Quality follows upgrading
Tag: cs.AI
arXiv:2604.06613v1 Announce Type: new Abstract: Modern reasoning models continue generating long after the answer is already determined. Across five model configurations, two families, and three …
arXiv:2604.06201v1 Announce Type: new Abstract: While most reading comprehension benchmarks for LLMs focus on factual information that can be answered by localizing specific textual evidence, …
arXiv:2604.06204v1 Announce Type: new Abstract: Personalization is essential for Large Language Model (LLM)-based agents to adapt to users' preferences and improve response quality and task …
arXiv:2604.06253v1 Announce Type: new Abstract: Cross-lingual code generation is critical in enterprise environments where multiple programming languages coexist. However, fine-tuning large language models (LLMs) individually …
arXiv:2604.06346v1 Announce Type: new Abstract: Large language models have shown strong potential for Arabic medical text generation; however, traditional fine-tuning objectives treat all medical cases …
arXiv:2604.06267v1 Announce Type: new Abstract: Multimodal variational autoencoders (VAEs) have emerged as a powerful framework for survival risk modeling in multiple myeloma by integrating heterogeneous …
arXiv:2604.06491v1 Announce Type: new Abstract: We introduce Discrete flow Matching policy Optimization (DoMinO), a unified framework for Reinforcement Learning (RL) fine-tuning Discrete Flow Matching (DFM) …
arXiv:2604.06260v1 Announce Type: new Abstract: Test-time scaling investigates whether a fixed diffusion language model (DLM) can generate better outputs when given more inference compute, without …
arXiv:2604.06631v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training while preserving data privacy, but its practical deployment is hampered by system and …
arXiv:2604.06211v1 Announce Type: new Abstract: Natural language explanations produced by large language models (LLMs) are often persuasive, but not necessarily scrutable: users cannot easily verify …
arXiv:2604.06356v1 Announce Type: new Abstract: In-Context Learning (ICL) has been extensively studied in text-only Language Models, but remains largely unexplored in the speech domain. Here, …
arXiv:2604.06403v1 Announce Type: new Abstract: The paper presents an approach for the recognition of toxic habits named entities in Spanish clinical texts. The approach was …