Contents

The Core is Duino itself — read these in order:

• Layout — Track A, the ensemble representation. Four layers: Hierarchy and Groupings (the invariant core), Heterogeneity (the three regimes R1/R2/R3) and Materialization (the three storage modes A/B/C).
• Forward-operator infrastructure — Track B. The operator records that turn an ensemble into an experimental likelihood, and the registry/storage discipline around them.
• Evaluation model — how scope-local descriptors compose into rendered coordinates: the discrete-nesting and continuous-additive stacks, independence vs nesting vs provenance, sample-axis classification, Mode C operator interfaces, cross-backend integration.

Prerequisite:

• Primer — Boltzmann ensembles, marginals vs joints, qFit, forward models, and the Bayesian posterior-sampling picture the Core takes as given. Start here if the conformational-ensemble framing is new.

Secondary — equally part of the mission, but supporting the Core rather than constituting it:

• Problems with mmCIF and the current ecosystem – single-structure assumption, parsing fragility, missing experimental linkage, no ML-native access.
• Annotations and the artifact engine – query language, the (selector, body, provenance) annotation overlay, ML-native featurization passes, training-data shapes.
• Technology survey – Zarr/OME-Zarr/copick/TileDB/Arrow/DataFusion as storage primitives, the three heterogeneity regimes mapped onto existing tools (H5MD, cryoDRGN, qFit, IHM).
• Open questions
• ML-native data: access patterns and precomputed features – random access, neighbor lookup, equivariant feature caches, tokenization, dataloader chunking.
• Appendix: “black-box” data and model-side artifacts – weights, decoders, latents, model outputs, training metadata co-located with the structure.
• Appendix: modularity examples in software ecosystems – LLVM, DataFusion, AnnData, ECS, ONNX, OpenTelemetry, CF Conventions.