Start with the decision you need to make
A model report can help you choose a system to deploy, understand a research contribution, or discover a team to follow. Those are different reading tasks. Before comparing scores, write down the question you need the report to answer: whether a model handles your language, whether its weights can be deployed under your constraints, or whether its evaluation resembles the work you expect it to do. A high average score may answer none of those questions.
In this atlas, a report connects people and organizations to a durable public source. It is a starting point for investigation rather than a complete record of who contributed what. Begin with the canonical report, then use the author profiles to find related work and public explanations.
Separate the model, the procedure, and the test
Record the exact model variant, release date, and evaluation setup. A family name may cover several sizes, architectures, instruction-tuned variants, and reasoning settings. Results from different variants are not interchangeable. Prompt templates, tool access, sampling settings, and the amount of inference computation can change the result even when the checkpoint is unchanged.
Qwen3 is a useful worked example because its release describes thinking and non-thinking modes with a controllable reasoning budget. This makes a bare model name an incomplete experiment description. When reading a comparison, ask which mode was used and what resources each model received. The release describes capabilities; an independent deployment still needs its own workload evaluation.
Read the denominator and the failure cases
Check how many tasks and examples were evaluated, how success was defined, and whether repeated attempts were allowed. A result for one sample per problem measures something different from success after many attempts. An average may also hide a weak language, a difficult subgroup, or a class of failures that matters disproportionately in your application.
For an operational decision, supplement a benchmark with requests resembling your own. Track failures and resource use alongside success. A system that solves more tasks but needs frequent human recovery may be a poor fit for an unattended workflow. Keep the model's claimed capability separate from the reliability of the complete service.
Use authorship responsibly
Coauthorship establishes a connection to a report; it does not by itself identify the person who designed a particular method or owned a subsystem. For individual attribution, look for a contribution statement, an official biography, a project repository, or a public explanation from the team. Avoid turning an author list into a ranking of importance.
Affiliations also have dates. A researcher associated with a report may have moved, and a paper's institutional affiliation is not a verified current job. When an atlas label and a biography differ, follow the primary sources and their dates. Treat that disagreement as a reason to request a correction rather than as evidence of two simultaneous roles.
Finish with a short evidence note
A useful note contains the report version, the claim you care about, the relevant experiment, its limitations, and the next test you would run. Link the note to the report and to the public people or projects that explain it. This turns reading into an auditable decision instead of a collection of impressive numbers.
Use the Reports directory to choose a source and the People directory to follow its research context. If you cannot verify an individual role, leave it unresolved. A smaller set of well-supported connections is more useful than a detailed story assembled from assumptions.