A4E is an open research initiative focused on AI-assisted engineering design, initiated and led by Bin Hu from ECE at UIUC. We focus on developing benchmarks, agents, and tools that advance engineering intelligence across multiple domains such as control systems, integrated circuits, computer architectures, structural engineering, mechanical/aerospace systems, signal processing, and others.
Developing tools to evaluate and improve AI for engineering.
AI-generated designs undergo rigorous testing through executable simulations and validation against physical data, moving beyond textbook knowledge to genuine engineering capability.
Spanning control systems, analog integrated circuits, digital hardware, mechanical systems, robotics, signal processing, structural design, and more.
All benchmarks, code, and datasets are open-source, enabling the research community to build upon and extend this work.
We work on four main areas.
Developing rigorous benchmarks that measure AI capabilities in practical engineering design tasks. Moving beyond textbook levels to open-ended design problems with simulation-based verification and physical real data.
Building LLM-based agents that can autonomously navigate the engineering design process, from requirement analysis through iterative refinement to final validation.
Connecting LLMs with engineering software ecosystems such as MATLAB, SPICE, CAD tools, and domain-specific simulators, while ensuring correctness and safety through formal verification and robust validation pipelines.
Finetuning and developing specialized language models tailored for engineering domains, enabling efficient on-premise deployment while preserving proprietary design knowledge.
Representative Projects pushing toward engineering AGI.
The team behind A4E.
Plus 50+ collaborators across multiple institutions. See individual project pages for full author lists.
Launch of the AGI4Engineering open research initiative website.
EngDesign benchmark officially accepted to the NeurIPS 2025 Datasets & Benchmarks Track.