AI Native Games: A Survey and Roadmap

• URL: http://arxiv.org/abs/2607.00527v2
• Date: Fri, 03 Jul 2026 14:06:15 GMT
• Title: AI Native Games: A Survey and Roadmap
• Abstract summary: Generative AI enables games to produce dialogue, quests, characters, images, and worlds at runtime.<n>Yet generation alone does not make a game AI-native, nor does it guarantee playability.<n>This paper defines AI-native games by whether generative AI is runtime of the core loop.
• Score: 7.187734244028075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative AI now enables games to produce dialogue, quests, characters, images, and worlds at runtime. Yet generation alone does not make a game AI-native, nor does it guarantee playability. This paper defines AI-native games by whether runtime generative AI is constitutive of the core loop: if the AI component were removed or trivially replaced, the central form of play would collapse or become fundamentally different. This counterfactual criterion separates AI-native games from AI-augmented games and adjacent boundary artifacts. Using this definition, we screen candidate artifacts and analyze 53 publicly available AI-native games and prototypes. We introduce a dual-axis G/N taxonomy: the G-axis captures player-facing game type, while the N-axis captures the dominant AI mechanic that makes generative AI indispensable to play. The corpus is concentrated around language-forward designs, especially narrative adventure, epistemic interaction, and generative narrative, while categories such as semantic adjudication, multi-agent simulation, generative construction, and relationship/companion play remain less represented. We argue that the central design problem is organizing semantic openness into stable gameplay. AI-native design depends on mechanical invariants: goals, rules, state, feedback, pacing, and player agency that make open-ended AI outputs interpretable and consequential. We conclude with a roadmap for controllable generation, AI-as-mechanic design, multimodal and multi-agent systems, inference economics, evaluation, safety, and regulation.

Related papers

• Augmenting Game AI with Deep Reinforcement Learning [36.25902064220114]
  We envision more applications of reinforcement learning for game AI in the future.<n>We present examples of games with reinforcement learning-augmented game AI and describe the practicalities of deploying player-facing machine learning agents in modern games.
  arXiv  Detail & Related papers  (2026-06-18T13:23:19Z)
• AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games [63.29377274531968]
  We introduce the AI GameStore, a scalable and open-ended platform to synthesize new representative human games.<n>We generate 100 such games based on the top charts of Apple App Store and Steam, and evaluate seven frontier vision-language models (VLMs) on short episodes of play.<n>The best models achieved less than 10% of the human average score on the majority of the games, and especially struggled with games that challenge world-model learning, memory and planning.
  arXiv  Detail & Related papers  (2026-02-19T18:17:25Z)
• Gaming the Arena: AI Model Evaluation and the Viral Capture of Attention [0.0]
  I examine the rise of so-called 'arenas' in which AI models are evaluated with reference to gladiatorial-style 'battles'<n>I argue that the arena-ization is being powered by a 'viral' desire to capture attention both in, and outside of, the AI community.
  arXiv  Detail & Related papers  (2025-12-17T09:50:13Z)
• Artificial Intelligence and Market Entrant Game Developers [0.34998703934432673]
  This paper shows that relatively more independent developers entered the market when generative AI became more publicly accessible.<n>Game features associated with AI hint nuanced impacts of AI on independent developers.
  arXiv  Detail & Related papers  (2025-09-18T12:35:19Z)
• Multi-Actor Generative Artificial Intelligence as a Game Engine [49.360775442760314]
  Generative AI can be used in multi-actor environments with purposes ranging from social science modeling to interactive narrative and AI evaluation.<n>We argue here that a good approach is to take inspiration from tabletop role-playing games (TTRPGs), where a Game Master (GM) is responsible for the environment and generates all parts of the story not directly determined by the voluntary actions of player characters.
  arXiv  Detail & Related papers  (2025-07-10T22:31:09Z)
• GRUtopia: Dream General Robots in a City at Scale [65.08318324604116]
  This paper introduces project GRUtopia, the first simulated interactive 3D society designed for various robots. GRScenes includes 100k interactive, finely annotated scenes, which can be freely combined into city-scale environments. GRResidents is a Large Language Model (LLM) driven Non-Player Character (NPC) system that is responsible for social interaction.
  arXiv  Detail & Related papers  (2024-07-15T17:40:46Z)
• Open-Ended Wargames with Large Language Models [3.2228025627337864]
  We introduce "Snow Globe," an LLM-powered multi-agent system for playing qualitative wargames. We describe its software architecture conceptually and release an open-source implementation alongside this publication. We discuss potential applications of the approach and how it fits into the broader wargaming ecosystem.
  arXiv  Detail & Related papers  (2024-04-17T14:54:58Z)
• Toward Human-AI Alignment in Large-Scale Multi-Player Games [24.784173202415687]
  We analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games) We find that while human players exhibit variability in fight-flight and explore-exploit behavior, AI players tend towards uniformity. These stark differences underscore the need for interpretable evaluation, design, and integration of AI in human-aligned applications.
  arXiv  Detail & Related papers  (2024-02-05T22:55:33Z)
• Beyond Reality: The Pivotal Role of Generative AI in the Metaverse [98.1561456565877]
  This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse. We delve into the applications of text generation models like ChatGPT and GPT-3, which are enhancing conversational interfaces with AI-generated characters. We also examine the potential of 3D model generation technologies like Point-E and Lumirithmic in creating realistic virtual objects.
  arXiv  Detail & Related papers  (2023-07-28T05:44:20Z)
• Promptable Game Models: Text-Guided Game Simulation via Masked Diffusion Models [68.85478477006178]
  We present a Promptable Game Model (PGM) for neural video game simulators. It allows a user to play the game by prompting it with high- and low-level action sequences. Most captivatingly, our PGM unlocks the director's mode, where the game is played by specifying goals for the agents in the form of a prompt. Our method significantly outperforms existing neural video game simulators in terms of rendering quality and unlocks applications beyond the capabilities of the current state of the art.
  arXiv  Detail & Related papers  (2023-03-23T17:43:17Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)
• From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI [10.80914659291096]
  Game AI has established itself as a research area for developing and testing the most advanced forms of AI algorithms. Advances in Game AI are starting to be extended to areas outside of games, such as robotics or the synthesis of chemicals.
  arXiv  Detail & Related papers  (2020-02-24T18:28:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.