National University, South Korea · 2Amazon AGI, USA
CAL-MOS: Bridging Layers with Adapters for Robust MOS Prediction Across Speech Foundation Models
Sep 14, 2026Alef Iury Siqueira Ferreira, Pedro Lustosa Rege Botelho, Fernanda Silva +5Speech Foundation Models Speech Quality Assessment
AKCIT, Brazil · Federal University of Rio Grande do Norte (UFRN), Brazil · Federal University of Technology (UTFPR), Brazil +1
TACTICL: Task-Aware Compression of Tabular ICL Models
Aug 11, 2026Mykhailo Koshil, Matthias Feurer, Katharina EggenspergerModel Compression Tabular Foundation Models
TU Dortmund University, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Germany
Beyond Rotations: AuroOFT for Expressive Quantized Orthogonal Fine-Tuning
Aug 5, 2026Yue Han, Dianlin WangLLM Quantization Fine-Tuning
College of Systems Engineering, National University of Defense Technology · College of Computer Science and Technology, National University of Defense Technology
Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning
Aug 5, 2026Yue Han, Ziniu LiuSupervised Fine-Tuning Multi-Task Learning
College of Systems Engineering, National University of Defense Technology · College of Computer Science and Technology, National University of Defense Technology
Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning
Jun 10, 2026Jiaqi LuoFine-Tuning Adapter Tuning
School of Mathematical Sciences, Soochow University, No.1 Shizi Street, Suzhou, 215006, Jiangsu Province, China
Spatial-Temporal Decoupled Adapter for Micro-gesture Online Recognition
Jun 5, 2026Xucheng Shen, Kun Li, Fei Wang +3Fine-Tuning Human Activity Recognition
Hefei University of Technology, Hefei, China · United Arab Emirates University, Al Ain, United Arab Emirates · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China +1
TALAN: Task-Aligned Latent Adaptation Networks for Targeted Post-Training of Large Language Models
Jun 5, 2026Chengkai Zhang, Ziteng Liu, Junpu Wang +4Supervised Fine-Tuning Adapter Tuning
Meta AI
RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit
Jun 4, 2026Amirhossein Ghaffari, Ali Goodarzi, Huong Nguyen +3LLM Evaluation LLM Fine-Tuning
Future Computing Group University of Oulu Oulu, Finland · Centre for Applied Computing University of Oulu Oulu, Finland
GLASS: GRPO-Trained LoRA for Acoustic Style Steering in Zero-Shot Text-to-Speech
Jun 4, 2026Jaehoon Kang, Yejin Lee, Kyuhong ShimTTS Synthesis Adapter Tuning
Department of Artificial Intelligence, Sungkyunkwan University, Korea
TaDA: Calibrated Probe Gating for Task-Domain LoRA Merging
Jun 3, 2026Huy Quoc To, Fuyi Li, Guangyan Huang +1Adapter Tuning Low-Rank Adaptation
Deakin University, Melbourne, VIC, Australia · Adelaide University, Adelaide, SA, Australia
Towards Pretraining Text Encoders for TabPFN
Jun 3, 2026Mustafa Tajjar, Alexander Pfefferle, Lennart Purucker +1Tabular Foundation Models Fine-Tuning
Department of Computer Science, University of Freiburg, Freiburg, Germany · ELLIS Institute Tübingen, Tübingen, Germany · Prior Labs, Freiburg, Germany.
Compress then Merge: From Multiple LoRAs into One Low-Rank Adapter
Jun 2, 2026Zhengbao He, Ruiqi Ding, Zhehao Huang +3Adapter Tuning Low-Rank Adaptation
Institute of Image Processing and Pattern Recognition, School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China · Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning
Jun 1, 2026Yu-Cheng Shi, Zhen-Hao Xie, Jun-Tao Tang +1Large Vision-Language Models Adapter Tuning
School of Artificial Intelligence, Nanjing University, China · State Key Laboratory of Novel Software Technology, Nanjing University, China
MASER: Modality-Adaptive Specialist Routing for Embodied 3D Spatial Intelligence
Jun 1, 2026Hilton Raj, Vishnuram AVEmbodied QA Adapter Tuning
Boston University
On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters
Jun 1, 2026Mind Lab, :, Vin Bo +64LLM Fine-Tuning Adapter Tuning
Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks
Jun 1, 2026Nermeen Abou Baker, David Rohrschneider, Uwe HandmannFine-Tuning Instance Segmentation
Ruhr West University of applied sciences · Lützowstraße 5, 46236 Bottrop, Germany: Ruhr West University of Applied Sciences
Finer Parameter Steps for Low-Rank PEFT: A Controlled Study with CP Tensor Adapters
May 29, 2026Xinjue Wang, Xiuheng Wang, Yejun Zhang +3Tensor Decomposition Adapter Tuning
Department of Information and Communications Engineering, Aalto University, 02150, Espoo, Finland · Université de Lorraine, CNRS, CRAN, France · Department of Computer Science, Aalto University, 02150, Espoo, Finland +1
CRMA: A Spectrally-Bounded Backbone for Modular Continual Fine-Tuning of LLMs
May 29, 2026Kiran Nayudu, Aswini Nutakki, Sai Vinay Naidu +1Continual Learning for LLMs Fine-Tuning
ModelBrew AI
CSULoRA: Closest Safe Update Low-Rank Adaptation
May 28, 2026Oleksandr Marchenko Breneur, Adelaide Danilov, Aria Nourbakhsh +1Adapter Tuning Low-Rank Adaptation
Department of Computer Science, University of Luxembourg Esch-sur-Alzette, Luxembourg
SLAD : Shared LoRA Adapters for Task Specific Distillation
May 28, 2026Reda Bensaid, Yassir Bendou, Vincent Gripon +1Vision Foundation Model Adaptation Adapter Tuning
IMT Atlantique, Brest, France · Polytechnique Montr´eal, Montr´eal, Canada
FoRA: Fisher-orthogonal Rank Adaptation for Parameter-Efficient Fine-Tuning
May 28, 2026Juneyoung Park, Seongbae Lee, Han-Sang Lee +6LLM Fine-Tuning Adapter Tuning
OptAI Inc · LG Uplus
Signs Beat Floats: Low-Rank Double-Binary Adaptation for On-Device Fine-Tuning
May 22, 2026Yoshihiko Fujisawa, Yuma Ichikawa, Yudai Fujimoto +2LLM Quantization Memory-Efficient Fine-Tuning
Fujitsu Limited · Institute of Science Tokyo · RIKEN Center for AIP +1
SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning
May 20, 2026Yongkang Liu, Xing Li, Mengjie Zhao +7LLM Fine-Tuning Adapter Tuning
Northeastern University, China · Shandong University, China · CIS, LMU Munich, Germany +1
Lighting-aware Unified Model for Instance Segmentation
May 19, 2026Qisai Liu, Alloy Das, Zhanhong Jiang +4Instance Segmentation Vision Foundation Model Adaptation
Iowa State University, Ames, IA 50011, USA
AR1-ZO: Topology-Aware Rank-1 Zeroth-Order Queries for High-Rank LoRA Fine-Tuning
May 19, 2026Ziye Chen, Hongbin Lin, Chenyu Zhang +3Memory-Efficient Fine-Tuning Zeroth-Order Optimization
Closing the Gap at CRAC 2026: Two-Stage Adaptation for LLM-Based Multilingual Coreference Resolution
May 16, 2026Antoine Bourgois, Olga Seminck, Thierry PoibeauMultilingual Language Models Coreference Resolution
Lattice (CNRS UMR 8094 & ENS-PSL & Universit´e Sorbonne Nouvelle), Montrouge, France
FIM-LoRA: Task-Informative Rank Allocation for LoRA via Calibration-Time Gradient-Variance Estimation
May 16, 2026Ramakrishnan SathyavageeswaranAdapter Tuning Low-Rank Adaptation
Intuit
PreFT: Prefill-only finetuning for efficient inference
May 14, 2026Andrew Lanpouthakoun, Aryaman Arora, Zhengxuan Wu +4Fine-Tuning LLM Serving
Stanford University · Tilde Research
MinT: Managed Infrastructure for Training and Serving Millions of LLMs
May 13, 2026Mind Lab, :, Song Cao +60LLM Serving Adapter Tuning
Not How Many, But Which: Parameter Placement in Low-Rank Adaptation
May 12, 2026Arijit Sehanobish, Charles LoveringAdapter Tuning Low-Rank Adaptation
Kensho Technologies
TB-AVA: Text as a Semantic Bridge for Audio-Visual Parameter Efficient Finetuning
May 12, 2026Seongah Kim, Dinh Phu Tran, Hyeontaek Hwang +3Audio-Visual Understanding Audio-Visual Representation Learning
AI2 Lab, KAIST
Decomposing Evolutionary Mixture-of-LoRA Architectures: The Routing Lever, the Lifecycle Penalty, and a Substrate-Conditional Boundary
May 11, 2026Ramchand KumaresanAdaptive Model Routing Evolutionary Optimization
Murai Labs
AdaPaD: Adaptive Parallel Deflation for PEFT with Self-Correcting Rank Discovery
May 11, 2026Barbara Su, Fangshuo Liao, Anastasios KyrillidisLLM Fine-Tuning Adapter Tuning
Department of Computer Science Rice University
HELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models
May 11, 2026Jia Wei, Zhonghao Zhang, Ping Chen +5Mixture-of-Experts Language Models Fine-Tuning
Department of Computer Science and Technlogy Tsinghua University Beijing 100084, China · School of Computer Science and Technlogy Xi’an Jiaotong University · The State Key Laboratory of Blockchain and Data Security, Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security
LLiMba: Sardinian on a Single GPU -- Adapting a 3B Language Model to a Vanishing Romance Language
May 9, 2026Luca BalloreLanguage Model Pretraining Adapter Tuning
Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atoms
May 8, 2026Omatharv Bharat Vaidya, Connor T. Jerzak, Nhat Ho +1Memory-Augmented Neural Networks Adapter Tuning
Department of Computer Science University of Texas at Austin · Department of Government University of Texas at Austin · University of Texas at Austin
MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning
May 8, 2026Ionut-Vlad Modoranu, Mher Safaryan, Dan AlistarhLLM Fine-Tuning Hierarchical Representation Learning
Institute of Science and Technology Austria (ISTA). · Lancaster University, UK · ISTA
DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models
May 8, 2026Mengxin Qin, Xiang Zhang, Xi Wang +3Vision-Language Models Fine-Tuning
School of Electronic Engineering, Xidian University Xi’an 710071, China
Rethinking Adapter Placement: A Dominant Adaptation Module Perspective
May 7, 2026Suoxin Zhang, Run He, Di Fang +3LLM Fine-Tuning Adapter Tuning
South China University of Technology, China · Zhejiang University, China
TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
May 7, 2026Duong Nguyen, Mohammed Jawhar, Nicolas ChesneauTabular Foundation Models Adapter Tuning
Ekimetrics
EGA: Adapting Frozen Encoders for Vector Search with Bounded Out-of-Distribution Degradation
May 7, 2026Dongfang ZhaoOOD Generalization Vision Foundation Model Adaptation
Tacoma School of Engineering and Technology University of Washington
Compress Then Adapt? No, Do It Together via Task-aware Union of Subspaces
May 4, 2026Jingze Ge, Yun Liu, Xue Geng +4Model Compression Adapter Tuning
National University of Singapore · Nankai University · Institute for Infocomm Research (I2R), A*STAR
SCALE-LoRA: Auditing Post-Retrieval LoRA Composition with Residual Merging and View Reliability
May 2, 2026Shuaipeng Zhou, Yu ZhangUncertainty Quantification Adapter Tuning
School of Computer Science and Technology, Shenyang University of Chemical Technology · 2Liaoning Key Laboratory of Intelligent Technology for Chemical Process Industry
GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models
May 2, 2026Zhiwen Ruan, Yichao Du, Jianjie Zheng +6LLM Fine-Tuning Large Language Model-Guided Optimization
Southern University of Science and Technology · Alibaba Group · Shanghai University of Finance and Economics +2
BoostLoRA: Growing Effective Rank by Boosting Adapters
Apr 30, 2026Raviteja Anantha, Nick Levato, Layne C. PriceLLM Fine-Tuning Adapter Tuning
Amazon
Low-Rank Adaptation Redux for Large Models
Apr 23, 2026Bingcong Li, Yilang Zhang, Georgios B. GiannakisAdapter Tuning Low-Rank Adaptation
Dept. of CS, ETH Zürich, Switzerland · Dept. of ECE, U. of Minnesota, USA · Morgan Stanley, USA
Crowded in B-Space: Calibrating Shared Directions for LoRA Merging
Apr 18, 2026Yixuan Tang, Yi YangAdapter Tuning Low-Rank Adaptation
The Hong Kong University of Science and Technology
Introspection Adapters: Training LLMs to Report Their Learned Behaviors
Apr 18, 2026Keshav Shenoy, Li Yang, Abhay Sheshadri +4LLM Auditing Language Model Introspection
Anthropic Fellows · Ashwood Centre on AI Science and Policy, University of Cambridge · Anthropic
SK-Adapter: Skeleton-Based Structural Control for Native 3D Generation
Mar 14, 2026Anbang Wang, Yuzhuo Ao, Shangzhe Wu +13D Foundation Models 3D Asset Generation
The Hong Kong University of Science and Technology · University of Cambridge
CeRA: Breaking the Linear Ceiling of Low-Rank Adaptation with Non-linearity Retained at Inference
Feb 26, 2026Hung-Hsuan ChenAdapter Tuning Low-Rank Adaptation
Computer Science and Information Engineering National Central University Taoyuan, Taiwan
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning
Jun 10, 2025Javier Lopez-Piqueres, Pranav Deshpande, Archan Ray +3Transformer Tensor Decomposition
Global Technology Applied Research, JPMorgan Chase, New York, NY 10001, USA
ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning
Date pendingXianming Li, Zongxi Li, Tsz-fung Andrew Lee +3Fine-Tuning Adapter Tuning
PolyU · School of Data Science, Lingnan University, Hong Kong SAR

Sep 27, 2026, cs.AIJ/K move · Enter open · S save

EHRAdapt: Adapting Pretrained Language Models to Electronic Health Records with Semantic Priors for Rare Clinical Events

Andre R Goncalves, Vincent Liu, Priyadip Ray

Lawrence Livermore National Laboratory · Kaiser Permanente

Long-Tail Learning Adapter Tuning Domain Adaptation for LLMs Longitudinal EHR Modeling Electronic Health Records

Open paper AskPDF arXiv

Electronic health records (EHRs) encode clinical histories as (time, modality, code) tuples, whereas pretrained language models expect text tokens. Serializing them as text inflates sequence length and redundantly encodes structure. We introduce EHRAdapt, an adapter that maps tuples directly into a frozen language model's embedding space. Modality receives a learned embedding, time gaps enter through learned attention biases, and event codes receive dedicated vectors. Learning event vectors is the central challenge: clinical vocabularies are long-tailed, leaving rare events too few observations for reliable estimates. EHRAdapt therefore represents each event vector as the sum of a semantic prior and an evidence residual. The prior is a frozen embedding of the event's clinical description from a biomedical language model trained on clinical ontologies, mapped into the model's input space by a shared learned projection, so it supplies clinical meaning even when observations are scarce. The residual, a learned low-rank event-specific correction, refines it as evidence accumulates. We run continued pretraining on about 4 million patients' records with three frozen LLM backbones (OLMo2 1B, Llama3.2 1B, and OLMo2 7B), training only the adapter (0.1--0.6% of all parameters). The full adapter outperforms all ablations in held-out next-event prediction on every backbone. Removing the semantic pathway hurts rare events over ten times more than the most frequent ones, whereas removing the residual hurts overall prediction but improves it for the rarest events. On reportable infectious-disease and syndromic downstream classification tasks, EHRAdapt outperforms text-based LLM and count-based baselines, and both pathways improve rare-disease discrimination. The two pathways therefore play complementary roles, visible only when results are broken down by event frequency rather than averaged.

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