TO-Agents: A Multi-Agent AI Framework for Subjective Preference-Guided Topology Optimization

TO-Agents is a multi‑agent AI framework that translates natural‑language design intent into iterative topology optimization. It converts a human problem description into solver inputs, runs the optimizer, renders 3D topologies, and employs a judge agent to critique and revise results using multiview vision‑language reasoning. Evaluated on a cantilever beam and a phone‑stand design, the system achieved preference‑aligned designs in 60% of trials, outperforming an ablated pipeline by up to six times and enabling end‑to‑end intent‑to‑prototype design with additive manufacturing.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• agents
• multimodal
• benchmarks