Hierarchical Multi-Persona Induction from User Behavioral Logs: Learning Evidenc

arXiv:2605.08496v1 Announce Type: new Abstract: Current adversarial robustness methods for large language models require extensive datasets of harmful prompts (thousands to

arXiv:2603.28197v1 Announce Type: new Abstract: Pluralistic alignment is essential for adapting large language models (LLMs) to the diverse preferences of individuals and minority

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains

arXiv:2607.13162v2 Announce Type: cross Abstract: What a language model will and will not do is largely set during post-training, but which behaviors it expresses, hides, or

arXiv:2605.11556v1 Announce Type: new Abstract: Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit

arXiv:2605.00012v1 Announce Type: cross Abstract: Modern large language models (LLMs) are used in many business applications in general, and specifically in web search systems and

arXiv:2605.11519v1 Announce Type: new Abstract: Using offline datasets to evaluate conversational agents often fails to cover rare scenarios or to support testing new policies.