Grid-Bench: Can LLMs Predict How Discrete Worlds Change?
We introduce Grid-Bench, an executable benchmark in which eight families of operations transform discrete worlds represented as two-, three-, and four-dimensional grids.
ProofPaper ↗
Key points
- Spatial instructions describe how to move objects, transform regions, or modify their surroundings.
- When several operations are applied in sequence, each acts on a configuration shaped by earlier changes.
- How accurately can large language models (LLMs) predict the resulting spatial state?
- Given an initial grid, explicit rules, and an event history expressed as text, a model predicts the complete final grid.
Sources (1)
- [1]Grid-Bench: Can LLMs Predict How Discrete Worlds Change?arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:50 PM
We introduce Grid-Bench, an executable benchmark in which eight families of operations transform discrete worlds represented as two-, three-, and four-dimensional grids.
Spatial instructions describe how to move objects, transform regions, or modify their surroundings.
Extractive summary: sentences quoted from the sources.
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