AI Launch Profile
Evaluation Cards
The EvalEval Coalition beta-launched Evaluation Cards, an open-source reader over a normalized evaluation warehouse with card-level context and four interpretive signals: reproducibility, completeness, provenance and comparability.
At a glance
Launch Snapshot
- Company
- EvalEval Coalition
- Launch date
- June 11, 2026
- Launch type
- Not classified
- Category
- AI Infrastructure, AI Research Tools, Open-Source AI
- Audience
- AI Engineers, Data Analysts, Enterprises, Researchers
- Pricing
- The reader is publicly accessible and described as open source. No separate paid reader plan was found in the reviewed official material; support, bulk access and future service terms are not guaranteed.
- Free plan
- Yes
- API
- No
- Open weights/source
- Yes
Launch Context
Use these links to move from this record into the broader Launch Intelligence database.
Verification & Sources
- Evidence state
- Recheck due
- Source links
- 6
- Freshness
- Needs recheck: checked July 28, 2026
- Last updated
- July 28, 2026
What this evidence state means
- Definition
- The claim was previously checked, but its review window expired or a material change may have invalidated it.
- Required provenance
- The prior evidence and check date are retained, together with the expiry or change signal that triggered recheck.
- Owner
- Kingy freshness queue owner and assigned editorial reviewer
- Freshness rule
- This is already outside its freshness rule. It must not be presented as current until reviewed against current evidence.
- Disputes and corrections
- Use “Suggest a correction” on the record. Kingy editorial reviews the cited evidence, records material corrections, and changes or removes the state when it is not supported.
Key source checks
Suggest a correction
Kingy AI Take
Evaluation Cards is a useful antidote to treating benchmark scores as self-explanatory because it foregrounds reporting gaps and provenance. Its signals still depend on upstream normalization and source quality, and Kingy did not audit the warehouse or reproduce its June corpus counts. Use each card as a route to primary evidence, not as an independent benchmark verdict.
Who it is for
AI evaluators, researchers, model-governance and procurement teams, developers, journalists and policy practitioners who need to trace benchmark claims back to reporting context.
What feels promising
A common card surface and explicit reproducibility, completeness, provenance and comparability signals make missing evaluation context easier to spot before a model claim is reused.
What feels unproven
Kingy did not verify warehouse-wide identity resolution, correction latency, source coverage, the accuracy of every signal, contribution governance, bulk-access stability or the reproducibility of displayed results.
Source list
Sources
Related Kingy Links
Editorial submissions and sponsor-fit reviews are separate. Payment does not influence Kingy scores, verdicts, rankings, evidence labels, or publication decisions.