Benchmark for Planning and Control with Large Language Model Agents: Blocksworld with Model Context Protocol
Year: 2025
Type: preprint
Abstract: Industrial automation increasingly requires flexible control strategies that can adapt to changing tasks and environments. Agents based on Large Language Models (LLMs) offer potential for such adaptive planning and execution but lack standardized benchmarks for systematic comparison. We introduce a benchmark with an executable simulation environment representing the Blocksworld problem providing five complexity categories. By integrating the Model Context Protocol (MCP) as a standardized tool interface, diverse agent architectures can be connected to and evaluated against the benchmark without implementation-specific modifications. A single-agent implementation demonstrates the benchmark's applicability, establishing quantitative metrics for comparison of LLM-based planning and execution approaches.
Source: arXiv (Cornell University)
Authors: Jobs, Niklas, da Silva, Luis Miguel Vieira, Somashekaraiah, Jayanth, Weigand, Maximilian, Kube, David, Gehlhoff, Felix
Institutions: Siemens (Germany), Helmut Schmidt University
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Siemens
OpenAlex ID: W4417034780
Cites:
Cited by:
Related to: