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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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Increasing the number of latent variables, including apparently superfluous variables, did not produce overfitting in the Figure 2 experiments; the paper attributes this to regularization by the variational lower bound. · CiteArk

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Increasing the number of latent variables, including apparently superfluous variables, did not produce overfitting in the Figure 2 experiments; the paper attributes this to regularization by the variational lower bound.

Source: source-paper:PDF p. 7; Figure 2 caption and Section 5 'Likelihood lower bound' paragraph

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Research Plan identity

Claim version: sha256:973577a0c175a336cffd0f6e6321babb2358d691ab5fea86facacda1bbc270e7
Planned experiments: 2
Evidence objects: 0

The Figure 2 packages retain train/test gap trajectories across all nine configurations. Because the source defines neither an overfitting statistic nor a causal test for regularization, the automated gap count is explicitly descriptive and the qualitative conclusion remains available for independent assessment.

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