RST: certifying and constructing prescribed information in variational autoencoders
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Abstract (arXiv)
Posterior-collapse diagnostics can show that a VAE uses its latent representation, but they do not determine whether a specified information view crosses a representation/readout interface fixed before training. Reconstruction-Student-Teacher (RST) makes this stronger requirement explicit: Reconstruction supplies the VAE objective, the Student is a fixed witness, and the Teacher is a frozen information specification. We derive a teacher-relative certificate showing that a positive margin guarantees transmission of the declared information through the prescribed interface, while every input-independent representation lies at or below the boundary. A centered regular-simplex witness turns this certificate into an explicit construction: a minimum-dimensional closed-form teacher code, its complete affine solution fiber, witness-visible and witness-null routing, a margin-energy path, function-space departure from the centered input-independent point, preservation cylinders, and orthogonal multi-view composition. Experiments on five datasets test boundary crossing, stronger KL pressure, teacher counterfactuals, null routing, escape, preservation, cross-seed reuse, and cross-architecture reuse. On CIFAR-100, auxiliary learned readers remain natively decodable but fail under no-refit transfer, whereas the same fixed RST witness directly reads independently trained ConvVAE and ResVAE encoders. Affine adapters recover transferred auxiliary readers, localizing much of the failure to interface-coordinate mismatch rather than absence of information. These results support prescribed alignment as distinct from ordinary decodability.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 43 references that are in the map. First posted on arXiv 18 May 2026.
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31 of 43 references.
References · 31
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19 more
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