Rate-adapting Poisson model

Identity Notes & Concept Scope Scoped to Gehler, Holub and Welling's ICML2006 RAP construction with fixed finite integer binomial trial counts. This differs from a Gaussian-hidden count harmonium, a two-unbounded-Poisson-layer construction, categorical word-bin EFH and replicated-token Softmax. Baseline Poisson rates and hidden logits are conditional parameters, not generally marginal means or independent hidden priors. The latent count model is distinct from its CD fitting algorithm or a trained retrieval system. No normalized identity matched four candidates against all 442 pinned AIO IDs, labels and scoped synonyms or 444 preceding native IDs, names and aliases. Full-checkout searches included ignored and hidden files in both checkouts; five matching lines per checkout came from two unique prior discovery-ledger contents, and all matching excerpts were read. All 205 semantic label/synonym hits were byte-verified against prior readings. Twenty-nine annotated AIO nodes and outgoing edges comprise 28 unchanged prior readings and one newly read multimodal-deep-learning node. Thirty-nine contextual YAMLs comprise 34 unchanged prior readings and five fully read current records. Three candidates are curated; the unbounded-count DWH specialization remains deferred because the inspected joint is not normalizable with nonzero count couplings. Absence is bounded to this pinned snapshot and maintained corpus.