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Rotating Carroll Black Holes: A No Go Theorem
Ivan Kol\'a\v{r}, David Kubiznak, Poula Tadros
https://arxiv.org/abs/2506.10451 https://

Rotating Carroll Black Holes: A No Go Theorem
Recently, there has been a lot of interest in Carroll black holes and in particular whether or not one could find a Carrollian analogue of a rotating black hole spacetime. Here we show that every stationary and axisymmetric solution (and thence also a black hole) of Carrollian general relativity in any number of $d\geq 3$ dimensions is necessarily also static. Using this result, we construct a unique stationary and axisymmetric black hole in three dimensions - a static Carroll BTZ black hole. A…

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
Sezen Per\c{c}in, Xin Su, Qutub Sha Syed, Phillip Howard, Aleksei Kuvshinov, Leo Schwinn, Kay-Ulrich Scholl
https://arxiv.org/abs/2507.06956

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
Large language models (LLMs) are very costly and inefficient to update with new information. To address this limitation, retrieval-augmented generation (RAG) has been proposed as a solution that dynamically incorporates external knowledge during inference, improving factual consistency and reducing hallucinations. Despite its promise, RAG systems face practical challenges-most notably, a strong dependence on the quality of the input query for accurate retrieval. In this paper, we investigate th…

Use of a genetic algorithm to find solutions to introductory physics problems
Tom Bensky, Justin Kopcinski
https://arxiv.org/abs/2508.10920 https://arxiv.o…

Use of a genetic algorithm to find solutions to introductory physics problems
In this work, we show how a genetic algorithm (GA) can be used to find step-by-step solutions to introductory physics problems. Our perspective is that the underlying task for this is one of finding a sequence of equations that will lead to the needed answer. Here a GA is used to find an appropriate equation sequence by minimizing a fitness function that measures the difference between the number of unknowns versus knowns in a set of equations. Information about knowns comes from the GA posing …