AGI Maze as a Benchmark Framework for World-Modeling Agents
Large language models (LLMs) are powerful pattern-completion systems, but their default operating mode - predicting the next token from a static context - does not reliably produce persistent, manipulable representations of an external world. Many tasks that look like "reasoning" in text become substantially harder once the environment is partially observable, stateful, and requires memory and structured hypotheses about hidden state. AGI Maze is a lightweight framework for building such environments without requiring high-dimensional sensory inputs. It provides a family of grid-based maze tasks with a clean API and multiple difficulty regimes. The goal is to create benchmarks where agent...
| Authors | Alexey Potapov |
|---|---|
| Primary category | cs.AI |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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