A video game at the centre of a debate over artificial intelligence (AI) is in the running for the top prize at next year's Bafta Game Awards. Arc Raiders, from Swedish developer Embark Studios, has been a smash-hit since its October launch, selling more than four million copies. But the multiplayer shooter has been criticised for using text-to-speech tools to create additional lines, based on dialogue previously recorded by the game's actors. It is one of 10 titles longlisted for the prestigious best game award, with a shortlist to be announced in the run-up to April's annual ceremony. Other games up for the top prize include blockbusters Ghost of Yōtei and Death Stranding 2, indie games Hollow Knight: Silksong and Hades II, and indie adventure Blue Prince.

Human-like virtual characters are crucial for games, storytelling, and virtual reality, yet current methods rely heavily on annotated data or handcrafted persona prompts, making it difficult to scale up and generate realistic, contextually coherent personas. We create the first QA dataset for BaZi-based persona reasoning, where real human experiences categorized into wealth, health, kinship, career, and relationships are represented as life-event questions and answers. Furthermore, we propose the first BaZi-LLM system that integrates symbolic reasoning with large language models to generate temporally dynamic and fine-grained virtual personas. Compared with mainstream LLMs such as DeepSeek-v3 and GPT-5-mini, our method achieves a 30.3%-62.6% accuracy improvement. In addition, when incorrect BaZi information is used, our model's accuracy drops by 20%-45%, showing the potential of culturally grounded symbolic-LLM integration for realistic character simulation.

Fans have reacted angrily after Microsoft announced price increases to its Xbox Game Pass subscription service. The company announced that the most popular tier of its Netflix-style video games system - available to PC and Xbox players - would rise by more than 50% from £14.99 to £22.99 per month. Reacting on social media, loads of fans said they had cancelled their Game Pass subscriptions, with some reporting the service's cancellation page had crashed due to demand. BBC Newsbeat has asked Microsoft if the outage was linked to a surge in visits. In a blog post detailing the changes to Game Pass, Microsoft said it would offer three tiers - Essential (£10 per month), Premium (£14.99) and Ultimate (£22.99).

Developing 3D games requires specialized expertise across multiple domains, including programming, 3D modeling, and engine configuration, which limits access to millions of potential creators. Recently, researchers have begun to explore automated game development. However, existing approaches face three primary challenges: (1) limited scope to 2D content generation or isolated code snippets; (2) requirement for manual integration of generated components into game engines; and (3) poor performance on handling interactive game logic and state management. While Multimodal Large Language Models (MLLMs) demonstrate potential capabilities to ease the game generation task, a critical gap still remains in translating these outputs into production-ready, executable game projects based on game engines such as Unity and Unreal Engine. To bridge the gap, this paper introduces UniGen, the first end-to-end coordinated multi-agent framework that automates zero-coding development of runnable 3D games from natural language requirements. Specifically, UniGen uses a Planning Agent that interprets user requirements into structured blueprints and engineered logic descriptions; after which a Generation Agent produces executable C# scripts; then an Automation Agent handles engine-specific component binding and scene construction; and lastly a Debugging Agent provides real-time error correction through conversational interaction. We evaluated UniGen on three distinct game prototypes. Results demonstrate that UniGen not only democratizes game creation by requiring no coding from the user, but also reduces development time by 91.4%. We release UniGen at https://github.com/yxwan123/UniGen. A video demonstration is available at https://www.youtube.com/watch?v=xyJjFfnxUx0.

This paper presents a novel framework for automated game template generation by transforming Game Design Documents (GDDs) into functional Unity game prototypes using Natural Language Processing (NLP) and multi-modal Large Language Models (LLMs). We introduce an end-to-end system that parses GDDs, extracts structured game specifications, and synthesizes Unity-compatible C# code that implements the core mechanics, systems, and architecture defined in the design documentation. Our approach combines a fine-tuned LLaMA-3 model specialized for Unity code generation with a custom Unity integration package that streamlines the implementation process. Evaluation results demonstrate significant improvements over baseline models, with our fine-tuned model achieving superior performance (4.8/5.0 average score) compared to state-of-the-art LLMs across compilation success, GDD adherence, best practices adoption, and code modularity metrics. The generated templates demonstrate high adherence to GDD specifications across multiple game genres. Our system effectively addresses critical gaps in AI-assisted game development, positioning LLMs as valuable tools in streamlining the transition from game design to implementation.

Mike Verdu has left Netflix, according to Game File with Stephen Totilo. Netflix brought the former Oculus and EA exec onboard to launch and lead its gaming efforts in 2021. Under Verdu's leadership, the company released a bunch of new and ported titles, as well as establishing an internal game development operation. In mid-2024, however, Netflix changed its gaming strategy and hired Alain Tascan, the executive vice president for game development at Epic Games, to lead its gaming efforts. Verdu still served as the VP for games until November 2024, after which he was named as the Vice President of generative AI for games. On LinkedIn, Verdu wrote that his role was about "driving a'once in a generation' inflection point for game development and player experiences using generative AI."

Warner Bros Discovery recently shut down a trio of game studios, including the well-regarded Monolith Productions. This has put one of the coolest game mechanics of the 2010s in limbo. Middle-earth: Shadow of Mordor's excellent Nemesis system is locked behind a patent owned by Warner Bros all the way until 2036, according to reporting by Eurogamer. The Nemesis system was featured in both 2014's Shadow of Mordor and the follow-up Middle-earth: Shadow of War. Simply put, it's a gameplay mechanic in which enemies remember previous encounters with the protagonist.

The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes. The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development. We approach the learning process for IDGEs as a Next State Prediction task, wherein the model autoregressively predicts the game states given player actions. The computation of game states must be precise; otherwise, slight errors could corrupt the game-play experience. This is challenging because of the gap between stability and diversity. To address this, we train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs. This work lays the groundwork for future advancements in transforming how games are created and played.

Game development is a highly technical practice that traditionally requires programming skills. This serves as a barrier to entry for would-be developers or those hoping to use games as part of their creative expression. While there have been prior game development tools focused on accessibility, they generally still require programming, or have major limitations in terms of the kinds of games they can make. In this paper we introduce Mechanic Maker, a tool for creating a wide-range of game mechanics without programming. It instead relies on a backend symbolic learning system to synthesize game mechanics from examples. We conducted a user study to evaluate the benefits of the tool for participants with a variety of programming and game development experience. Our results demonstrated that participants' ability to use the tool was unrelated to programming ability. We conclude that tools like ours could help democratize game development, making the practice accessible regardless of programming skills.

Game development is a highly specialized task that relies on a complex game engine powered by complex programming languages, preventing many gaming enthusiasts from handling it. This paper introduces the Interaction-driven Game Engine (IGE) powered by LLM, which allows everyone to develop a custom game using natural language through Human-LLM interaction. To enable an LLM to function as an IGE, we instruct it to perform the following processes in each turn: (1) $P_{script}$ : configure the game script segment based on the user's input; (2) $P_{code}$ : generate the corresponding code snippet based on the game script segment; (3) $P_{utter}$ : interact with the user, including guidance and feedback. We propose a data synthesis pipeline based on the LLM to generate game script-code pairs and interactions from a few manually crafted seed data. We propose a three-stage progressive training strategy to transfer the dialogue-based LLM to our IGE smoothly. We construct an IGE for poker games as a case study and comprehensively evaluate it from two perspectives: interaction quality and code correctness. The code and data are available at \url{https://github.com/alterego238/IGE}.