PLANET: A Collection of Benchmarks for Evaluating LLMs' Planning Capabilities (2504.14773v1)

Abstract: Planning is central to agents and agentic AI. The ability to plan, e.g., creating travel itineraries within a budget, holds immense potential in both scientific and commercial contexts. Moreover, optimal plans tend to require fewer resources compared to ad-hoc methods. To date, a comprehensive understanding of existing planning benchmarks appears to be lacking. Without it, comparing planning algorithms' performance across domains or selecting suitable algorithms for new scenarios remains challenging. In this paper, we examine a range of planning benchmarks to identify commonly used testbeds for algorithm development and highlight potential gaps. These benchmarks are categorized into embodied environments, web navigation, scheduling, games and puzzles, and everyday task automation. Our study recommends the most appropriate benchmarks for various algorithms and offers insights to guide future benchmark development.

Evaluating Planning Capabilities of LLMs with the Planet Benchmark

The paper "Planet: A Collection of Benchmarks for Evaluating LLMs' Planning Capabilities" provides a thorough exploration of benchmark frameworks for assessing the planning abilities of LLMs. Planning, a core aspect of both human and artificial intelligence, requires breaking down complex tasks into manageable steps and optimizing resource use. The paper contends that understanding and measuring LLMs' planning abilities are crucial since these models are increasingly employed as autonomous agents capable of complex decision-making over long durations.

Overview of Benchmarks

The authors categorize existing benchmarks into several domains:

1. Embodied Environments: These benchmarks simulate physical spaces where LLM agents perform tasks akin to household activities. Notable implementations include ALFRED and ALFWorld, which provide environments and tasks to align textual instructions with physical execution. ALFWorld emphasizes the integration of high-level task reasoning with grounded execution in an embodied setting.
2. Web Navigation: Benchmarks like WebShop and Mind2Web challenge LLMs to perform tasks by interacting with web environments. These settings test the models' ability to interpret web page structures and sequentially navigate through them to achieve specified goals.
3. Scheduling: This area includes scenarios for time optimization, such as trip planning and meeting scheduling. TravelPlanner exemplifies benchmarks that require balancing multiple constraints, such as time and budget, while leveraging external sourcing for data like flights and accommodations.
4. Games and Puzzles: By utilizing strategic games like Tower of Hanoi and multi-agent environments, these benchmarks examine how well LLMs can engage in and plan for intricate, goal-oriented tasks.
5. Everyday Task Automation: Benchmarks here focus on task decomposition and completion by breaking down instructions into specific steps. TaskLAMA is an example dataset that aids in examining models' capacities in this domain.
6. Text-Based Reasoning: This dimensions explores reasoning capabilities through formal logic and mathematics. For instance, PrOntoQA assesses the models’ ability to handle logical proof construction based on predefined world models.
7. Planning in Agentic Contexts: Benchmarks here assess planning as a part of a broader agentic task suite, inviting LLMs to demonstrate their competencies in software development, project management, and day-to-day office operations as evaluated in frameworks like AgentBench.

Implications and Future Directions

The paper provides critical insights into benchmarking planning abilities, underscoring the challenges posed by benchmark gaps such as the need for complex world models and long-horizon task handling. While LLMs excel in static environments, dynamic and multimodal tasks frequently reveal their limitations. Addressing these gaps would involve enhancing benchmarks with features like adaptive world models, uncertainty handling, and integrated multimodal inputs.

In the broader scope of AI research, understanding and refining these benchmarks can contribute significantly to the development of more sophisticated, reliable, and general-purpose LLMs capable of autonomous planning. Future research might also focus on improving LLMs’ robustness against cascading errors in long-horizon planning and enhancing their adaptability to incomplete and uncertain data.

In conclusion, the Planet Benchmark paper marks a significant contribution to evaluating LLMs’ planning capabilities through comprehensive and multidimensional benchmarks. This work aims not only at scrutiny but also at inspiring improvements and innovations in LLM benchmarking methodologies, which are pivotal for the next generation of AI systems.