Inside the Frontier
Make frontier AI evidence comparable without pretending it is uniform.
Frontier lab reports contain useful evidence, but benchmark names can hide changes in prompts, tools, attempt budgets, graders, thresholds, and release context.
I built a grounded research system to compare how leading labs train, evaluate, and govern releases while keeping claims traceable to reviewed primary sources.
Build the evidence system before trusting the synthesis.
The workflow separates curation, chunking, structured extraction, comparison, and synthesis. Each evaluation occurrence is stored by lab and release, with protocol fingerprints that preserve how the evaluation was actually run.
The reader product retrieves from the reviewed source library and exposes source evidence instead of presenting model-generated synthesis as authority.
The same benchmark was often not the same test.
A shared benchmark name often concealed materially different testing conditions. That made comparability itself a product requirement: the Atlas should warn when two numbers should not be treated as a direct ranking.
Turn technical research into something a non-researcher can inspect.
The deployed Atlas combines high-level synthesis, grounded question answering, and structured exploration. A reader can move from conclusion to evidence without reading every source document first.
A research product that connects AI capability, evaluation, and governance.
The project demonstrates primary-source research, RAG and retrieval design, structured extraction, AI evaluation literacy, safety and governance analysis, evidence auditing, and technical communication.
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