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...
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Multi-Method Validation of Large Language Model Medical Translation Across High- and Low-Resource Languages
arXiv:2603.22642v1 Announce Type: new Abstract: Language barriers affect 27.3 million U.S. residents with non-English language preference, yet professional medical translation remains costly and often unavailable. We evaluated four frontier large language models (GPT-5.1, Claude Opus 4.5, Gemini 3 Pro, Kimi...
Mi:dm K 2.5 Pro
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...
Prompts and Prayers: the Rise of GPTheology
arXiv:2603.10019v1 Announce Type: cross Abstract: Increasingly artificial intelligence (AI) has been cast in "god-like" roles (to name a few: film industry - Matrix, The Creator, Mission Impossible, Foundation, Dune etc.; literature - Children of Time, Permutation City, Neuromancer, I Have...
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...