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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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In Figure 3 with 1,000 MNIST training examples, the AEVB training marginal-likelihood curve rises faster than MCEM and the two approach similar displayed training endpoints; the displayed MCEM test curve finishes above AEVB, while wake-sleep has lower train and test curves than both near the end. · CiteArk

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In Figure 3 with 1,000 MNIST training examples, the AEVB training marginal-likelihood curve rises faster than MCEM and the two approach similar displayed training endpoints; the displayed MCEM test curve finishes above AEVB, while wake-sleep has lower train and test curves than both near the end.

Source: source-paper:PDF pp. 7-8; Section 5 'Marginal likelihood' and Figure 3, left panel (N_train=1000)

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Research Plan identity

Claim version: sha256:f794818dc811cdb83f6c63209e39250c785f32366f54888ff519155c9ab1a29a
Planned experiments: 1
Evidence objects: 0

The 1000-example Figure 3 package reconstructs all three algorithms, prescribed HMC evaluation, and the complete qualitative curve relations without inventing endpoint values.

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