AGI Maze as a Benchmark Framework for World-Modeling Agents
Quick Answer
AGI Maze introduces a benchmark framework for world-modeling agents, highlighting limitations of LLMs like GPT-3 in representing environments.
Quick Take
AGI Maze introduces a benchmark framework for world-modeling agents, highlighting limitations of LLMs like GPT-3 in representing environments. Initial tests reveal that vanilla LLMs struggle with maze tasks, while a baseline agent using message history shows some improvement but still underperforms compared to human capabilities.
Key Points
- AGI Maze provides grid-based maze tasks with varying difficulty levels.
- Vanilla LLMs fail to internally represent mazes during inference.
- A baseline agent using message history improves performance but is still inadequate.
- The framework aims to enhance agent learning of world state representations.
- Tasks require memory and structured hypotheses about hidden states.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2607. 00627v1 Announce Type: new Abstract: 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 agents must learn and use world state representations, not just infer a local rule over readily provided observations. We provide an initial evaluation of several vanilla LLMs on simple mazes showing that they fail to represent mazes internally at LLM inference time.
We also introduce a baseline agent, which is allowed to use its message history as a working memory to construct descriptions of observations at agentic runtime. Although this can improve performance, it is still insufficient for an LLM agent to reliably solve even small mazes within a step budget that is more than enough for humans.
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