Design contract for the v0.3.0 config break and network.py refactor,
following the 2026-08-04 meeting with Jan. Not implemented yet.
The 2026-08-03 WGAN rollout benchmark failed specifically at the
secondary-species level (zero photons, ~4M hallucinated -14 muon
antineutrinos). The response pivots Stage 2 to autoregressive generation
in descending-energy order with teacher forcing, and reverts the particle
type to a categorical representation.
That needs a config break: [conditioning] / [stage1_model] /
[stage2_model] / [train] replace the single global train.mode and
[model] block, so per-stage generators (stage1 flow + stage2 wgan),
stage-2-only training, and one-shot-vs-autoregressive comparison all
become expressible. The particle and material conditioning axes are
configured independently and mix freely, each with physical / embedding /
onehot modes; the stage-2 type target mirrors the same three names, with
conditioning.particle.emb_dim sizing both so the two share one class map.
network.py collapses from ten permutation classes (stage x objective x
routed) into composable parts — encoder x trunk x objective — which also
makes routed WGAN work for the first time; it was only ever rejected
because no routed WGAN generator class existed.
The doc specifies every config option, the v0.2 migration (shim for both
configs and checkpoints, gated on a bit-identical output diff), the
refactor, and the implementation order.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
lars
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