Domain Adaptation

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Overview

Domain adaptation studies how to make a model trained in one setting work in another, where images, sensors, language, populations, or conditions may differ. Common approaches align feature distributions with methods such as MMD, CORAL, or adversarial training; generate pseudo-labels for target data; or fine-tune models with a small labeled set, often using lightweight adapters. Recent work also explores source-free and test-time adaptation, as well as foundation models, across settings such as medical imaging, speech, graphs, remote sensing, and sim-to-real transfer. A central challenge is that alignment can cause negative transfer when label spaces or class proportions differ, so methods must balance target gains with preservation of useful source knowledge and be tested under realistic shifts.

Latest papers

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253 papers26 papers in the last four weeks, up 30% on the four weeks before. 0.2% of all new papers.

May 14, 2026

Tokenizer Fertility and Zero-Shot Performance of Foundation Models on Ukrainian Legal Text: A Comparative Study

Tokenizer fertility varies 1.6x across foundation models on Ukrainian legal text, yet this cost-critical dimension is absent from model selection practice. We benchmark seven models from five providers on 273 validated court decisions from Ukraine's state registry (EDRSR), measuring tokenizer fertility and zero-shot performance on three tasks. Four findings emerge. (1) Qwen 3 models consume 60% more tokens than Llama-family models on identical input, making tokenizer analysis a prerequisite for cost-efficient deployment. (2) NVIDIA Nemotron Super 3 (120B) achieves the highest composite score (83.1), outperforming Mistral Large 3 (5.6x more total parameters) at one-third the API cost model scale is a poor proxy for domain performance. (3) Few-shot prompting degrades performance by up to 26 percentage points; stratified and prompt-sensitivity ablations confirm this is intrinsic to Ukrainian-language demonstrations, not an artifact of example selection. (4) A cross-temporal generalization experiment reveals that classifiers trained on pre-war court ecisions (2008-2013) lose 27.9 percentage points when applied to full-scale invasion era decisions (2022-2026), with a pronounced forward-backward asymmetry: newer models transfer backward (+14.6 pp above forward transfer), but older models fail catastrophically on wartime legal language. For practitioners: tokenizer analysis should precede model selection, and zero-shot is a more reliable default than few-shot for morphologically rich languages. To support reproducibility and address the absence of Ukrainian from legal NLP benchmarks, we release a public dataset of 14,452 court decisions spanning 2008-2026, annotated with seven outcome labels across three temporal epochs that capture the impact of armed conflict on judicial proceedings.

Volodymyr Ovcharov

LEX AI Platform, legal.org.ua Kyiv, Ukraine

Multilingual Language Model EvaluationLegal NLPZero-Shot Text ClassificationDomain AdaptationText ClassificationTemporal Domain Adaptation
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May 14, 2026

GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning

Graph prompt tuning has shown great potential in graph learning by introducing trainable prompts to enhance the model performance in conventional single-domain scenarios. Recent research has extended graph prompts to improve Graph Foundation Models (GFMs) by few-shot tuning auxiliary prompts. Despite their progress, most existing methods embed source-domain information into prompts, which serve either as input to GFMs or encoded during model pre-training. Such prompt entanglement with specific source domains and GFM pre-training strategy restricts their generalisability to other domains and different GFMs. Furthermore, existing GFM prompts merely rely on few-shot tuning for adaptation, neglecting the rich information in unlabelled target domain test data. Motivated by these insights, this paper aims to empower GFMs with pre-training-agnostic test-time graph prompt tuning, named GFMate. GFMate introduces centroid and layer prompts applied after pre-training on target domains, avoiding entanglement with specific source domains and model pre-training. In addition, a test-time complementary learning objective is devised to exploit both labelled and unlabelled target domain data for effective test-time prompt tuning. Extensive experiments on 12 benchmark datasets demonstrate the superior performance and efficiency of GFMate, achieving improvements of up to 30.63%. Code is available at https://github.com/YanJiangJerry/GFMate.

Yan Jiang, Ruihong Qiu, Zi Huang

School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Queensland, Australia

Domain AdaptationGraph Domain AdaptationPrompt TuningGraph Foundation ModelsTest-Time Prompt Tuning
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May 14, 2026

Refining Pseudo-Audio Prompts with Speech-Text Alignment for Text-Only Domain Adaptation in LLM-Based ASR

LLM-based automatic speech recognition models demonstrate strong performance by connecting audio encoders and LLMs. However, data scarcity of paired speech and transcription often hinders their adaptation to new domains, making text-only domain adaptation crucial. Existing methods typically rely on either fine-tuning the LLM alone or employing pseudo-audio prompts. The former neglects essential acoustic context, while the latter either suffers from limited scalability in data-scarce conditions, or yields inexpressive prompts by leveraging only textual features, ignoring audio modality. To address this, we propose an enhanced framework that explicitly models speech-text alignment. Our method efficiently generates highly expressive pseudo-audio prompts that bridges the modality gap, enabling effective target-domain adaptation. Experiments demonstrate that our approach outperforms existing text-only methods, improving both overall error rates and out-of-vocabulary coverage.

Ryo Magoshi, Takashi Maekaku, Yusuke Shinohara

Graduate School of Informatics, Kyoto University, Japan · LY Corporation, Japan

Domain Adaptation for ASRDomain AdaptationAutomatic Speech RecognitionSpeech Language Models
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May 14, 2026

Language-Induced Priors for Domain Adaptation

Domain adaptation faces a fundamental paradox in the cold-start regime. When target data is scarce, statistical methods fail to distinguish relevant source domains from irrelevant ones, which often leads to negative transfer. In this paper, we address this challenge by leveraging expert textual descriptions of the target domain, a resource that is often available but overlooked. We propose a probabilistic framework that translates these semantic descriptions into a choice model, namely a Language-Induced Prior (LIP), that learns the preferences from a pretrained Large Language Model (LLM). The LIP is then integrated into an Expectation-Maximization algorithm to identify source relevance. Methodologically, this framework is compatible with any parametric model where a likelihood is available. It allows the LIP to guide the selection of sources when target signals are weak, while gradually refining these choices as samples accumulate. Theoretically, we prove that the estimator roughly matches an oracle cold-start MSE under a correct prior, while remaining asymptotically consistent regardless of the quality of the LIP. Empirically, we validated the framework on a descriptive (Gaussian estimation), a predictive (C-MAPSS dataset), and a prescriptive task (MuJoCo hopper).

Qiyuan Chen, Jiayu Zhou, Raed Al Kontar

Industrial and Operations Engineering University of Michigan Ann Arbor, MI 48109 · School of Information University of Michigan Ann Arbor, MI 48109

Multi-Source Domain AdaptationDomain AdaptationNegative TransferProbability Elicitation from Language Models
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May 14, 2026

TILT: Target-induced loss tilting under covariate shift

We introduce and analyze Target-Induced Loss Tilting (TILT) for unsupervised domain adaptation under covariate shift. It is based on a novel objective function that decomposes the source predictor as f+bf+b, fits f+bf+b on labeled source data while simultaneously penalizing the auxiliary component bb on unlabeled target inputs. The resulting fit ff is deployed as the final target predictor. At the population level, we show that this target-side penalty implicitly induces relative importance weighting at the population level, but in terms of an estimand bf∗b^*_f that is self-localized to the current error, and remains uniformly bounded for any source-target pair (even those with disjoint supports). We prove a general finite-sample oracle inequality on the excess risk, and use it to give an end-to-end guarantee for training with sparse ReLU networks. Experiments on controlled regression problems and shifted CIFAR-100 distillation show that TILT improves target-domain performance over source-only training, exact importance weighting, and relative density-ratio baselines, with a stable dependence on the regularization parameter.

Kakei Yamamoto, Martin J. Wainwright

Lab for Information and Decision Systems · Statistics and Data Science Center · EECS† +2

Unsupervised Domain AdaptationDistribution ShiftDomain AdaptationCovariate ShiftImportance Weighting
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May 13, 2026

Learning to Select Source Domains: Proxy-Rewarded Policy Optimization for Molecular OOD Generalization

Molecular property prediction under severe out-of-distribution (OOD) shifts remains challenging because conventional scaffold splits can retain local structural similarity between training and test molecules, while adaptation from heterogeneous source domains may cause negative transfer. We introduce SCOPE-Bench, a scaffold-cluster benchmark constructed by partitioning Bemis-Murcko scaffolds in an explicit physicochemical descriptor space, and POMA, a retrieve-compose-adapt framework for source-domain selection when target property labels are unavailable. POMA uses unlabeled target structures to retrieve labeled source scaffolds as proxy targets. A policy is trained from reductions in proxy-task mean absolute error after adaptation, and the selected source subset is then used for target adaptation with covariance alignment at the whole-molecule and BRICS-derived substructure levels. We evaluate HOMO, LUMO, and HOMO-LUMO gap prediction on QM9 using three 3D molecular backbones and 15 target-scaffold tasks. Relative to conventional scaffold splitting, prediction errors on SCOPE-Bench increase by up to 8.0-fold, with a mean increase of 5.9-fold. Relative to the supervised baseline under the strict OOD split, POMA reduces mean absolute error by up to 11.2%, with an average relative reduction of 6.2% over the nine backbone-property combinations. These results support adaptive source selection as a useful strategy for molecular prediction under strong structural distribution shifts.

Zhuohao Lin, Kun Li, Jiameng Chen +4

School of Computer Science Wuhan University Wuhan, China · College of Computer Science and Technology, Zhejiang University Hangzhou, China · School of Life Sciences and Technology, Tongji University Shanghai, 200092, China +1

Unsupervised Domain AdaptationDomain AdaptationDomain GeneralizationOOD GeneralizationMolecular Property Prediction
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May 13, 2026

On the Limits of Latent Reuse in Diffusion Models

Diffusion models are often trained in low-dimensional latent spaces, which are then reused for related but shifted datasets. In this work, we study when such latent reuse remains reliable under distribution shift. We consider a source-target setting in which both datasets are approximately low-dimensional but may lie near different subspaces. We show that freezing and reusing a source latent space induces a target-domain score error governed by two quantities: the principal-angle misalignment between the source and target subspaces, and the target ambient noise amplified by the diffusion time scale. Motivated by these limits, we further study mixed source-target training and characterize how the required shared latent dimension depends on the relative geometry of the two distributions. Our results provide theoretical guidance on when latent reuse is reliable and when learning a shared representation may be necessary.

Yifeng Yu, Lu Yu

Department of Mathematical Sciences, Tsinghua University · Department of Data Science, City University of Hong Kong

Latent Diffusion ModelsDomain AdaptationTransfer LearningDiffusion Models
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May 13, 2026

Mitigating Illumination-Induced Domain Shift in Night-Time Pedestrian Detection for Intelligent Vehicles using Annotation-Preserving Diffusion Augmentation

Night-time pedestrian detection remains challenging because labelled night-time data are limited and large illumination differences make daytime-only trained detectors unreliable. Latent diffusion models (LDMs) provide a powerful basis for image-to-image translation and cross-domain augmentation, but their effectiveness in safety-critical perception depends on whether detector-relevant objects and local semantic structure are preserved when translating between source and target domains. In this work, we present Contrastive-SDXL, a day-to-night augmentation framework for night-time pedestrian detection built on SDXL-Turbo and fine-tuned using Low-Rank Adaptation (LoRA). To preserve semantic correspondence between daytime inputs and translated night-time images, we introduce a patch-wise semantic contrastive loss guided by a pretrained DINOv2 encoder rather than generator encoder features. Multi-level DINOv2 self-attention maps enforce both local and global semantic consistency, while an object consistency loss explicitly encourages pedestrian preservation. Contrastive-SDXL produces realistic night-time images, achieving a Frechet Inception Distance (FID) of 22.5. Detectors trained with our synthetic images obtain a 6-7% reduction in miss rate compared with a daytime-only baseline, approaching the performance of detectors trained on real night-time data. These results demonstrate that consistency-driven diffusion augmentation can effectively support safety-critical night-time pedestrian detection.Specific

Franky George, Muhammad Khalid, Adil Khan +1

School of Digital and Physical Sciences, University of Hull, UK

Synthetic Data AugmentationDomain AdaptationDiffusion Model Fine-TuningUnpaired Image-to-Image TranslationObject Detection
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May 13, 2026

Multi-Modal Guided Multi-Source Domain Adaptation for Object Detection

General object detection (OD) struggles to detect objects in the target domain that differ from the training distribution. To address this, recent studies demonstrate that training from multiple source domains and explicitly processing them separately for multi-source domain adaptation (MSDA) outperforms blending them for unsupervised domain adaptation (UDA). However, existing MSDA methods learn domain-agnostic features from domain-specific RGB images while preserving domain-specific information from the domain-agnostic feature map. To address this, we propose MS-DePro: Multi-Source Detector with Depth and Prompt, composed of (1) depth-guided localization and (2) multi-modal guided prompt learning. We leverage domain-agnostic input modalities, namely depth maps and text, to encode domain-agnostic characteristics. Specifically, we utilize depth maps to generate domain-agnostic region proposals for localization and integrate multi-modal features to align learnable text embeddings for classification. MS-DePro achieves state-of-the-art performance on MSDA benchmarks, and comprehensive ablations demonstrate the effectiveness of our contributions. Our code is available on https://github.com/sejong-rcv/Multi-Modal-Guided-Multi-Source-Domain-Adaptation-for-Object-Detection.

Sangin Lee, Seokjun Kwon, Jeongmin Shin +2

Sejong University, Seoul, Republic of Korea · NAVER LABS, Seongnam, Republic of Korea · Artificial Intelligence and Robotics Institute (AIRI), Seoul, Republic of Korea

Multi-Source Domain AdaptationDomain AdaptationObject Detection
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May 12, 2026

PyroAdapt: Adapting Wildfire Prediction under Spatial Heterogeneity and Temporal Shift

Prediction of wildfire occurrence is a rare-event problem compounded by spatial heterogeneity and temporal distribution shift, as fire occurrences are vastly outnumbered by non-occurrences, and predictor--fire relationship varies across space and time. Models trained on historical fire data may perform poorly under new conditions and require adaptation to the target distribution before operational use. We propose PyroAdapt, a pretrain--retrieve--rank framework that adapts a pretrained model to target conditions by retrieving historical locations with similar conditions and fine-tuning on the retrievals through risk ranking. For spatial adaptation, we condition risk on terrain, ecoregion embeddings, and fire rates, accounting for spatial context in the retrieval, and learn risk ordering from same-day fire--nonfire cell pairs. We compare direct ranking, residual pairwise DPO (RDPO), and selective ranking through a unified score-gap formulation that characterizes their gradient allocation. Over California (discretized into 666 0.25x0.25 grid cells), these objectives raise daily average precision from 21.62% for continued focal fine-tuning to 24.35--24.57%, and Top5% recall from 18.70% to 22.11--22.79%. Under a fixed daily detection budget of 34 cells (5% area), selective ranking captures 344 additional positive cell--days. For fires in the top 5%/10%/20% of dry matter consumption, selective ranking raises recall by 39.70/28.18/20.50 percentage points, respectively. Furthermore, rolling evaluations over Yosemite show that the gains from ranking persist under temporal distribution shift. Together, these results show that PyroAdapt prioritizes the most fire-prone locations under a daily budget constraint and detects more extreme fire events.

Enyi Jiang, Wu Sun

Siebel School of Computing and Data Science, University of Illiois at Urbana-Champaign · Computer Science, Stanford University · Department of Global Ecology, Carnegie Institution for Science

Learning to RankDomain AdaptationWildfire ForecastingOOD GeneralizationRare-Event PredictionSpatiotemporal Forecasting
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May 11, 2026

Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights

Large Language Models(LLMs) are increasingly explored for cybersecurity applications such as vulnerability detection. In the domain of threat modelling, prior work has primarily evaluated a number of general-purpose Large Language Models under limited prompting settings. In this study, we extend the research area of structured threat modelling by systematically evaluating domain-adapted language models of different sizes to their general counterparts. We use both LLMs and Small Language Models(SLMs) that were domain adapted to telecommunications and cybersecuirty. For the structured threat modelling, we selected the widely used STRIDE approach and the application area is 5G security. We present a comprehensive empirical evaluation using 52 different configurations (on 8 different language models) to analyze the impact of 1) domain adaptation, 2) model scale, 3) decoding strategies (greedy vs. stochastic sampling), and 4) prompting technique on STRIDE threat classification. Our results show that domain-adapted models do not consistently outperform their general-purpose counterparts, and decoding strategies significantly affect model behavior and output validity. They also show that while larger models generally achieve higher performance, these gains are neither consistent nor sufficient for reliable threat modelling. These findings highlight fundamental limitations of current LLMs for structured threat modelling tasks and suggest that improvements require more than additional training data or model scaling, motivating the need for incorporating more task-specific reasoning and stronger grounding in security concepts. We present insights on invalid outputs encountered and present suggestions for prompting tailored specifically for STRIDE threat modelling.

Saba Pourhanifeh, AbdulAziz AbdulGhaffar, Ashraf Matrawy

Department of Systems and Computer Engineering Carleton University · School of Information Technology Carleton University

Domain AdaptationCybersecuritySmall Language ModelsLLMs for CybersecurityDomain Adaptation for LLMs
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May 11, 2026

Phoenix-VL 1.5 Medium Technical Report

We introduce Phoenix-VL 1.5 Medium, a 123B-parameter natively multimodal and multilingual foundation model, adapted to regional languages and the Singapore context. Developed as a sovereign AI asset, it demonstrates that deep domain adaptation can be achieved with minimal degradation to broad-spectrum intelligence and alignment. Continued pretraining was performed on Mistral Medium 3.1 using a localized 1-trillion tokens multimodal corpus, followed by a 250-billion tokens long-context extension phase. Subsequent post-training incorporated a novel human-annotated Singapore multimodal dataset and curated textual corpus on Singapore culture, knowledge, and legislation, totaling 22-billion tokens. An additional 5 billion tokens of model alignment was performed through Online Direct Preference Optimization. Phoenix-VL 1.5 Medium achieves state-of-the-art performance for its size on Singapore multimodal, legal, and government policy benchmarks while remaining globally competitive on general multimodal intelligence, multilingual, and STEM benchmarks. We also introduce a novel evaluation suite encompassing localized knowledge benchmarks and an institutionally aligned model behavior and safety framework. We report the data curation principles, training methodology, and highlight benchmark and inference performance.

Team Phoenix, :, Arka Ray +30

HTX × Mistral AI ∗

Multilingual Language ModelsDomain AdaptationMultimodal PretrainingMultimodal Large Language ModelsDomain Adaptation for LLMsMultimodal Foundation Models+1
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May 11, 2026

CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients

Motor imagery electroencephalography (MI-EEG) decoding offers a non-invasive route for post-stroke rehabilitation, but cross-patient use remains difficult because pathological neural reorganization changes task-related EEG dynamics, aperiodic activity, local excitability, cross-regional coordination, and trial-level brain-state context. This makes source-learned MI representations unreliable for unseen patients. To address this problem, we propose CFSPMNet, a cross-patient adaptation framework that models post-stroke MI-EEG as latent neural-state organization. CFSPMNet combines a Fourier-Reorganized State Mamba Network (FRSM) with Shared-Private Prototype Matching (SPPM). FRSM represents each trial as a latent physiological token sequence, reorganizes token states in the Fourier domain, and uses Fourier-derived trial context to guide Mamba state-space propagation. SPPM improves target pseudo-label updating by combining semantic confidence with shared-private physiological consistency, filtering confident but physiologically inconsistent target predictions. Leave-one-subject-out experiments on two stroke MI-EEG datasets show that CFSPMNet outperforms representative CNN-, Transformer-, Mamba-, and adaptation-based baselines, achieving average accuracies of 68.23% on XW-Stroke and 73.33% on 2019-Stroke, with gains of 5.63 and 8.25 percentage points over the strongest competitors. Ablation, sensitivity, feature-alignment, pseudo-label selection, and neurophysiological visualization analyses further support the roles of Fourier-domain token-state reorganization and calibrated pseudo-label updating. These results suggest that latent neural-state modeling can improve rehabilitation-oriented cross-patient BCI decoding. Code is available at https://github.com/wxk1224/CFSPMNet.

Xiangkai Wang, Yun Zhao, Dongyi He +5

School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China; Chongqing Key Laboratory of Embodied Intelligence Perception and Autonomous Learning for Humanoid Robots; Key Laboratory of Advanced Equipment Intelligence of the Chongqing Education Commission, Chongqing 401135, China · School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing 401131, China · Department of Language Science and Technology, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China +1

Cross-Subject EEG DecodingBrain-Computer InterfacesDomain AdaptationEEG DecodingFrequency-Domain Feature Learning
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May 10, 2026

cantnlp@DravidianLangTech 2026: organic domain adaptation improves multi-class hope speech detection in Tulu

This paper presents our systems and results for the Hope Speech Detection in Code-Mixed Tulu Language shared task at the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages (DravidianLangTech-2026). We trained an XLM-RoBERTa-based text classification system for detecting hope speech in code-mixed Tulu social media comments. We compared this organically adapted hope speech detection model with our baseline model. On the development set, the organically adapted model outperformed the baseline system. While our submitted systems performed more modestly on the official test set, these results suggest that further adapting XLM-RoBERTa on organically collected Tulu social media text containing code-mixed and mixed-script variation can improve hope speech detection in code-mixed Tulu.

Andrew Li, Sidney Wong

Lake Washington School District · University of Otago, New Zealand · Te P¯unaha Matatini, New Zealand

Domain AdaptationText ClassificationLow-Resource Language Processing
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May 10, 2026

SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecasting

Large vision models (LVMs) have recently proven to be surprisingly effective time series forecasters, simply by rendering temporal data as images. This success, how ever, rests on a largely unexamined premise: the rendered time series images are sufficiently close to natural images for knowledge in pre-trained models to transfer effectively. We argue that two gaps still remain, i.e., spectral and structural gaps, fundamentally limiting the potential of LVMs for time series forecasting. Spectrally, we systematically reveal that rendered time series images exhibit a markedly shallower power spectrum than the natural images LVMs are pre-trained to recognize. Structurally, reshaping 1D temporal sequences into 2D grids fabricates spurious spatial adjacencies while severing genuine temporal continuities, misleading the spatial inductive biases of pre-trained LVMs. To bridge these gaps, we propose SSDA, a dual-branch network that spectrally and structurally adapts to unlock the full potential of LVMs for time series forecasting. At the data level, a Spectral Magnitude Aligner (SMA) applies 2D FFT to selectively enhance the magnitude spectrum toward natural-image statistics while preserving phase. At the model level, a Structural-Guided Low-Rank Adaptation (SG-LoRA) injects position-aware temporal encodings into patch embeddings and adapts at tention via low-rank updates. The two branches are further adaptively fused to produce the final forecast. Extensive experiments on seven real-world benchmarks demonstrate that SSDA consistently outperforms strong LVM- and LLM-based baselines under both full-shot and few-shot settings. Code is publicly available at https://anonymous.4open.science/r/SSDA-8C5B.

Mingrui Zhang, Hanchen Yang, Wengen Li +4

Domain AdaptationTime Series ForecastingVision Foundation Model AdaptationLow-Rank Adaptation
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May 8, 2026

Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics

Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fast simulation provides large statistics at reduced realism. In this work, we systematically study transfer learning between fast-simulated and fully simulated datasets in a realistic LHC environment. We consider three representative tasks, signal-background classification, quark-gluon jet tagging, and missing transverse energy reconstruction, using dense neural networks, graph neural networks, and transformer-based architectures. Models are pretrained on ATLAS-like fast simulation and adapted to CMS-like fast simulation and to fully simulated ATLAS Open Data. Across all tasks, pretrained models consistently outperform independently trained baselines and require significantly less target-domain training data, typically reducing the needed statistics by about a factor of two. These results demonstrate that fast simulation can be used to learn robust, reusable representations and motivate publishing trained models as reusable scientific assets beyond large foundation models.

Matthias Schott, Lucie Flek

Institute of Physics, University of Bonn, Germany · Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Germany

Domain AdaptationHigh-Energy PhysicsCross-Domain Transfer LearningTransfer LearningData-Efficient Learning
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May 8, 2026

HPC-LLM: Practical Domain Adaptation and Retrieval-Augmented Generation for HPC Support

Modern scientific research increasingly depends on High-Performance Computing (HPC) infrastructures, yet many researchers face significant operational barriers when interacting with cluster environments, job schedulers, GPU resources, and parallel computing frameworks. General-purpose large language models (LLMs) provide useful coding assistance but often lack the domain-specific operational knowledge required for reliable HPC support. This paper presents HPC-LLM, a retrieval augmented and domain-adapted assistant designed to support common HPC workflows including Slurm scheduling, MPI execution, GPU utilization, filesystem management, and cluster troubleshooting. The proposed framework integrates automated documentation ingestion, dense retrieval, lightweight domain adaptation using QLoRA, and local inference within a modular orchestration pipeline. To support domain adaptation, we construct an HPC-oriented corpus from publicly available university HPC documentation, curated operational examples, and synthetic instruction-answer pairs generated from retrieved HPC content. The resulting dataset contains approximately 9,000 to 24,000 HPC-focused training examples spanning job scheduling, GPU computing, distributed training, storage systems, and cluster administration topics. We fine-tune Llama 3.1 8B using QLoRA and evaluate the resulting model against several open weight baselines under retrieval-augmented settings on JetStream2 infrastructure. Experimental results indicate that the adapted 8B model achieves performance comparable to substantially larger general-purpose models while operating under significantly lower GPU memory requirements and inference latency. In particular, the adapted model approaches the performance of Qwen 2.5 14B while requiring substantially fewer computational resources.

Nourin Shahin, Izzat Alsmadi

Texas A&M University San Antonio

Retrieval-Augmented GenerationDomain AdaptationHigh-Performance ComputingLLM Fine-TuningDomain Adaptation for LLMs
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May 7, 2026

Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference

Simulation-based inference (SBI) provides amortized Bayesian parameter inference from simulator-generated data without requiring explicit likelihood evaluation. Its reliability can degrade under model misspecification, where real-world observations are not well represented by the simulator used for training. Existing methods using unlabeled real-world data often align simulated and real-world data distributions, but marginal alignment alone does not directly preserve parameter-relevant information needed for posterior inference. We propose SPIN, an SBI framework with parameter-relevant information-preserving domain transfer using unlabeled, unpaired real-world observations. During training, SPIN translates labeled simulator observations toward the real-world domain and back to the simulator domain, using the original simulator labels to encourage domain transfer that preserves parameter-relevant mutual information. At test time, the learned real-to-simulator transport maps real-world observations into the simulator domain for posterior inference, without requiring real-world parameter labels or paired real--simulator observations. Across controlled synthetic and physical real-world benchmarks, SPIN improves real-world posterior inference, with the improvement becoming clearer as misspecification increases.

Joon Jang, Eunho Jeong, Kyu Sung Choi +1

Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea · Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea · Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea +3

Synthetic-to-Real Domain AdaptationSimulation-Based InferenceUnsupervised Domain AdaptationDomain AdaptationBayesian InferenceModel Misspecification
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May 7, 2026

Locality-aware Private Class Identification for Domain Adaptation with Extreme Label Shift

Domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain with different distributions. In real-world scenarios, the label spaces of the two domains often have an inclusion relationship, where some classes exist only in one domain but not the other. These non-overlapping classes are referred to as private classes. Identifying private class samples and mitigating their adverse effects is critical in the literature. Existing methods rely on the assumption that shifts in private classes are large enough to be considered outliers. However, the variance within a single shared class can be significantly larger than the difference between a private class and another shared class, challenging this assumption. Consequently, private classes substantially increase the difficulty of cross-domain classification. To address these issues, based on local transportation and metric properties of optimal transport (OT), a locality-aware private class identification approach is proposed in the form of a score function on transport mass. The effectiveness of the proposed approach is theoretically proven, highlighting the score function's strong ability to distinguish between shared and private class samples. Building on this, we introduce a reliable OT-based method (ReOT) for domain adaptation under severe label shift. ReOT minimizes classification risk while learning the separated cluster structure between the identified shared classes and private classes, effectively avoiding mismatch between shared-private sample pairs, thus ensuring that important knowledge is reliably transported intra-class to mitigate class-conditional discrepancy. Furthermore, a generalization upper bound of the target risk is provided for extreme label shift scenarios, which can be minimized by ReOT. Extensive experiments on benchmarks validate the effectiveness of ReOT.

Chuan-Xian Ren, Cheng-Jun Guo, Hong Yan

School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China · Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China

Label ShiftUnsupervised Domain AdaptationDomain AdaptationOptimal Transport
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May 5, 2026

Domain-Adaptive Arrhythmia Classification Using a Hybrid Transformer on Wearable Heart Signals

Cardiovascular disease remains the leading cause of death globally, underscoring the need for effective, accessible monitoring solutions, particularly through wearable devices that enable continuous, real-time tracking of heart rhythms in home settings. However, deploying deep learning models trained on clinical electrocardiogram (ECG) datasets to wearable devices remains challenging, as differences in recording equipment, signal quality, and patient populations introduce domain shifts that degrade model performance. We propose a hybrid transformer model that processes continuous ECG signals alongside seven heart rate variability (HRV) features, where the raw signal path captures beat-level morphological patterns and the HRV path encodes rhythm regularity statistics, allowing the model to jointly leverage complementary information from both representations. To enhance the model's ability to generalize across domains, we employ representation learning techniques, including Maximum Mean Discrepancy (MMD), a non-parametric kernel-based metric that quantifies the distance between feature distributions of different domains, to align feature distributions between source and target domains, addressing the challenge of domain shifts between public datasets and wearable device data. By leveraging five public ECG datasets for training, the model learns robust, generalized representations that mitigate domain-specific biases. When tested on wearable device data with an unseen domain, the model achieved an F1-macro 95% and balanced accuracy of 96.15%. These results demonstrate minimal performance degradation, with only a 2% drop in F1-macro compared to seen-domain evaluation, highlighting the model's generalization capabilities and its potential for reliable, real-time heart monitoring applications in home and ambulatory settings.

Maedeh H. Toosi, Siamak Mohammadi

School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

TransformerDomain AdaptationElectrocardiogram ClassificationHybrid Transformer ArchitecturesWearable Health Monitoring
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May 5, 2026

DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation

Decentralized multi-source domain adaptation seeks to transfer knowledge from multiple heterogeneous and related source domains to an unlabeled target domain in a decentralized setting. We address this challenge through a fully decentralized federated approach, DeFed-GMM-DaDiL, an extension of the GMM-Dataset Dictionary Learning (DaDiL) framework. Each client models its dataset as a Gaussian Mixture Model (GMM), and the federation jointly approximates them via labeled Wasserstein barycenters of shared, learnable GMM atoms. This design enables adaptation without a central server while preserving clients' privacy. We empirically study the stability of the learned representations in scenarios where the target domain has missing classes. Empirical results demonstrate that DeFed-GMM-DaDiL maintains stable and consistent shared representations across clients, effectively reconstructs missing classes, and achieves competitive performance on multi-source domain adaptation benchmarks.

Rebecca Clain, Eduardo Fernandes Montesuma, Fred Ngole Mboula

Universit´e Paris-Saclay, CEA, List, F-91120 Palaiseau, France · Sigma Nova

Multi-Source Domain AdaptationDomain AdaptationWasserstein BarycentersFederated Learning
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May 5, 2026

Text-Guided Multi-Scale Frequency Representation Adaptation

Parameter-efficient fine-tuning methods introduce a small number of training parameters, enabling pre-trained models to adapt rapidly to new data distributions. While these methods have shown promising results, they exhibit notable limitations. First, most existing methods operate in the signal space domain, which results in substantial information redundancy. Second, most existing methods utilize fixed prompts or adaptation layers, failing to fully account for the multi-scale characteristics of signals. To address these challenges, we propose the Multi-Scale Frequency Adapter (FreqAdapter), which integrates textual information and performs multi-scale fine-tuning of signals in the frequency domain. Additionally, we introduce a multi-scale adaptation strategy to optimize receptive fields across different frequency ranges, further enhancing the model's representational capacity. Extensive experiments on multimodal models, including CLIP and LLaVA, demonstrate that FreqAdapter significantly improves both performance and efficiency. FreqAdapter improves performance with minimal cost and fast convergence within one epoch. Code is available at https://github.com/Kelvin-ywc/FreqAdapter.

Weicai Yan, Xinhua Ma, Wang Lin +1

Zhejiang University · 2Nanyang Technological University

Domain AdaptationVLM AdaptationParameter-Efficient Fine-Tuning
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May 5, 2026

SoDa2: Single-Stage Open-Set Domain Adaptation via Decoupled Alignment for Cross-Scene Hyperspectral Image Classification

Cross-scene hyperspectral image (HSI) classification stands as a fundamental research topic in remote sensing, with extensive applications spanning various fields. Owing to the inclusion of unknown categories in the target domain and the existence of domain shift across different scenes, open-set domain adaptation techniques are commonly employed to address cross-scene HSI classification. However, existing open-set cross-scene HSI classification methods still face two critical challenges: (1) domain shift issues arising from the direct alignment of mixed spectral-spatial features; (2) high computational costs caused by two-stage training strategies. To address these issues, this paper proposes a single-stage open-set domain adaptation method with decoupled alignment (SoDa2^2) for cross-scene HSI classification. A contribution-aware dual-modality feature extraction is customized to disentangle the characteristics from spectral sequence signals and spatial details, selectively and adaptively enhancing discriminative features. The decoupled alignment module minimizes the Maximum Mean Discrepancy to independently reduce the spectral discrepancy and the spatial discrepancy between the source and target domains, extracting more fine-grained domain-invariant features. A cost-effective single-stage dual-branch framework is designed to learn MMD-constrainted aligned features and constraint-free intrinsic features for adaptive distinction between known and unknown classes. This framework employs a Gaussian Mixture Model to model the squared cosine similarity distribution between the two feature types, enabling open-set recognition without prior knowledge of unknown classes. Extensive experiments on three groups of HSI datasets demonstrate that SoDa2^2 outperforms state-of-the-art methods, achieving superior classification accuracy and model transferability for open-set cross-scene tasks.

Yiwen Liu, Minghua Wang, Jing Yao +2

Institute of Robotics and Automatic Information System (IRAIS), College of Artificial Intelligence, and the Tianjin Key Laboratory of Intelligent Robotics (tjKLIR), Nankai University, Tianjin 300071, China · Aerospace Information Research Institute, Chinese Academy of Sciences, 100094 Beijing, China · Institute of Methodologies for Environmental Analysis (CNR-IMAA), National Research Council, 85050 Tito, Italy

Unsupervised Domain AdaptationDomain AdaptationHyperspectral Image Classification
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May 4, 2026

Reliability-Oriented Multilingual Orthopedic Diagnosis: A Domain-Adaptive Modeling and a Conceptual Validation Framework

Large Language Models (LLMs) are increasingly proposed for clinical decision support including multilingual diagnosis in low-resource settings. However, their reliability, calibration and safety characteristics remain insufficiently understood for structured, high-risk tasks. We present a system-level analysis of multilingual orthopedic diagnosis from free-text clinical notes in English, Hindi and Punjabi. We evaluate three modeling regimes: (i) task-aligned multilingual transformer encoders, (ii) a task-fine-tuned baseline (DistilBERT), and (iii) a domain-adaptive architecture tailored to orthopedic text (IndicBERT-HPA). These models are compared with zero-shot, instruction-tuned LLMs to assess suitability for structured diagnostic classification. Results indicate that while LLMs exhibit strong linguistic fluency, they show unstable calibration and reduced reliability under structured multilingual conditions, particularly in low-resource languages. These findings are specific to zero-shot evaluation and do not imply limitations of fine-tuned models. Domain-adaptive specialization substantially improves cross-lingual discrimination and confidence behavior. IndicBERT-HPA, with language-specific orthopedic adapter heads achieves consistently strong performance across six diagnostic categories and more predictable deployment characteristics than task-only adaptation. Building on these observations, we outline a conceptual deterministic agent-based validation framework for future implementation, formalizing evidence checks, language-sensitive validation and conservative human-in-the-loop gating. Reliable multilingual clinical decision support requires specialized architecture, explicit reliability analysis, and structured validation for safety-critical systems.

Danish Ali, Li Xiaojian, Sundas Iqbal +1

School of Computer Science, Wuhan University, China · School of Software, Nanjing University of Information Science and Technology (NUIST), China · Department of Orthopedic, Bahawal Victoria Hospital, Pakistan

Multilingual Language Model EvaluationHealthcareDomain AdaptationMedical DiagnosisLanguage Model CalibrationClinical Language Model Evaluation+1
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Apr 30, 2026

AIDA-ReID: Adaptive Intermediate Domain Adaptation for Generalizable and Source-Free Person Re-Identification

Person re-identification (Re-ID) aims to match images of the same individual across non-overlapping camera views and remains challenging due to domain shifts caused by variations in illumination, background, camera characteristics, and population distributions. Although supervised models perform well under matched training and testing conditions, their performance degrades significantly when deployed in unseen environments. Existing intermediate domain approaches such as IDM and IDM++ alleviate this gap by constructing bridge feature distributions between domains; however, they rely on fixed mixing strategies and joint source-target access, limiting their applicability to multi-source and source-free settings. To address these limitations, this paper proposes Adaptive Intermediate Domain Adaptation (AIDA), also referred to as Source-Free Multi-Source Intermediate Domain Adaptation (SF-MIDA). The proposed framework treats intermediate-domain learning as a dynamically regulated process, where feature mixing and regularization strength are adaptively controlled using feedback signals derived from model uncertainty and training stability. A multi-source intermediate domain generator synthesizes diverse intermediate representations, while a pseudo-mirror regularization strategy preserves identity consistency under domain perturbations. Extensive experiments across domain generalization and source-free settings demonstrate the effectiveness of the proposed framework.

Sundas Iqbal, Qing Tian, Danish Ali +2

School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China · Wuxi Institute of Technology, Nanjing University of Information Science and Technology, Wuxi 214000, China · School of Computer Science, Wuhan University, Wuhan 430070, China +2

Multi-Source Domain AdaptationSource-Free Domain AdaptationDomain AdaptationCross-Domain GeneralizationPerson Re-Identification
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Apr 29, 2026

Translating Under Pressure: Domain-Aware LLMs for Crisis Communication

Timely and reliable multilingual communication is critical during natural and human-induced disasters, but developing effective solutions for crisis communication is limited by the scarcity of curated parallel data. We propose a domain-adaptive pipeline that expands a small reference corpus, by retrieving and filtering data from general corpora. We use the resulting dataset to fine-tune a small language model for crisis-domain translation and then apply preference optimization to bias outputs toward CEFR A2-level English. Automatic and human evaluation shows that this approach improves readability, while maintaining strong adequacy. Our results indicate that simplified English, combined with domain adaptation, can function as a practical lingua franca for emergency communication when full multilingual coverage is not feasible.

Antonio Castaldo, Maria Carmen Staiano, Johanna Monti +2

University of Pisa · University of Naples “L’Orientale” · University of Macerata +1

Domain AdaptationDisaster ResponseLow-Resource MTText SimplificationMachine Translation
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Apr 28, 2026

Medoid Prototype Alignment for Cross-Plant Unknown Attack Detection in Industrial Control Systems

Deploying an intrusion detector trained in one industrial plant to another remains difficult because Industrial Control System (ICS) traffic is highly site-dependent, labels are scarce, and unseen attacks often appear after deployment. To address this challenge, this paper introduces a medoid prototype alignment framework for cross-plant unknown attack detection. Instead of aligning all source and target samples directly, the method first compresses heterogeneous traffic into a comparable representation space and then extracts robust medoid prototypes that summarize local operational structure in each domain. A prototype-calibrated transfer objective is further designed to align target prototypes with source prototypes while preserving source-domain discrimination and encouraging confident target predictions. This strategy reduces noisy cross-domain matching and improves transfer stability under heterogeneous industrial conditions. Experiments conducted on natural gas and water storage control systems show that the proposed method achieves the best average performance among all compared models, reaching an average accuracy of 0.843 and an average F1-score of 0.838 across four unknown-attack transfer tasks. The analysis also shows clear transfer asymmetry between source-target directions and confirms that prototype guidance is especially helpful on challenging reverse-transfer settings. These findings suggest that medoid prototype alignment is a practical solution for robust industrial intrusion detection under domain shift.

Luyao Wang

University of Malaya Malaysia

Domain AdaptationNetwork Intrusion DetectionCross-Domain Transfer LearningIndustrial Control System Security
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Apr 28, 2026

UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval

Unsupervised domain adaptation generalizes neural retrievers to an unseen domain by generating pseudo queries on target domain documents. The quality and efficiency of this adaptation critically depend on which documents are selected for pseudo query generation. The existing document sampling method focuses on diversity but fails to capture model uncertainty. In contrast, we propose Uncertainty-based Iterative Document Sampling (UnIte) addressing these limitations by (1) filtering documents with high aleatoric uncertainty and (2) prioritizing those with high epistemic uncertainty, maximizing the learning utility of the current model. We conducted extensive experiments on a large corpus of BEIR with small and large models, showing significant gains of +2.45 and +3.49 nDCG@10 with a smaller training sample size, 4k on average.

Jongyoon Kim, Minseong Hwang, Seung-won Hwang

Interdisciplinary Program in Artificial Intelligence, Seoul National University

Unsupervised Domain AdaptationDomain AdaptationInformation RetrievalUncertainty Sampling
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Apr 27, 2026

When To Adapt? Adapting the Model or Data in Federated Medical Imaging

Federated learning enables collaborative model training across medical institutions without sharing raw data, but its performance is often limited by domain heterogeneity across clients. Existing approaches to address this challenge fall into two main paradigms: model-side personalization, which adapts model parameters to each client, and data-side harmonization, which reduces inter-client variation at the input level. Despite their widespread use, these strategies have not been systematically compared. In this work, we conduct a comprehensive study across six medical imaging settings-colon polyp, skin lesion, and breast tumor segmentation, and tuberculosis CXR, brain tumor, and breast tumor classification-covering diverse types of domain shift. We evaluate a broad set of state-of-the-art harmonization and personalization methods under a unified framework. Our results reveal a conditional trade-off driven by the nature of heterogeneity: harmonization is more effective when variation is primarily appearance-based (e.g., CXR classification), while personalization performs better when differences are structural (e.g., colon polyp segmentation). When inter-client variation is limited, both strategies perform similarly. These findings demonstrate that the effectiveness of adaptation in federated medical imaging depends on the type and magnitude of domain shift rather than the strategy alone. We provide practical guidelines for selecting between harmonization and personalization and highlight directions for future hybrid approaches that combine both paradigms. Code is available at https://github.com/ChamaniS/WhenToAdapt.

Chamani Shiranthika, Parvaneh Saeedi

School of Engineering Science, Simon Fraser University, Burnaby, BC, V5A 1S6

Domain AdaptationMedical Image ClassificationMedical Image SegmentationFederated LearningHeterogeneous Federated LearningMedical Image Harmonization
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Apr 26, 2026

Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Model

Adaptation of blackbox generative models has been widely studied recently through the exploration of several methods including generator fine-tuning, latent space searches, leveraging singular value decomposition, and so on. However, adapting large-scale generative AI tools to specific use cases continues to be challenging, as many of these industry-grade models are not made widely available. The traditional approach of fine-tuning certain layers of a generative network is not feasible due to the expense of storing and fine-tuning generative models, as well as the restricted access to weights and gradients. Recognizing these challenges, we propose a novel end-to-end pipeline aimed at domain adaptation by leveraging geometry-preserving loss functions in conjunction to pre-trained generative adversarial networks (GANs). Our method rethinks the problem of adaptation by re-contextualizing the role of GAN inversion in obtaining accurate latent space representations. Extending the ability of existing state-of-the-art inverters, we preserve pair-wise distances between tangent spaces to successfully train a latent generative model to produce samples from the target distribution. We evaluate our proposed pipeline on StyleGANs with real distribution shifts and demonstrate that the introduction of the geometry preserving loss function lends to improved adaptation of generative models compared to other traditional loss functions.

Sinjini Mitra, Constantine Kyriakakis, Shenyuan Liang +2

1Geometric Media Lab, Arizona State University, Tempe 85281, USA. · Department of Statistics, Florida State University, Tallahassee 32306, USA.

Domain AdaptationLatent Space OptimizationGenerative Adversarial Networks
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