analiza aiqbench
PCBWorld establishes a critical benchmark where agentic AI interacts directly with EDA engines like KiCad, moving beyond simulation to perform actual hardware design tasks under strict rule constraints. By leveraging Design Rule Check (DRC) feedback loops similar to human workflows, the system demonstrates that reinforcement learning agents can approach or surpass traditional rule-based routers in complex routing scenarios. This development signals a tangible shift toward autonomous engineering capabilities where AI tools execute native operations rather than merely generating code snippets for external execution.
PCBWorld: A Benchmark Environment for Engine-Grounded PCB Design Automation
arXiv:2607.05915v2 Announce Type: replace Abstract: PCB routing is the task of connecting the nets of a board with copper traces under strict design rules, yet learning-based methods still lag behind rule-based routers. We introduce PCBWorld, an open-source engine-grounded PCB routing environment built on KiCad, an electronic design automation (EDA) engine. As a human engineer does, agents in PCBWorld interactively route a board through the engine's native operations, guided by its Design Rule Check (DRC) feedback. The environment supports both RL and tool-using LLM agents. Alongside the environment, PCBWorld-Bench provides three board datasets in the native .kicad_pcb format, two controllable synthetic generators and 679 real open-source boards. It scores any completed board with eight engine-checked evaluation metrics, regardless of the routing method. In our experiments, agents in PCBWorld consistently outperformed grid-action RL policies and open-loop LLM baselines, and an RL policy trained only on synthetic boards transferred zero-shot to real boards, approaching rule-based routers.
Abstract:PCB routing is the task of connecting the nets of a board with copper traces under strict design rules, yet learning-based methods still lag behind rule-based routers. We introduce PCBWorld, an open-source engine-grounded PCB routing environment built on KiCad, an electronic design automation (EDA) engine. As a human engineer does, agents in PCBWorld interactively route a board through the engine's native operations, guided by its Design Rule Check (DRC) feedback. The environment supports both RL and tool-using LLM agents. Alongside the environment, PCBWorld-Bench provides three board datasets in the native .kicad_pcb format, two controllable synthetic generators and 679 real open-source boards. It scores any completed board with eight engine-checked evaluation metrics, regardless of the routing method. In our experiments, agents in PCBWorld consistently outperformed grid-action RL policies and open-loop LLM baselines, and an RL policy trained only on synthetic boards transferred zero-shot to real boards, approaching rule-based routers.
Accepted to the KDD 2026 Workshop on Evaluation and Trustworthiness of Agentic AI (non-archival). Main text with appendix
Artificial Intelligence (cs.AI)
I.2.6; I.2.8; B.7.2; J.6
arXiv:2607.05915 [cs.AI]
(or arXiv:2607.05915v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.05915 Focus to learn more arXiv-issued DOI via DataCite
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