AI Research Release Evaluation Framework

AI Research Release Evaluation Framework

Decision first

A review starts with a decision, not an announcement. The reviewer should state the task, current baseline, acceptable failure cost, protected data, required latency, intended users, and the smallest useful test.

Evidence chain

The claim should be traced from the launch language into the paper, benchmark design, comparator, ablations, and limitations. Every runnable element should be inventoried separately, including code, weights, data, configuration, demo, API, hardware requirement, and dependency.

Rights are a separate gate. A public repository can contain files under different terms. Model weights, data, code, outputs, trademarks, and third-party dependencies may each have distinct restrictions.

Reproduction should target the narrowest decision-relevant result. A team does not need to rebuild an entire paper before learning that the artifact cannot run in its environment or does not match its data.

Field-test record

The field test needs a representative sample, leakage-resistant split, baseline, error categories, human-review rule, security boundary, stop condition, and named owner. High average performance does not answer whether a rare failure is unacceptable.

Lifecycle decision

The final record should distinguish explore, pilot, adopt, monitor, and stop. It should capture repository state, last release, unresolved issues, support expectations, update path, and exit plan.

Passing this framework is not a safety certification, legal opinion, or production approval. It means only that the evidence is clear enough for the next accountable decision.