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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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Large sampled-frequency scale in 2D image regression overfits: training fit improves while test performance worsens, and interpolation becomes noisy. · CiteArk

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

Not assessedPlan blockedFindingoverfit-2d

Large sampled-frequency scale in 2D image regression overfits: training fit improves while test performance worsens, and interpolation becomes noisy.

Source: paper:§A.4 p. 14; Figure 9(a,b) and Figure 10

Reported and observed measurements

No structured measurement is attached to this Claim.

Assessments (0)

No immutable Assessment has been published for this Claim yet.

Runs (0)

No execution has been linked to this Claim yet.

Research Plan identity

Claim version: sha256:928e64c6e4b81ee40c0f7edb6e3e425f8895c853caed0ff0d8e8cb53ca25ed1f
Planned experiments: 0
Evidence objects: 0

A concrete investigation is retained in distribution-2d, 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.

Machine-readable Claim

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