Agentic Design of Compositional Machines
Authors: name: Wenqian Zhang, name: Weiyang Liu, name: Zhen Liu

The design of complex machines stands as both a marker of human intelligence
and a foundation of engineering practice. Given recent advances in large
language models (LLMs), we ask whether they, too, can learn to create. We
approach this question through the lens of compositional machine design: a task
in which machines are assembled from standardized components to meet functional
demands like locomotion or manipulation in a simulated physical environment. To
support this investigation, we introduce BesiegeField, a testbed built on the
machine-building game Besiege, which enables part-based construction, physical
simulation and reward-driven evaluation. Using BesiegeField, we benchmark
state-of-the-art LLMs with agentic workflows and identify key capabilities
required for success, including spatial reasoning, strategic assembly, and
instruction-following. As current open-source models fall short, we explore
reinforcement learning (RL) as a path to improvement: we curate a cold-start
dataset, conduct RL finetuning experiments, and highlight open challenges at
the intersection of language, machine design, and physical reasoning.