Fresh suite
Seeded generators vary physical parameters while preserving the governing equations.
tasks.private.jsonlOhmJudge builds a fresh, committed suite of EE calculations, collects answers from any web interface without exposing the answer key, and grades values and units later with local Python rules.
Each step creates a plain file. The private answer key stays separate from the public prompts; the final audit recomputes every verdict instead of trusting the report.
Seeded generators vary physical parameters while preserving the governing equations.
tasks.private.jsonlSHA-256 records the exact suite before model answers are collected.
prompts.public.jsonlUse the answer-free collector, an optional API adapter, or a replayed response set.
responses.jsonlNormalize units, apply declared tolerances, and classify concrete failure modes.
grades.jsonlCheck protocol metadata, coverage, duplicates, and deterministic recomputation.
audit.jsonOhmJudge avoids the false precision of a universal intelligence score. It answers one reproducible question about scalar EE calculations and exposes the evidence behind the answer.
Fresh parameters reduce exact-answer memorization. They do not make familiar formulas or task structures secret.
The project is not the first EE benchmark. Its practical contribution is the combination of user-generated suites, a no-API capture lab, unit-aware local grading, and an artifact-level audit.
| Approach | Best at | Typical requirement | OhmJudge distinction |
|---|---|---|---|
| EEE-Bench / CIRCUIT | Broad curated EE knowledge and multimodal questions | Static published datasets | Fresh seeded scalar suites and local collection |
| PCB-Bench / EDA tasks | Realistic board, layout, and design artifacts | EDA infrastructure and heavier verification | Runs on a laptop; deliberately narrower construct |
| General evaluation frameworks | Many providers, datasets, and judge types | Configuration, APIs, or external services | Purpose-built browser workflow for an EE researcher |
| OhmJudge | Repeatable scalar EE calculation studies | Python 3.11 and a browser | Fresh suite + capture protocol + deterministic audit |
This positioning is a testable product hypothesis, not a claim of unique academic novelty. Independent studies, task-pack contributions, and reproduced results are the evidence that would validate it.
The present release targets introductory-to-intermediate numerical reasoning and unit discipline. It does not infer professional hardware-design competence.
Dividers, filters, op-amps, Thevenin models, series networks.
RMS, sampling, aliases, energy, and convolution.
Poles, overshoot, settling, stability, and gain.
Capacity, path loss, noise, symbols, and link budgets.
Diodes, BJTs, MOSFETs, ADCs, and slew rate.
Three-phase loads, transformers, energy, PF, and batteries.
Waves, fields, capacitance, magnetic flux, and inductance.
Timing, clock limits, counters, encodings, and distance.
UART, PWM, timers, buffers, and I²C transfers.
Shunts, sensors, uncertainty, dB, and bridge outputs.
A score is useful only inside the construct actually measured.
The standalone collector contains public prompts and the suite commitment, but no expected values, tolerances, scores, network calls, or grading code. The separate report remains synthetic and makes no model-ranking claim.