Interactive Learning

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1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

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Merging the Knowledge of LLMs for Automatic Speech Recognition
Sep 14, 2026Hayato Futami, Tatsuya KawaharaEnd-To-End Automatic Speech Recognition Models Automatic Speech Recognition
Graduate School of Informatics, Kyoto University, Japan
Subword Segmental BabyLMs: Learning to Tokenise for Sample-Efficient Pretraining
Sep 1, 2026Francois MeyerSubword Tokenization Large Language Model Pretraining
Department of Computer Science University of Cape Town
CogEvol: Towards Efficient and Reliable Learning Environment Generation
Aug 31, 2026Shangqing Tu, Daniel Zhang-Li, Yucheng Wang +20Synthetic Environments Interactive Learning
CogEvol Inc. & Tsinghua University
When Less is More: Understanding When Token Filtering Helps and Fails in AI-generated Text Detection
Aug 30, 2026Xiaoyang Han, Lvxiaowei Xu, Ming CaiMachine-Generated Text Detection Large Language Models Fail
College of Computer Science and Technology, Zhejiang University, Hangzhou, China · College of Artificial Intelligence, Zhejiang University, Hangzhou, China
OctoLong: Mid-Training On Cross-Repository Code Contexts Enhances Long-Context Modeling
Aug 5, 2026Indraneil Paul, Falko Helm, Goran Glavaš +1Efficient Long-Context Inference Codebases
UKP Lab & Center for Applied Cybersecurity ATHENE, TU Darmstadt · CAIDAS, University of Würzburg
AgentForge: An Immersive Role-Playing Platform for Learning Agentic Software Engineering
Aug 4, 2026Zihan Fang, Yueke Zhang, Yu HuangSoftware Engineering Software
Vanderbilt University Nashville, Tennessee, USA
Slides2MindMap: Reconstructing Cognitively Efficient Knowledge Hierarchies from Lecture Slides
Aug 1, 2026Yuzhi Wang, Rongjun Ye, Shengyuan Chen +5Interactive Learning Diagrams
1The Hong Kong Polytechnic University · 2Shanghai Jiaotong University
PersonalPlan: Planning Multi-Agent Systems for Personalized Programming Learning
Jun 17, 2026Zhiyuan Wen, Jiannong Cao, Peng Gao +4Large Language Model Planning Interactive Learning
Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China · JIUTIAN Team, China Mobile Research Institute, Beijing, China · School of Artificial Intelligence, Nankai University, Tianjin, China
Clinically Grounded Privacy Evaluation of Medical LMs
Jun 8, 2026Sasha Ronaghi, Sana Tonekaboni, Lena Stempfle +6Data Leakage Privacy
1Stanford University · 2Massachusetts Institute of Technology · 3American Board of Family Medicine
Developing a UXR Point of View for Cognitive Accessibility in Mobile Learning with Generative AI
May 29, 2026Fatima Ahmad Muazu, Festus Adedoyin, Huseyin Dogan +3Accessibility Interactive Learning
School of Computing and Engineering, Bournemouth University Poole, UK
Classification of non-analyzable word types in web documents to implement an effective Korean e-learning system
May 28, 2026Sang-Taek Park, Ae-Lim Ahn, Eric Laporte +1Text Corpora Interactive Learning
DICORA, Hankuk University of Foreign Studies, Korea · LIGM, Université Paris-Est, France
From Learning Resources to Competencies: LLM-Based Tagging with Evidence and Graph Constraints
May 27, 2026Ngoc Luyen Le, Marie-Hélène Abel, Bertrand LaforgeCompetence Tagging
Gamaizer, 93340 Le Raincy, France · Université de technologie de Compiègne, CNRS, Heudiasyc (Heuristics and Diagnosis of Complex Systems), CS 60319 - 60203 Compiègne Cedex, France · Sorbonne Université, CNRS UMR 7585, LPMHE (Laboratoire de Physique Nucléaire et des Hautes Énergies), 75252 Paris cedex 05, France
When Does Model Collapse Occur in Structured Interactive Learning?
May 19, 2026Yuchen Wu, Kangjie Zhou, Weijie SuCollapse Generative Models
School of Operations Research and Information Engineering, Cornell University · Department of Statistics, Columbia University · Department of Statistics and Data Science, University of Pennsylvania
MIRAGE: Retrieval and Generation of Multimodal Images and Texts for Medical Education
May 6, 2026Miguel Diaz Benito, Cecilia Diana Albelda, Alvaro Garcia Martin +3Multimodal Clinical Data Multimodal Retrieval Augmented Generation
Universidad Autónoma de Madrid, 28049 Madrid, Spain
GA-VisAgent: A Multi-Agent application for code generation and visualization in interactive learning
May 2, 2026Wang Jian, Zhou Jianbo, Xiong Yuhao +4Code Generation Visualization
School of Geography, Nanjing Normal University, Nanjing 210023, Jiangsu, China · School of Environment, Nanjing Normal University, Nanjing 210023, Jiangsu, China · Key Laboratory of Virtual Geographic Environment, Ministry of Education,2026 Nanjing Normal University, Nanjing 210023, Jiangsu, China +2
AI-Integrated Learning Management System for Middle School: A Longitudinal Study of Learning Outcomes Through High School and Beyond
May 2, 2026Misan Paul Etchie, Taiwo OlutosinInteractive Learning Student
Northern Arizona University, Flagstaff, AZ, USA
MAIC-UI: Making Interactive Courseware with Generative UI
Apr 28, 2026Shangqing Tu, Yanjia Li, Keyu Chen +7Interactive Learning Pedagogical Frameworks
Tsinghua University · Guangzhou University · Zhejiang University
T2MM: An LLM Supported Architecture For Inquiry-Based Modeling
Apr 24, 2026John Kos, Rudra Singh, Ashok GoelMultimodal Model Interactive Learning
Georgia Institute of Technology, Atlanta GA 30322, USA
Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models
Apr 23, 2026Chee Wei Tan, Yuchen Wang, Shangxin GuoInteractive Learning Chatbots
Nanyang Technological University, Singapore, Nanyang Ave., Singapore · Nautilus Software Technologies Limited, Hong Kong.
BOIL: Learning Environment Personalized Information
Apr 18, 2026Rohan Patil, Henrik I. ChristensenMulti-Agent Planning Stochastic Exploration
Perturbation: A simple and efficient adversarial tracer for representation learning in language models
Mar 25, 2026Joshua Rozner, Cory ShainRepresentation Learning Feature Alignment
Stanford University
Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
Nov 11, 2025Jon Saad-Falcon, Avanika Narayan, Hakki Orhun Akengin +12LLM Inference Optimization Efficiency
Stanford University · Together AI
Interactive Learning for LLM Reasoning
Sep 30, 2025Hehai Lin, Shilei Cao, Sudong Wang +5LLM Reasoning Strategies Interactive Learning
1The Hong Kong University of Science and Technology (Guangzhou) · 2Sun Yat-sen University · 4National University of Singapore +2
Ultra Strong Machine Learning: LLM-Generated Explanations Do Not Yet Suffice for Teaching Humans Active Learning Strategy
Aug 31, 2025Lun Ai, Johannes Langer, Ute Schmid +1Active Learning Interactive Learning
Department of Computing, Imperial College London, UK · Faculty Information Systems and Applied Computer Science, University of Bamberg, Germany
iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception
Oct 1, 2024Jishnu Jaykumar P, Cole Salvato, Vinaya Bomnale +4Robotic Perception Robot Systems
The University of Texas at Dallas
Dont Just Teach, Explain! A Gamified 20Q Recommender for Cybersecurity Education
Date pendingMary Nusrat, Sarfuddin Bhuiyan, Gahangir HossainCybersecurity Interactive Learning
Information Science, University of North Texas, Denton, TX, USA · Mechanical Engineering, Bangladesh University of Eng. and Tech., BUET, Dhaka, Bangladesh

Sep 14, 2026, cs.CLJ/K move · Enter open · S save

Merging the Knowledge of LLMs for Automatic Speech Recognition

Hayato Futami, Tatsuya Kawahara

Graduate School of Informatics, Kyoto University, Japan

End-To-End Automatic Speech Recognition Models Automatic Speech Recognition Domain Adaptation Interactive Learning

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Automatic speech recognition (ASR) systems, trained on paired speech-text data, have been improved by leveraging language models (LMs) trained on text-only data. LM fusion methods such as shallow fusion and density ratio are well-established methods that incorporate external LMs during ASR decoding. However, they incur additional computational costs due to LM inference, which is particularly problematic for recent larger LMs. In this study, we propose incorporating external LMs via model merging. This method integrates the LMs directly into the parameters of an LLM-based ASR model, requiring no additional computational cost at inference. We formulate domain extension and transfer via arithmetic operations on LoRA parameters. Experimental evaluations were conducted for the domain adaptation of LLM-based ASR trained on CSJ and LibriSpeech. We show that our LM merging consistently improved the ASR performance in the target domains, without degrading inference speed or memory footprint.

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Latest papers 26