Imagine sifting through mountains of data, searching for the tiniest, most elusive particles that hold the secrets of the universe. That's the daily challenge for physicists working with massive datasets from particle colliders. Traditionally, they’ve relied on specialized models to analyze this data, each designed for a specific task. But what if a single, powerful AI model could tackle a wide range of these complex analyses? That's the promise of BBT-Neutron, a new task-agnostic model designed to unlock the power of Big Science data. Unlike typical language models that struggle with numbers, BBT-Neutron uses a clever trick called Binary Tokenization. This allows it to understand and process numerical data directly, just like it understands text. The researchers tested BBT-Neutron on a particularly challenging task: identifying the origins of particle jets produced in high-energy collisions. Remarkably, it performed comparably to highly specialized models, proving its ability to handle complex scientific data. What's even more exciting is BBT-Neutron's ability to scale. As it's fed more data, its performance improves dramatically, hinting at even greater potential as datasets grow larger. This scalability opens doors to a new era of scientific discovery, where a single AI model could revolutionize data analysis across diverse fields like particle physics, astronomy, and beyond. By eliminating the need for task-specific models, BBT-Neutron streamlines research and enables knowledge transfer across different scientific domains. It's a powerful illustration of how AI can transform Big Science, pushing the boundaries of our understanding of the universe and beyond.