The modern landscape of AI evaluation is a fragmented mess that successfully hides actual model performance behind marketing smokescreens. According to a preprint from the Evaluating Evaluations (EvalEval) coalition, nominally identical tests yield radically different results depending on the framework used. As researchers Yannic Kilcher, Sri Harsha Nelaturu, and their colleagues note, this chaos makes direct system comparisons impossible, inflates R&D budgets, and turns selecting a tech stack into a guessing game.
To bring order to this zoo, researchers from IBM Research, Stanford University, and Meta FAIR have introduced the Every Eval Ever project. This initiative offers a single, vendor-neutral data schema that packages test results into a standardized JSON format. Instead of scouring scattered tables in blog posts and PDF reports for crumbs of data, tech leads now have access to a centralized metadata repository, including generation parameters and execution conditions.
Key features of the new standard
Data Unification: Translating disparate reports into a single machine-readable format. Transparency of Conditions: Recording generation parameters that vendors often hide. Independence: The project is not affiliated with specific model developers.
"This project is more than just another attempt to create a standard; it is a tool for meta-analysis that will allow engineers and investors to choose solutions based on hard data rather than the ambitious promises of AI labs."
For businesses, this represents a long-awaited shift from "taking their word for it" to a verifiable frame of reference. The Every Eval Ever project aggregates data from various evaluation environments and academic papers to form a reproducible scientific foundation. Any attempt to manipulate test results by cherry-picking a convenient framework will now become obvious the moment it is cross-referenced with the common registry.