Config defaults: dropout 0.1 -> 0.0, wandb false -> true. The v0.3.0 work
is a sequence of architecture comparisons, and an unlogged run is not
comparable, so W&B is on unless explicitly disabled.
Restructure the open-questions section into settled / deferred / tracked /
still-open, since most of it is now decided:
- other_policy three-way switch and separate flow/ddpm sub-tables are
approved as specified.
- stop_token is schema-valid but raises "not implemented in v0.3.0";
charge conservation gets no key at all and is left deliberately
undesigned, to be worked out on its own terms rather than pre-shaped by
this refactor. The speculative charge-mask sketch is removed.
- Logging the L1 decode-distance distribution under target = "embedding"
becomes tracked implementation work, landing with the rollout decode.
- estimate_batch_size recalibration becomes implementation step 8, last:
the activation-memory profile is not knowable until the AR trunk and
history encoder are final, so it is measured on real hardware with the
example configs rather than guessed.
Only the differentiability question for Jan remains genuinely open.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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