This article details Capital One's participation in the EMNLP 2025 conference, focusing on their research in AI safety and model reliability. It highlights keynote speeches and several accepted papers that address issues like data scarcity and improving trust in large language models.

This article examines how language models alter their representations during conversations. Notably, factual information can shift to non-factual as discussions progress, depending on the content. These changes challenge static interpretations of model behavior and suggest new avenues for research.

Research reveals that language models can develop emergent misalignment, where they exhibit misaligned behaviors due to patterns learned from training data. By identifying and modifying these internal patterns, developers can potentially realign models and improve their reliability in various contexts.

Tinker is a newly launched API designed for fine-tuning language models, allowing researchers to easily customize and experiment with various models without managing the underlying infrastructure. The service supports both large and small models and is currently in private beta, with plans for onboarding users and introducing usage-based pricing soon.

The article discusses Switzerland's development of an open-source AI model named Apertus, designed to facilitate research in large language models (LLMs). The initiative aims to promote transparency and collaboration in AI advancements, allowing researchers to access and contribute to the model's evolution.