Rapid Adaptation

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10 papers in the last four weeks, up 43% on the four weeks before. 0.1% of all new papers.

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Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates
May 26, 2026Kei Takemura, Ryuta Matsuno, Keita SakumaLearning-Augmented Algorithms Batch
NEC Corporation Tokyo, Japan
Separate Aggregation of Split Network for Personalized Federated Learning
May 26, 2026Yunseok Kang, Jaeyoung SongFederated Learning Heterogeneity
Department of Electronics Engineering, Pusan National University, Republic of Korea
Modeling Dynamic Mixtures of Time-Delay Systems from Streaming Time Series
May 25, 2026Ren Fujiwara, Yasuko Matsubara, Yasushi SakuraiMixture Models Rapid Adaptation
SANKEN, The University of Osaka, Japan
How to Mitigate the Distribution Shift Problem in Robotics Control: A Robust and Adaptive Approach Based on Offline to Online Imitation Learning
May 25, 2026Hyung-Suk Yoon, Seung-Woo SeoImitation Learning Distribution Shifts
Department of Electronic and Computer Engineering, Seoul National University, Seoul, South Korea.
Trust-Aware Joint Feature-Prediction Discrepancy for Robust Domain Adaptation
May 24, 2026Xi Ding, Lei Wang, Syuan-Hao Li +1Domain Adaptation Mismatch
School of Engineering and Built Environment, Griffith University, Australia
Turning Adaptation into Assets: Cross-Domain Bridging for Online Vision-Language Navigation
May 22, 2026Zixuan Hu, Xuantuo Huang, Yancheng Li +3Vision-Language Model Adaptation Vision-Language Navigation
School of Computer Science, Peking University, Beijing, China · School of Electronics, Peking University, Beijing, China · Peng Cheng Laboratory, Shenzhen, China
Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems
May 21, 2026Jiaqi Yan, Ankush Chakrabarty, Niklas Schmid +2Meta-Learning System Identification
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, P. R. China · Mitsubishi Electric Research Laboratories, Cambridge, MA 02139, USA · Automatic Control Laboratory, ETH Zurich, Switzerland +1
Return of Frustratingly Easy Unsupervised Video Domain Adaptation
May 19, 2026Pengfei Wei, Yiqun Sun, Zhiqiang Xu +2Domain Adaptation Open-Vocabulary Action Recognition
1Magellan Technology Research Institute (MTRI), Japan · 2Mohamed bin Zayed University of Artificial Intelligence, UAE · 3Nanyang Technological University, Singapore.
Towards Principled Test-Time Adaptation for Time Series Forecasting
May 17, 2026Haochun Wang, Ruichen Xu, Georgios Kementzidis +3Test-Time Adaptation Time Series Forecasting
Stony Brook University
Expandable, Compressible, Mineable: Open-World Thermal Image Restoration
May 16, 2026Pu Li, Huafeng Li, Yafei Zhang +3Restoration Degradation
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan, China · School of Mathematics and Statistics, Yunnan University, Yunnan, China · Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, China
Strategic Over-Parameterization for Generalizable Low-Rank Adaptation
May 15, 2026Jing Gao, Zhong-Yi Lu, Pan Zhang +1Training-Side Stage-Aware Low-Rank Adaptation Low-Rank Adaptation Adapters
School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China · School of Physical Sciences, University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China · CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China +1
ALSO: Adversarial Online Strategy Optimization for Social Agents
May 15, 2026Xiang Li, Liping Yi, Mingze Kong +3Rapid Adaptation Persona Consistency
School of Artificial Intelligence, Tianjin University, Tianjin, China · The Chinese University of Hong Kong, Shenzhen, China · East China Normal University, Shanghai, China.
EMA: Efficient Model Adaptation for Learning-based Systems
May 13, 2026Daiyang Yu, Xinyu Chen, Yihan Zhang +3Parameter-Efficient Adaptation Rapid Adaptation
University of Illinois Urbana-Champaign USA · The Hong Kong University of Science and Technology Hong Kong, China
Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
May 12, 2026Roozbeh Razavi-Far, Mohammad Meymani, Erfan Mahmoudinia +6Quantum Machine Learning Adversarial Training
Faculty of Computer Science, University of New Brunswick, Fredericton, Canada. · Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran. · Faculty of Mathematical and Computer Science, Kharazmi University, Tehran, Iran. +4
LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection
May 12, 2026Lanxin Zhao, Bamdev Mishra, Pratik Jawanpuria +4Parameter-Efficient Fine-Tuning Methods Model Fine-Tuning
The University of Sydney, Australia. · Microsoft India. · Indian Institute of Technology Bombay, India. +1
BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation
May 9, 2026Antong Zhang, Han Qi, Heng YangRobot Policies Visuomotor Policy
Department of Computer Science, Brown University · School of Engineering and Applied Sciences, Harvard University
Flexi-LoRA with Input-Adaptive Ranks: Efficient Finetuning for Speech and Reasoning Tasks
May 3, 2026Zongqian Li, Yixuan Su, Han Zhou +2Rapid Adaptation Reasoning Skills
University of Cambridge
Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning
May 1, 2026Zhi-Quan Feng, Ying-Jia Lin, Hung-Yu KaoSubspace Rapid Adaptation
Department of Computer Science and Information Engineering, National Cheng Kung University · Department of Artificial Intelligence, Chang Gung University · Department of Computer Science, National Tsing Hua University
SciHorizon-DataEVA: An Agentic System for AI-Readiness Evaluation of Heterogeneous Scientific Data
Apr 29, 2026Dianyu Liu, Chuan Qin, Xi Chen +6Artificial Intelligence Evaluation Scientific Discovery
Computer Network Information Center, Chinese Academy of Sciences Beijing, China
Towards Unified Multi-task EEG Analysis with Low-Rank Adaptation
Apr 28, 2026Sicheng Dai, Kai Chen, Hongwang Xiao +2Electroencephalography Brain-Computer Interface
Institute of Automation, Chinese Academy of Sciences · School of Artifcial Intelligence, University of Chinese Academy of Sciences · State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology +2
CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning
Apr 25, 2026Marcel Hedman, Kale-ab Abebe Tessera, Juan Claude Formanek +5Multi-Agent Reinforcement Learning Diffusion Policies
Inephany Ltd · Department of Statistics, University of Oxford · The University of Edinburgh +2
Proteus: Shapeshifting Desktop Visualizations for Mobile via Multi-level Intelligent Adaptation
Apr 25, 2026Can Liu, Sizhe Cheng, Feng Liang +4Visual Analytics Visualization
Nanyang Technological University Singapore, Singapore · Providence Health & Services Renton, Washington, USA · The Hong Kong Polytechnic University Hong Kong, China
Distributional Value Estimation Without Target Networks for Robust Quality-Diversity
Apr 22, 2026Behrad Koohy, Jamie BayneDistributional Reinforcement Learning Soft Actor-Critic
Luffy.AI Culham, Oxfordshire, United Kingdom
A Benchmark Study of Segmentation Models and Adaptation Strategies for Landslide Detection from Satellite Imagery
Apr 17, 2026Md Kowsher, Weiwei Zhan, Chen ChenLandslides Remote Sensing Image Segmentation
University of Central Florida
Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning
Apr 16, 2026Lute Lillo, Nick CheneyReplay-Based Continual Learning Offline Reinforcement Learning
Department of Computer Science University of Vermont USA
Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits
Apr 16, 2026Emre Özyıldırım, Barış Yaycı, Umut Eren Akturk +1Power Allocation Rapid Adaptation
Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Türkiye
Receptogenesis in a Vascularized Robotic Embodiment
Mar 10, 2026Kadri-Ann Pankratov, Leonid Zinatullin, Hans Priks +6Fluid Controls Catheters
Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia · Institute of Technology, Via Morego, 30, 16163 Genova, Italy
Power Couple? AI Growth and Renewable Energy Investment
Mar 8, 2026Luyi Gui, Tinglong DaiArtificial Intelligence Lifecycle Carbon Emissions
Paul Merage School of Business, University of California, Irvine. Irvine, California 92697 · Carey Business School, Johns Hopkins University, Baltimore, Maryland 21202; Data Science and AI Institute, Johns Hopkins University, Baltimore, Maryland 21218
VertiAdaptor: Online Kinodynamics Adaptation for Vertically Challenging Terrain
Mar 6, 2026Tong Xu, Chenhui Pan, Aniket Datar +1Terrain Vehicle Dynamics
Department of Computer Science, George Mason University
CeRA: Breaking the Linear Ceiling of Low-Rank Adaptation with Non-linearity Retained at Inference
Feb 26, 2026Hung-Hsuan ChenLow-Rank Adaptation Framework Rapid Adaptation
Computer Science and Information Engineering National Central University Taoyuan, Taiwan
MAS-on-the-Fly: In-Context Structural Adaptation of LLM-Based Multi-Agent Systems
Feb 14, 2026Guangyi Liu, Haojun Lin, Huan Zeng +2Model-Based Multi-Agent Systems Rapid Adaptation
Department of Electronic Engineering, Tsinghua University · Ant Group
Online Regime-aware Calibration for Black-box Social Simulators via Posterior-assisted Evolutionary Dynamic Optimization
Jan 27, 2026Peng Yang, Zhenhua Yang, Boquan Jiang +3Black-Box Optimization Robust Optimization
Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen 518055, China · Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China · Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, China +2
Exploiting Stability-Plasticity Asymmetry in Pretrained Detectors for Incremental Object Detection
Apr 14, 2025Songze Li, Qixing Xu, Tonghua Su +3Plasticity Pretraining
Harbin Institute of Technology, Harbin, China · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China · Chongqing Research Institute of HIT, Chongqing, China +2
Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms
Jul 21, 2024Sheila Schoepp, Mehran Taghian, Shotaro Miwa +3Fault Tolerance Rapid Adaptation
Department of Computing Science, University of Alberta, 8900 114 St NW, Edmonton, Alberta, T6G 2S4, Canada · Advanced Technology R&D Center, Mitsubishi Electric Corporation, 8-1-1,Tsukaguchi-honmachi, Amagasaki-shi, Hyogo, 661-8661, Japan · Information Technology R&D Center, Mitsubishi Electric Corporation, 5-1-1, Ofuna, Kamakura-shi, Kanagawa, 247-8501, Japan +1
SPECTRA: Band-Routed Embedding and Stage-Wise LoRA for Cross-Sensor Fine-Tuning of Geospatial Foundation Models
Date pendingXingyan Li, Jordan A. Caraballo-Vega, Jie Gong +2Geospatial Foundation Models Parameter-Efficient Fine-Tuning Methods
University of Maryland, Baltimore County Baltimore, Maryland, USA · NASA Goddard Space Flight Center Greenbelt, Maryland, USA

May 26, 2026, cs.LGJ/K move · Enter open · S save

Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates

Kei Takemura, Ryuta Matsuno, Keita Sakuma

NEC Corporation Tokyo, Japan

Learning-Augmented Algorithms Batch Mirror Descent Rapid Adaptation Tuning Model Selection

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Maintaining predictive accuracy in non-stationary environments requires online model selection to adapt autonomously to unknown distribution shifts. However, existing tuning-free algorithms face a fundamental trade-off between robustness and agility. Specifically, to ensure dynamic regret bounds, they must restrict learning rates to small constants (e.g.,

O(1)

). This restriction inevitably causes significant adaptation lag during abrupt changes. To resolve this, we propose a novel optimistic online mirror descent that utilizes safeguarded large learning rates up to

Θ(T)

, where

T

is the number of rounds. Our key technical contribution is a post-hoc penalty mechanism that dynamically monitors unstable updates and excludes learning rates incurring excessive regret, eliminating the need for restrictive a priori constraints. We show that the cumulative penalty remains

O(\log T)

, allowing our algorithm to match near-optimal worst-case guarantees while achieving superior rates in benign cases. Empirical evaluations on three synthetic and eleven diverse real-world datasets demonstrate that our approach reduces the adaptation lag from hundreds of rounds to a few rounds, consistently outperforming tuning-free baselines.

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