LLMSTEP: LLM proofstep suggestions in Lean

We present LLMSTEP, a tool for integrating a language model into the Lean proof assistant. LLMSTEP is a Lean 4 tactic that sends a user's proof state to a server hosting a language model. The language model generates suggestions, which are checked in Lean and displayed to a user in their development environment. We provide a baseline language model, along with code for fine-tuning and evaluation to support further development. We provide server implementations that run on CPU, a CUDA GPU, or a Google Colab notebook, as a step towards fast, effective language model suggestions for any user

Frame-dragging and the kinematics of Galactic-Center stars

We calculate the effects of frame dragging on the Galactic-Center stars. Assuming the stars are only slightly relativistic, we derive an approximation to the Kerr metric, which turns out to be a weak field Schwarzschild metric plus a frame dragging term. By numerically integrating the resulting geodesic equations, we compute the effect on keplerian elements and the kinematics. We find that the kinematic effect at pericenter passage is proportional to (a(1-e^2))^{-2}. For known Galactic-center stars it is of order 10 m/s. If observed this would provide a measurement of the spin of the black hole.Comment: To appear in Ap