mlsanityData to 14 Oct 2026

CrafText Benchmark: Advancing Instruction Following in Complex Multimodal Open-Ended World

arXiv 2505.11962 ↗First posted 17 May 2025cs.AIcited by 2

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Abstract (arXiv)

Following instructions in real-world conditions requires the ability to adapt to the world's volatility and entanglement: the environment is dynamic and unpredictable, instructions can be linguistically complex with diverse vocabulary, and the number of possible goals an agent may encounter is vast. Despite extensive research in this area, most studies are conducted in static environments with simple instructions and a limited vocabulary, making it difficult to assess agent performance in more diverse and challenging settings. To address this gap, we introduce CrafText, a benchmark for evaluating instruction following in a multimodal environment with diverse instructions and dynamic interactions. CrafText includes 3,924 instructions with 3,423 unique words, covering Localization, Conditional, Building, and Achievement tasks. Additionally, we propose an evaluation protocol that measures an agent's ability to generalize to novel instruction formulations and dynamically evolving task configurations, providing a rigorous test of both linguistic understanding and adaptive decision-making.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 28 of its 35 references that are in the map. First posted on arXiv 17 May 2025.

Builds on

28 of 35 references.

References · 28

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
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16 more
  1. 2019MineRL: A Large-Scale Dataset of Minecraft Demonstrationscited by 215
  2. 2021Benchmarking the Spectrum of Agent Capabilitiescited by 200
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  15. 2022Learning to Generalize with Object-centric Agents in the Open World Survival Game Craftercited by 13
  16. 2019Reinforcement Learning for Learning of Dynamical Systems in Uncertain Environment: a Tutorialcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.