← Back to all news
arXiv AI By Dilan Mian, Jeremy Bradbury, Yann-Ga\"el Gu\'eh\'eneuc, Fabio Petrillo, Cristiano Politowski

LLM-based NPCs in Video Games: A Systematic Mapping Study

Read the original on arXiv AI →

The study reviews 377 academic papers on large language model (LLM)–based non‑player characters (NPCs) in video games, focusing on 42 key works. It quantitatively maps component adoption, noting that short‑term memory and world‑state grounding are common (60% and 69%), while long‑term memory, reflection, and retrieval‑augmented generation are less frequent. The paper also reports latency ranges (2.6–15 s) and identifies six structural barriers to commercial adoption, offering evidence‑based recommendations for developers and researchers.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Sep 17

Which LLM is Best for Translating Natural Language Goals to PDDL

The paper evaluates how well current Large Language Models can translate natural language goals, written by video game testers, into well‑formed PDDL targets for classical planning. Using a carefully designed prompt template, six state‑of‑the‑art LLMs were tested on correctness, speed, and error tendencies with real‑world benchmarks. All models achieved high correctness (>92%), with Gemini 2.5 Flash reaching 96% accuracy and the fewest false positives, while GPT‑4.1 was the fastest, yet differences in performance and occasional failures due to ambiguity and domain limits remain.

By Tomas Balyo, Lukas Chrpa, G. Michael Youngblood
llmsbenchmarks
More like this →
arXiv AI
Oct 7

SWE-Game: Can Coding Agents Build the Games We Want?

SWE-Game is a benchmark comprising 247 tasks based on 41 Godot games across 13 gameplay categories, testing coding agents on tasks such as brief-to-game, design-document implementation, skeleton completion, fault repair, and Godot-to-Unity porting. Evaluation uses engine-state checks, replay of certified reference inputs, and agent-authored demonstrations to judge mechanic correctness, playability, and post-repair behavior, supplemented by vision‑language rubrics for presentation. Across six models, Opus5 leads but overall scores stay below 60/100, highlighting common issues like omitted requirements and gameplay logic errors.

By Xiaoyu Chen, Lai Wei, Jin Wang, Xiangyu Zou, Ruochen Fan, Enze Luo, Mingzhe Yao, Jiahui Zhu, Yuhua Wen, Linghe Kong, Weiran Huang
agentsmultimodalbenchmarks
More like this →
arXiv Computer Vision
Sep 22

GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay

arXiv:2609.25001v1 Announce Type: new Abstract: Modern video games provide a measurable testbed for AI models, combining abilities of visual understanding, instruction decomposition, goal planning, a...

By Yiran Wang, Xingyilang Yin, Junfu Pu, Guangzhi Wang, Kaifeng Li, Mingyu Ouyang, Huiqiang Sun, Lingen Li, Cheng Cheng, Wangbo Yu, Honghao Chen, Xiaodong Cun, Chi-Man Pun, Zhiguo Cao, Ying Shan
benchmarks
More like this →