Adaptive Learning

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7 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

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Sherpa: Teaching LLMs to Teach Adaptively
Oct 6, 2026Weixian Xu, Yanzhe Zhang, Zora Zhiruo Wang +2RL for Language Models Intelligent Tutoring Systems
Stanford University · Georgia Tech · Carnegie Mellon University
Robust and Learned Online Matching in Growing Trees
Sep 30, 2026Marek Gałązka, Hanna WdowickaAdaptive Learning
Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poznań, Poland · Department of Statistics, Poznań University of Economics and Business, Poznań, Poland
Shared Phase and Retention Control for Efficient Adaptive Spectral Recurrence
Sep 30, 2026Wentao Wang, Hengyu Zhong, Yunhan Jiang +2Recurrent Neural Networks Adaptive Learning
Peking University · GigaAI
AdaTutoRank: Learning to Rerank Document Sets via Adaptive Tutoring Optimization for RAG and Deep Research
Sep 26, 2026Kailin Jiang, Lei Liu, Jian Xi +6Retrieval-Augmented Generation Learning to Rank
University of Science and Technology of China · Yuanbao Team, Tencent
Online Task Adaptation via Self-Organisation
Sep 24, 2026Krsto ProrokovićNeural Cellular Automata Memory-Augmented Neural Networks
Independent Researcher Montenegro
Evaluating Feedback Focus and Pedagogical Adaptivity in LLM-Generated Feedback on Student Writing
Sep 23, 2026Norah Almousa, Shayan Peyghambari Oskoui, Raquel Coelho +3LLM Evaluation LLM Alignment
University of Pittsburgh
SCCM : Stream Cruise Control Method for Automated Drift Detection and Adaptation
Sep 10, 2026Mohammad Abu-Shaira, Weishi ShiConcept Drift Adaptive Learning
Department of Computer Science and Engineering, University of North Texas, 1155 Union Circle #311366, Denton, 76203, Texas, USA
CircuTutor: Transforming Static Circuit Problems into Intelligent and Dynamic Tutoring
Sep 8, 2026Ziyu Luo, Xiaorui Ma, Lin Chen +1Intelligent Tutoring Systems Adaptive Learning
Beijing Technology and Business University Beijing, China
Structure and Implementation of New Practical English Textbooks Driven by Artificial Intelligence
Sep 2, 2026Ya Wang, Lei Zhang, Xueguang Yang +1Intelligent Tutoring Systems AI in Education
Air Force Logistics Academy, Xuzhou, 221000,China
Flawed in Nature, Perfect through Evolution
Aug 31, 2026J. M. Diederik KruijssenEvolution Strategies Adaptive Learning
Allora Foundation
Reproducible macroscopic dynamics in a closed-loop human-AI learning system
Aug 31, 2026Minlin Wu, Xu Fang, Yicheng Zhang +2Human-in-the-Loop AI Human-AI Interaction
Tianli Qiming AI Research Institute, Sichuan Qiming Daren Technology Co., Ltd., Chengdu, China · Swiss AI Laboratories, Blonay, Switzerland
Atom Learning Model (ALM): how a real classroom got tokenised
Aug 21, 2026Philipp BogdanAI in Education User Modeling
Department of Computing Imperial College London
Defensive Boosting for Online Probabilistic Forecasting
Aug 13, 2026Georgy Noarov, Aaron RothProbabilistic Forecasting Weak-to-Strong Generalization
Concept Drift Detection and Adaptive Retraining of Malware Classification Models
Aug 13, 2026Christofer Washington Berruz Chungata, Martin Jurecek, Katerina Potika +2Concept Drift Malware Classification
Department of Computer Science, San Jose State University · Faculty of Information Technology, Czech Technical University in Prague
Closing the loop in learning with missing data
Aug 10, 2026Dimitrios Pylorof, Humberto E. GarciaLearning with Missing Data Adaptive Learning
U.S. Department of Energy Idaho National Laboratory
Towards Sustainable Learning in Online Education: A Reinforcement Learning Approach
Aug 2, 2026Chaofan Zhai, Yicheng Song, Ravi Bapna +1Educational Technology AI in Education
Carlson School of Management, University of Minnesota · MaiMemo Inc.
Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret
Jul 29, 2026Atharva Navsalkar, Hongyu Zhou, Vasileios TzoumasDynamical Systems Regime-Switching Dynamical Systems
Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109 USA
AdaKP: Online Adaptive Knowledge-Point Selection for Reasoning-Oriented Reinforcement Learning
Jul 23, 2026Zibin Meng, Zhenyu Zhao, Chunqiang RunKnowledge Augmentation for Language Models RL for Language Model Reasoning
The Hong Kong University of Science and Technology · University of the Chinese Academy of Sciences
ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
Jul 9, 2026Ashit Kumar Subudhi, Bhargav Chirumamilla, Shubham Vaishnav +5Concept Drift Open Radio Access Network
Indian Institute of Technology Dharwad, Karnataka, India · Johns Hopkins University, USA · Stockholm University, Sweden +2
Spatio-Temporal Retrieval-based Priors for Adaptive Computational Teaching in Driving
Jun 23, 2026Deepak Edakkattil Gopinath, Xiongyi Cui, Jonathan DeCastro +2Autonomous Driving Imitation Learning
Toyota Research Institute, Cambridge, MA, USA
Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning
Jun 22, 2026Samuel Yen-Chi Chen, Yifeng Peng, Kuo-Chung Peng +8Time Series Forecasting Quantum Machine Learning
Wells Fargo · Stevens Institute of Technology · National Taiwan University +3
Counterfactual learning of new adaptive instructional policies using logged data
Jun 22, 2026Samuel Girard, Sein Minn, Amel Bouzeghoub +1Intelligent Tutoring Systems Contextual Bandits
SODA · Soda Team, Inria Saclay, Palaiseau, France · Pix, Paris, France +4
SCR-Guided Difficulty-Aware Optimization for Infrared Small Target Detection
Jun 17, 2026Yunus Sevim, Behçet Uğur TöreyinInfrared Small Target Detection Adaptive Loss Weighting
Aselsan · Ankara, Turkey · Istanbul Technical University +1
Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction
Jun 16, 2026Yongkyung Oh, Lynn Talton, Alex BuiAI in Education Generative AI in Education
University of California, Los Angeles (UCLA), Los Angeles, CA, USA
Damage Adaptation in Seconds for Architected Materials
Jun 16, 2026James Avtges, Jake Ketchum, Helena Young +3Adaptive Learning Soft Robotics
Northwestern University, Evanston, IL, USA
A Programmer's Guide to Cascaded Adaptive Combiners: Online Learning by Biologically Accurate Models of Multilayer Neuron Networks
Jun 12, 2026Martin Nilsson, Denis KleykoBiologically Plausible Learning Adaptive Learning
RISE Research Institutes of Sweden, Kista, Sweden · Örebro University, Örebro, Sweden
Efficiently Learning Drifting Halfspaces with Massart Noise
Jun 9, 2026Mingchen Ma, Guyang Cao, Jelena Diakonikolas +1Noisy-Label Learning Learning with Noisy Labels
University of Wisconsin-Madison
Exploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization
Jun 8, 2026Balaraju BattuExploration-Exploitation Tradeoff Adaptive Learning
European University Institute, Florence, Italy · New York University Abu Dhabi, UAE
Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization
Jun 6, 2026Wentao Zhang, Yutong Zhang, Wentao MoOnline Convex Optimization Bandit Convex Optimization
Tsinghua Shenzhen International Graduate School, Tsinghua University, China · College of Mathematics, Sichuan University, China
A physical adaptive material motor unit neural network: a hygromorph composite material machine
Jun 5, 2026Charles de Kergariou, David Correa, Adam W. Perriman +2Materials Science Adaptive Learning
Bristol Composites Institute, School of Civil, Aerospace and Mechanical Engineering, University of Bristol, University Walk, Bristol BS8 1TR, United Kingdom · School of Architecture, University of Waterloo, 7 Melville Street South, Cambridge, Ontario, N1S 2H4, Canada · Research School of Chemistry and John Curtin School of Medical Research, Australian National University, Canberra ACT2601, Australia +2

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Sherpa: Teaching LLMs to Teach Adaptively

Weixian Xu, Yanzhe Zhang, Zora Zhiruo Wang, Changyu Chen+1

Stanford University · Georgia Tech · Carnegie Mellon University

RL for Language Models Intelligent Tutoring Systems AI in Education Adaptive Learning

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Large language models (LLMs) have become increasingly capable problem solvers, but being able to solve a problem is not the same as being able to teach it. Existing approaches to training LLMs as teachers rely on demonstrations, preference data, or predefined pedagogical criteria that specify what good teaching looks like. However, these signals are often not grounded in individual student learning outcomes, where effective teaching strategies can vary substantially across learners. To address this, we introduce Sherpa, a multi-turn reinforcement learning framework that instantiates multiple student archetypes with LLMs conditioned on distinct learning preferences and trains a teacher model to adapt its instruction by directly maximizing their learning outcomes. Teacher LLMs trained with Sherpa improve instructed students' performance across all archetypes by an average of 20.5 percentage points. Under MathTutorBench's evaluation, Sherpa raises the overall pedagogy score from 52.5% to 79.2%, indicating better teaching responses. Our human studies show that the trained teacher is preferred over the base model in 79.6% of pairwise comparisons. Together, Sherpa trains LLM teachers to adapt to diverse simulated students and become better aligned with human teachers, paving the road towards AI tutors teaching real students.

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