The reviewed record of science sign in
Pith

arxiv: 2402.08392 · v1 · pith:LLCYXFBO · submitted 2024-02-13 · cs.CL

Large Language Models as Minecraft Agents

Chris Madge , Massimo Poesio This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:LLCYXFBOrecord.jsonopen to challenge →

classification cs.CL
keywords agentslanguagelargellmsminecraftmodelsactingaddition
0
0 comments X
read the original abstract

In this work we examine the use of Large Language Models (LLMs) in the challenging setting of acting as a Minecraft agent. We apply and evaluate LLMs in the builder and architect settings, introduce clarification questions and examining the challenges and opportunities for improvement. In addition, we present a platform for online interaction with the agents and an evaluation against previous works.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. PokeGym: A Visually-Driven Long-Horizon Benchmark for Vision-Language Models

    cs.CV 2026-04 unverdicted novelty 7.0

    PokeGym is a new benchmark that tests VLMs on long-horizon tasks in a complex 3D game using only visual observations, identifying deadlock recovery as the primary failure mode.