Icarus A unified multi modal curriculum dataset for evolutionary neural architecture search. Every row is one self contained Task = {meta, support, query} , where support and query are lists of (input Field, output Field) pairs. The inner loop trains on support ; fitness is scored on query . Support is non empty for every task. Encoders read the Field descriptor (axes, value type, n classes, value range, mask); mask is True where a value is padding/ignored. meta.class names , when present, names the target classes (e.g. fsd50k sound events); it is viewer/eval metadata only, never read by the loader or encoder, so the dataset stays structural. Built by the generator repo https://github.com/ArdeaAI/Icarus Dataset, which also holds the explorer ( uv run explore ), the build pipeline, and the full reference tooling. This card and the one self contained file shipped beside the data ( icarus.py : the loader plus a reference encoder, no dependencies on this repo) are everything you need. Rungs (18 rung difficulty ladder; one config rung each) split is how support/query is formed: native rungs ship a built in split (ARC train/test, XOR's degenerate support==query) and the loader keeps it a…
Runs entirely in your browser via DuckDB-Wasm — this dataset's real data file is loaded once, then queried locally. Nothing is sent to a server.
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