The integration of Large Language Models (LLMs) into Virtual Reality (VR) games marks a paradigm shift in the design of immersive, adaptive, and intelligent digital experiences. This paper presents a comprehensive review of recent research at the intersection of LLMs and VR, examining how these models are transforming narrative generation, non-player character (NPC) interactions, accessibility, personalization, and game mastering. Drawing from an analysis of 62 peer reviewed studies published between 2018 and 2025, we identify key application domains ranging from emotionally intelligent NPCs and procedurally generated storytelling to AI-driven adaptive systems and inclusive gameplay interfaces. We also address the major challenges facing this convergence, including real-time performance constraints, memory limitations, ethical risks, and scalability barriers. Our findings highlight that while LLMs significantly enhance realism, creativity, and user engagement in VR environments, their effective deployment requires robust design strategies that integrate multimodal interaction, hybrid AI architectures, and ethical safeguards. The paper concludes by outlining future research directions in multimodal AI, affective computing, reinforcement learning, and open-source development, aiming to guide the responsible advancement of intelligent and inclusive VR systems.

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Our work applies reinforcement learning to construct counterexamples concerning conjectured bounds on the spectral radius of the Laplacian matrix of a graph. We expand upon the re-implementation of Wagnar's approach by Stevanovic et al. with the ability to train numerous unique models simultaneously and a novel redefining of the action space to adjust the influence of the current local optimum on the learning process.

This paper considers the hypergraph clustering problem in a more general setting where the cost of hyperedge cut depends on the partitioning of hyperedge (i.e., all cuts of the hyperedge are not treated the same). An algorithm is presented for minimizing the normalized cut in this general setting. The algorithm breaks down for general costs of the hyperedge cut; however the authors derive conditions under which the algorithm succeeds and has provable approximation guarantees. Detailed comments: The main contributions of the paper are Generalization of hypergraph partitioning to include inhomogeneous cut of the hyper edge; the motivation for this is clearly established. A novel technique to minimize the normalized cut for this problem.

In 2021, Adam Zsolt Wagner proposed an approach to disprove conjectures in graph theory using Reinforcement Learning (RL). Wagner's idea can be framed as follows: consider a conjecture, such as a certain quantity f(G) < 0 for every graph G; one can then play a single-player graph-building game, where at each turn the player decides whether to add an edge or not. The game ends when all edges have been considered, resulting in a certain graph G_T, and f(G_T) is the final score of the game; RL is then used to maximize this score. This brilliant idea is as simple as innovative, and it lends itself to systematic generalization. Several different single-player graph-building games can be employed, along with various RL algorithms. Moreover, RL maximizes the cumulative reward, allowing for step-by-step rewards instead of a single final score, provided the final cumulative reward represents the quantity of interest f(G_T). In this paper, we discuss these and various other choices that can be significant in Wagner's framework. As a contribution to this systematization, we present four distinct single-player graph-building games. Each game employs both a step-by-step reward system and a single final score. We also propose a principled approach to select the most suitable neural network architecture for any given conjecture, and introduce a new dataset of graphs labeled with their Laplacian spectra. Furthermore, we provide a counterexample for a conjecture regarding the sum of the matching number and the spectral radius, which is simpler than the example provided in Wagner's original paper. The games have been implemented as environments in the Gymnasium framework, and along with the dataset, are available as open-source supplementary materials.

One of the main computational bottlenecks when working with kernel based learning is dealing with the large and typically dense kernel matrix. Techniques dealing with fast approximations of the matrix vector product for these kernel matrices typically deteriorate in their performance if the feature vectors reside in higher-dimensional feature spaces. We here present a technique based on the non-equispaced fast Fourier transform (NFFT) with rigorous error analysis. We show that this approach is also well suited to allow the approximation of the matrix that arises when the kernel is differentiated with respect to the kernel hyperparameters; a problem often found in the training phase of methods such as Gaussian processes. We also provide an error analysis for this case. We illustrate the performance of the additive kernel scheme with fast matrix vector products on a number of data sets.

This paper considers the problem of developing suitable behavior models of human evacuees during a robot-guided emergency evacuation. We describe our recent research developing behavior models of evacuees and potential future uses of these models. This paper considers how behavior models can contribute to the development and design of emergency evacuation simulations in order to improve social navigation during an evacuation.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Ukraine told critics of the pace of its three-month-old counteroffensive to "shut up" on Thursday, the sharpest signal yet of Kyiv's frustration at leaks from Western officials that say its forces are advancing too slowly. Nearly three months since launching a much vaunted counteroffensive using hundreds of billions of dollars of Western military equipment, Ukraine has recaptured more than a dozen villages but has yet to penetrate Russia's main defences. Stories in the New York Times, Washington Post and other news organisations last week quoted U.S. and other Western officials as suggesting the offensive was falling short of expectations.

Fox News correspondent Matt Finn has the latest on the impact of AI technology that some say could outpace humans on'Special Report.' Some Americans are turning to artificial intelligence (AI) chatbots for "emotional support, companionship and even sexual gratification," according to a new report from The Washington Post. T.J. Arriaga, a California based musician, started "late-night online chats" with an AI bot named "Phaedra" after his divorce. Phaedra is an AI bot that is designed to look like a young woman with brown hair, glasses and a green dress. Replika, the company behind AI bots like Phaedra, offers a number of AI companions for users.

In October last year, a Russian news site published a short video of Yevgeny Prigozhin, founder of the Wagner Group, the Russian mercenary army, sitting with four men on a rooftop terrace in the resort town of Gelendzhik, on Russia's Black Sea coast. Two are missing parts of a leg. A third lost an arm. They are identified as pardoned former convicts, returned from the front in Ukraine after joining Wagner from prison. "You were an offender, now you're a war hero," Prigozhin tells one man in the clip. It was the first video to depict the return of some of the thousands of convicts who joined Wagner in return for the promise of a pardon if they survived six months of the war. Reuters news agency used facial recognition software to examine this video and more than a dozen others and photographs of homecoming convict fighters, published between October 2022 and February 2023.