Evaluation
Reproducible benchmarks for AI hardware design
PCBBench editorial · Published October 14, 2026 · Editorial policy
A reproducible hardware benchmark records the task, tool environment, allowed actions and independent acceptance tests. Another engineer should be able to identify what was evaluated and reproduce the grading without relying on a leaderboard screenshot.
Version the whole evaluation boundary
Track the input design, libraries, rule deck and any device models along with the grader. A source commit is insufficient if a remote catalog or local EDA library changes underneath it. Store hashes and dates for external artifacts where redistribution is not possible.
Separate candidate work from the oracle
Keep hidden answers and acceptance files outside the candidate's accessible workspace. Save the exact files submitted for grading. If an agent can alter a testbench, rule configuration or reference output, verify those artifacts before trusting the result.
Document what cannot be repeated exactly
Hosted models and external tools may change. Preserve route identifiers, run dates, settings and complete traces within the permitted disclosure boundary. Report these limitations directly instead of promising deterministic generation when only deterministic grading is available.
Practical checklist
- Version inputs and graders.
- Pin tool and library versions.
- Protect acceptance artifacts.
- Save outputs and failure logs.
- Distinguish repeatable grading from generation.
A concrete example
Release an evaluation of generated schematics.
- Evidence to accept
- A reviewer can regrade the submitted native files with the recorded environment.
- Evidence that is insufficient
- Only aggregate scores are retained, with no way to recover the accepted outputs.
Common questions
Must the model return the same circuit every time?
No. Reproducibility includes knowing the sampling policy and being able to verify each recorded artifact.
Can private tasks still be auditable?
Yes, through protected review and published aggregate methodology. Be explicit about which evidence is public and which is restricted.
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.
- Data contamination in AI PCB benchmarksReduce benchmark leakage with held-out designs, task families, provenance and controlled public examples.
- Pass rate, pass@k and reliability in PCB benchmarksUnderstand first-attempt success, repeated-attempt success and reliability without confusing their denominators.