Evaluation
Data contamination in AI PCB benchmarks
PCBBench editorial · Published October 14, 2026 · Editorial policy
Benchmark contamination occurs when evaluation answers or closely related tasks are available through training data or the run environment. Reduce this risk with provenance tracking, protected holdouts and tests that require applying requirements to new designs.
Separate public demonstrations from evaluation evidence
Public tasks help people understand the format and reproduce grading. They should not be treated as proof of unseen-task generalization. Keep the split policy visible and report performance by split when the evaluation supports that comparison.
Look beyond exact file duplication
A renamed board can preserve the same circuit, layout or answer structure. Group related designs and variants together when constructing splits. Record upstream sources and transformations so a near-duplicate cannot accidentally appear on both sides of the boundary.
Use leakage checks as signals, not guarantees
Canary strings and environment audits can reveal some contamination paths. They cannot prove that a model has never seen related information. Combine them with new task construction, protected grading and a cautious interpretation of unusually easy public examples.
Practical checklist
- Track upstream provenance.
- Group related task variants.
- Protect holdouts and acceptance data.
- Audit candidate workspace access.
- Report public and held-out evidence separately.
A concrete example
Create two cosmetic variants of an open-source board.
- Evidence to accept
- Both stay in the same task family and split.
- Evidence that is insufficient
- One variant is public training material while its electrically identical sibling is labeled unseen.
Common questions
Does a canary prove a benchmark is uncontaminated?
No. A positive match can be informative, but absence of a match does not establish absence of leakage.
Should hidden tasks be published for SEO?
No. Publish methodology and representative public examples while preserving the actual holdout boundary.
Primary references and further reading
Use the matching device documentation and project configuration when applying this checklist. These references explain the underlying tools and evaluation approaches.
How this fits PCBBench
The Electronics reasoning benchmark describes the proposed inputs, outputs and checks for this capability. The pilot snapshot contains partial measured attempts; it is not a finished ranking.
Related reading
- How to evaluate an AI PCB design toolA practical evaluation protocol for AI PCB tools: native files, electrical checks, routing, cost and reproducibility.
- How to measure the cost of AI PCB designMeasure AI PCB design cost using all attempts, tool expenses, human corrections and successful completion.
- Reproducible benchmarks for AI hardware designDesign a reproducible hardware benchmark with versioned inputs, pinned tools, independent graders and auditable attempts.
- Pass rate, pass@k and reliability in PCB benchmarksUnderstand first-attempt success, repeated-attempt success and reliability without confusing their denominators.