Scientific writing has always relied on the implicit contract that claims must remain traceable to evidence. Large language models complicate that relationship. Modern systems can generate reports that appear rigorous and extensively sourced while quietly introducing fabricated citations. CiteVerify is FirstPrinciples’ answer to the rise of fake citations.

Before a theorem can be proved, someone has to decide what is worth proving. For decades, the formation of a conjecture remained largely outside the reach of machines. In Automated Conjecturing with TxGraffiti, Randy Davila explores how that boundary has shifted.

A recent recap of work at FirstPrinciples, from fine-tuning and RLVR to ensembles and 122B-scale models. We’re beginning to clarify what improves scientific reasoning, and these are our notes.

A reflection on ML4PS 2025, where researchers in physics and machine learning grappled with the role AI should play in scientific discovery, and what it would take to move from process acceleration toward deeper scientific insight.

The team at FirstPrinciples has reached the AI Physicist’s first autonomy milestone ahead of schedule, assembling the early loops of a system that will guide its own scientific reasoning from research to hypothesis and beyond.

Large Language Models have changed how we think, work, and do science, but can they truly reason like scientists? At FirstPrinciples, we’re exploring the limits of AI generalization and the promise of specialization through the development of the AI Physicist.

With a deep passion for physics, Shiraz Minwalla investigates the complexities of black holes, grey galaxies, and beyond.

FirstPrinciples presented an early demo of its AI Physicist at the Open Science Fair, held this year at CERN. The event sparked conversations on trust, openness, and the role of AI in research, underscoring how collaboration will shape the future of discovery.

In a research world defined by scale and AI-driven discovery, small colleges are proving impact isn’t measured in dollars. At places like Wellesley and Bowdoin, close mentorship, collaborative problem-solving, and a readiness to experiment with new tools are shaping the next generation of scientists to navigate a rapidly evolving research landscape.