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PCBRouteAgent posts mixed clean-pass rates on PCBWorld-Bench

Original title: PCBRouteAgent: How Close Are LLM Agents to Becoming PCB Routing Engineers?

Hugging Face daily papersby Yue Zhang, Jilei Sun, Liqiang Jing ()1 min read
AI summary · x-ai/grok-4.7Summaries by Vani AI

On 247 boards it reports 100%, 94.9%, 100%, and 40% clean-pass rates, ahead of baselines on three real-world subsets.

PCBRouteAgent is a multimodal PCB routing agent that pairs a Global Routing Planner with a Detailed Routing Executor using KiCad's native engine and bounded local recovery. Unresolved failures return as structured feedback so guides can be revised without discarding accepted routes. On 247 synthetic and real-world boards from four PCBWorld-Bench splits, clean-pass rates are 100%, 94.9%, 100%, and 40% on D2, D3-A, D3-B, and D3-C. On three real-world subsets it beats the best evaluated baseline by 13.9, 36, and 10 percentage points.

Unconfirmed: Clean-pass rates and baseline gaps are the paper's own evaluation results.

Key facts
  • Evaluation covers 247 boards in four PCBWorld-Bench splits.
  • D3-C clean-pass rate is 40%, the weakest split.
  • Executor uses KiCad's native routing engine.
  • Gains versus the best baseline are 13.9, 36, and 10 points.
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From the original article

1 min read at huggingface.co

Automating printed circuit board (PCB) routing requires coordinating board-level connectivity decisions with geometric execution under strict design rules. Although recent agents can interact with native routing tools, reliable routing on complex boards remains challenging. We present PCBRouteAgent, a multimodal PCB routing agent inspired by how human engineers coordinate global strategy with local execution.

Hugging Face daily papers

By Yue Zhang, Jilei Sun, Liqiang Jing · huggingface.co

Read the full article at Hugging Face daily papers

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.