AI system benchmarking: compare complete realizations
Compare AI systems across quality, robustness, latency, memory, cost, dependencies and failure behavior.
A system benchmark compares deployable realizations, not only checkpoints executed under different conditions.
A comparison without normalized hardware, runtime, context, concurrency and cost creates misleading economic conclusions.
The protocol must freeze common conditions and publish remaining differences as experimental variables.
What the architecture must make explicit
Evaluating an AI system binds a capability, an exact object, a protocol, observations and a decision rule. An isolated score is not a qualification.
Move from a keyword to an inspectable engineering decision
Contract
Define required behavior, forbidden failures and thresholds before testing.
Separate
Keep search, confirmation, protected data and Fresh Final apart to prevent contamination.
Measure
Record outputs, metrics, failures, latency and measured-subject identity.
Adjudicate
Interpret observations without letting the candidate producer impose the verdict.
A claim is only as strong as its binding
The gate must evaluate the exact output produced by the exact realization. Integrity, scientific validity and authority are related, but never interchangeable.
Questions that change the design
What does ai system benchmarking: compare complete realizations require?
A system benchmark compares deployable realizations, not only checkpoints executed under different conditions. Evaluating an AI system binds a capability, an exact object, a protocol, observations and a decision rule. An isolated score is not a qualification.
Which evidence should be retained?
At minimum: exact realization identity, inputs and outputs, protocol version, measurements, failure cases and the decision rule. The protocol must freeze common conditions and publish remaining differences as experimental variables.
Does AMF prescribe a particular model?
No. AMF treats the model, runtime, data, tools and deterministic components as candidate materials. Their retention depends on measured contribution and declared constraints.