# Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft

arXiv: https://arxiv.org/abs/2407.03460 · HTML: https://mlsanity.com/paper/2407.03460/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 3 Jul 2024.

## Builds on (most cited first, 2 of 2)

- [Player-Driven Emergence in LLM-Driven Game Narrative](https://mlsanity.com/paper/2404.17027/) (2024)
- [Investigating Agency of LLMs in Human-AI Collaboration Tasks](https://mlsanity.com/paper/2305.12815/) (2023)

## Built on by (most cited first, 9 of 9)

- [V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models](https://mlsanity.com/paper/2504.06148/) (2025)
- [TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model Reasoning](https://mlsanity.com/paper/2502.18431/) (2025)
- [TongSIM: A General Platform for Simulating Intelligent Machines](https://mlsanity.com/paper/2512.20206/) (2025)
- [Understanding Persuasive Interactions between Generative Social Agents and Humans: The Knowledge-based Persuasion Model (KPM)](https://mlsanity.com/paper/2602.11483/) (2026)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [MARCO: Meta-Reflection with Cross-Referencing for Code Reasoning](https://mlsanity.com/paper/2505.17481/) (2025)
- [LLMs as Agentic Cooperative Players in Multiplayer UNO](https://mlsanity.com/paper/2509.09867/) (2025)
- [Deflanderization for Game Dialogue: Balancing Character Authenticity with Task Execution in LLM-based NPCs](https://mlsanity.com/paper/2510.13586/) (2025)
- [A.D.A.M.O. (Agent for language-Driven Actions with Multimodal Observations): A Visual-Symbolic Framework for Virtual Humans](https://mlsanity.com/paper/2609.35463/) (2026)

## Abstract (arXiv)

The use of generative AI in video game development is on the rise, and as the conversational and other capabilities of large language models continue to improve, we expect LLM-driven non-player characters (NPCs) to become widely deployed. In this paper, we seek to understand how human players collaborate with LLM-driven NPCs to accomplish in-game goals. We design a minigame within Minecraft where a player works with two GPT4-driven NPCs to complete a quest. We perform a user study in which 28 Minecraft players play this minigame and share their feedback. On analyzing the game logs and recordings, we find that several patterns of collaborative behavior emerge from the NPCs and the human players. We also report on the current limitations of language-only models that do not have rich game-state or visual understanding. We believe that this preliminary study and analysis will inform future game developers on how to better exploit these rapidly improving generative AI models for collaborative roles in games.