Formal verification using proof assistants, such as Coq, enables the creation of high-quality software. However, the verification process requires significant expertise and manual effort to write proofs. Recent work has explored automating proof synthesis using machine learning and large language models (LLMs). This work has shown that identifying relevant premises, such as lemmas and definitions, can aid synthesis. We present Rango, a fully automated proof synthesis tool for Coq that automatically identifies relevant premises and also similar proofs from the current project and uses them during synthesis. Rango uses retrieval augmentation at every step of the proof to automatically determine which proofs and premises to include in the context of its fine-tuned LLM. In this way, Rango adapts to the project and to the evolving state of the proof. We create a new dataset, CoqStoq, of 2,226 open-source Coq projects and 196,929 theorems from GitHub, which includes both training data and a curated evaluation benchmark of well-maintained projects. On this benchmark, Rango synthesizes proofs for 32.0% of the theorems, which is 29% more theorems than the prior state-of-the-art tool Tactician. Our evaluation also shows that Rango adding relevant proofs to its context leads to a 47% increase in the number of theorems proven.

Though the primary method for evaluating multiple UAV path planning algorithms is simulation, there is no lightweight open-source software built to compare algorithms. As a result, most researchers develop their own simulation environments. The presence of many simulation environments makes evaluation of separately developed algorithms difficult. To introduce standardization into the multiple UAV path planning space, we have created an easy-to-use simulator for both development and evaluation of path planning algorithms. Our simulator focuses on the problem of small object detection using multiple UAVs. Its careful object-oriented design allows users unlimited flexibility in developing planning algorithms. UavSim is freely available on GitHub (https://github.com/rmaksymiuk/UavSim).