3-adic-vae-cytochrome-c

3-Adic Hyperbolic VAE — Cytochrome C Phylogeny Validation

Three trained conditions from docs/plans/PHYLOGENY-VALIDATION-PIPELINE.md (repo: https://github.com/gesttaltt/3-adic-ml), testing whether a p-adic / hyperbolic VAE architecture recovers real cytochrome c phylogeny (39 species, UniProt Pfam PF00034, NCBI taxonomy) better than simpler baselines.

Conditions

Fase 4 result: does embedding distance recover taxonomy?

raw_encoding_baseline = zero-model control (Euclidean distance on raw hydropathy-encoded aligned sequences, no VAE). Any condition must beat this to demonstrate the architecture learned something beyond conserved-sequence-implies-close-relative.

Full results with bootstrap CIs: phylogeny_recovery_results_full.json.

Follow-up: Condition D (taxonomy-conditioned), with a real species holdout

condition_D_taxonomy_conditioned/ tests a narrower question the table above left open: hyperbolic geometry is well-suited to trees, but nothing above ever pointed a hyperbolic loss at the real tree -- C pointed it at v_3(index) instead. Condition D uses a new TaxonomyGeodesicLoss (targets real inter-species taxonomic distance) and, for the first time in this repo, a genuine species-level holdout: trained on 30/39 species, evaluated separately on the 9 held out (stratified across kingdoms, never seen in training). See condition_D_taxonomy_conditioned/heldout_species.json for the exact list.

A no-encoder sanity check (39 free Poincare points fit directly against the taxonomy matrix, no VAE) scored 0.9057 -- confirming hyperbolic geometry can represent this tree well on its own. That advantage did not transfer once an encoder had to derive placement from the same collision-heavy 3-symbol hydropathy encoding used throughout this dataset (see index_collision in the results JSON). Bootstrap CIs at n=9 held-out species are wide ([-0.12, 0.87] vs. [-0.04, 0.94]), so this is best read as "no detectable held-out generalization advantage," not a definitive negative.

Full methodology and all four splits (held-in/held-out/mixed/overall): phylogeny_recovery_results_D_taxonomy_conditioned.json and TAXONOMY-CONDITIONED-EMBEDDING-PLAN.md in the source repo.

Loading

import torch
ckpt = torch.load("condition_C_padic/checkpoint.pt", weights_only=True)

See the source repo's src/models/vae.py (TernaryVAEV6Controllable) and src/models/vae_baseline.py (TernaryVAEEuclideanBaseline) for the model classes these checkpoints load into.