Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification
arXiv:2604.05302v1 Announce Type: new Abstract: Text simplification supports second language (L2) learning by providing comprehensible input, consistent with the Input Hypothesis. However, constructing personalized parallel corpora is costly, while existing large language model (LLM)-based readability control methods rely on pre-labeled...
Time-Warping Recurrent Neural Networks for Transfer Learning
arXiv:2604.02474v1 Announce Type: new Abstract: Dynamical systems describe how a physical system evolves over time. Physical processes can evolve faster or slower in different environmental conditions. We use time-warping as rescaling the time in a model of a physical system....
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arXiv:2603.18788v1 Announce Type: new Abstract: The evolving LLM landscape requires capabilities beyond simple text generation, prioritizing multi-step reasoning, long-context understanding, and agentic workflows. This shift challenges existing models in enterprise environments, especially in Korean-language and domain-specific scenarios where scaling is...
Distilling Deep Reinforcement Learning into Interpretable Fuzzy Rules: An Explainable AI Framework
arXiv:2603.13257v1 Announce Type: new Abstract: Deep Reinforcement Learning (DRL) agents achieve remarkable performance in continuous control but remain opaque, hindering deployment in safety-critical domains. Existing explainability methods either provide only local insights (SHAP, LIME) or employ over-simplified surrogates failing to...
Evaluating Cross-Lingual Classification Approaches Enabling Topic Discovery for Multilingual Social Media Data
arXiv:2602.17051v1 Announce Type: new Abstract: Analysing multilingual social media discourse remains a major challenge in natural language processing, particularly when large-scale public debates span across diverse languages. This study investigates how different approaches for cross-lingual text classification can support reliable...
From Transcripts to AI Agents: Knowledge Extraction, RAG Integration, and Robust Evaluation of Conversational AI Assistants
arXiv:2602.15859v1 Announce Type: new Abstract: Building reliable conversational AI assistants for customer-facing industries remains challenging due to noisy conversational data, fragmented knowledge, and the requirement for accurate human hand-off - particularly in domains that depend heavily on real-time information. This...