Agentic Design of Compositional Machines
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.
68f1c78c6e0bef323a68feb6
Wenqian Zhang
68f1c78c6e0bef323a68feb7
Weiyang Liu
claimed_verified
2025-10-18T04:49:18.075Z
648905d1a15c43c791d4381f
https://cdn-avatars.huggingface.co/v1/production/uploads/648905d1a15c43c791d4381f/GpqGBzsLiMHX0gWZEz3qn.jpeg
Weiyang Liu
user
wy1iu
68f1c78c6e0bef323a68feb8
Zhen Liu
Agentic Design of Compositional Machines
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.
State-of-the-art LLMs are benchmarked in a machine design testbed, BesiegeField, highlighting the need for reinforcement learning to improve spatial reasoning, strategic assembly, and instruction-following capabilities.
large language models compositional machine design BesiegeField spatial reasoning strategic assembly instruction-following reinforcement learning cold-start dataset RL finetuning