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AI is dramatically lowering the cost of reading papers, writing code, and running experiments. But as research output explodes, telling which results can be trusted keeps getting harder. CiteArk connects the claims in scientific papers with their experimental conditions, execution process, result evidence, and verification records, so researchers and agents can verify claims, reuse experimental results, and build follow-up research on them instead of repeating work. We hope these trustworthy research records will grow into a scientific foundation that researchers, labs, and agents can all build on together.

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For 1/f^1 targets, the best sparse random-feature models match the performance of the same MLP using dense Fourier features. · CiteArk

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

Not assessedPlan blockedFindingsparse-dense-match-1d-10

For 1/f^1 targets, the best sparse random-feature models match the performance of the same MLP using dense Fourier features.

Source: paper:Figure 4(b) and caption, p. 7; §5 p. 6

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Runs (0)

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Research Plan identity

Claim version: sha256:b65f8c818bc0f1f7596a416b8407cecaefe4811eda76ae30ace5b1976705dafe
Planned experiments: 0
Evidence objects: 0

A concrete investigation is retained in distribution-1d-10, including necessary controls, scientific inputs, prospective observations and full finite quotes. Only the automatic qualitative verdict is blocked; this does not establish the route is operationally infeasible. Preserve evidence for independent assessment.

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