How can I best benchmark LLM planning?
PLANET: A Collection of Benchmarks for Evaluating LLMs' Planning Capabilities
April 22, 2025
https://arxiv.org/pdf/2504.14773This paper introduces PLANET, a comprehensive survey of benchmarks for evaluating the planning capabilities of Large Language Models (LLMs), particularly in the context of agentic AI. It categorizes existing benchmarks based on application domains like embodied environments, web navigation, scheduling, games, and task automation.
Key takeaways for LLM-based multi-agent systems include:
- Emphasis on planning: The paper highlights the crucial role of planning in LLM agents for complex task completion.
- Benchmark categorization: Provides a structured overview of various planning benchmarks, facilitating selection for specific multi-agent scenarios.
- Multi-agent environments: Benchmarks like GAMA-Bench and AgentBoard specifically test LLM decision-making in competitive and collaborative multi-agent settings.
- Gaps in current benchmarks: Identifies areas needing improvement, such as the complexity of world models, long-horizon tasks, planning under uncertainty, and multimodal planning support. This is particularly relevant for building robust multi-agent systems.
- Relevance of games: Games are emphasized as valuable testbeds for strategic planning and multi-agent behavior in LLMs.
- Shift towards embodied and web-based agents: The survey demonstrates a growing trend towards testing LLMs in interactive environments like simulated households (ALFWorld, VirtualHome) and websites (WebArena, Mind2Web), pushing beyond text-based reasoning. This trend is directly applicable to building real-world multi-agent applications.