The Open O1 project is a groundbreaking initiative aimed at matching the powerful capabilities of proprietary models, particularly OpenAI’s O1, through an open-source approach. By leveraging advanced training methodologies and community-driven development, Open O1 seeks to democratize access to state-of-the-art AI models. Proprietary AI models like OpenAI’s O1 have demonstrated exceptional capabilities in reasoning, tool… →

Large language model (LLM)–based AI companions have evolved from simple chatbots into entities that users perceive as friends, partners, or even family members. Yet, despite their human-like capability, the AI companions often make biased, discriminatory, and harmful claims. These biases are capable of enforcing inherent stereotypes and causing psychological suffering, particularly in marginalized communities. Conventional… →

Machines learn to connect images and text by training on large datasets, where more data helps models recognize patterns and improve accuracy. Vision-language models (VLMs) rely on these datasets to perform tasks like image captioning and visual question answering. The question is whether increasing datasets to 100 billion examples dramatically improves accuracy, cultural diversity, and… →

The dominant approach to pretraining large language models (LLMs) relies on next-token prediction, which has proven effective in capturing linguistic patterns. However, this method comes with notable limitations. Language tokens often convey surface-level information, requiring models to process vast amounts of data to develop deeper reasoning capabilities. Additionally, token-based learning struggles with capturing long-term dependencies,… →

Artificial Intelligence is increasingly integrated into various sectors, yet there is limited empirical evidence on its real-world application across industries. Traditional research methods—such as predictive modeling and user surveys—struggle to capture AI’s evolving role in workplaces. This makes it difficult to assess its influence on productivity, labor markets, and economic structures. A more data-driven approach… →

Test-Time Scaling (TTS) is a crucial technique for enhancing the performance of LLMs by leveraging additional computational resources during inference. Despite its potential, there has been little systematic analysis of how policy models, Process Reward Models (PRMs), and problem complexity influence TTS, limiting its practical application. TTS can be categorized into Internal TTS, which encourages… →