v0.3.0 post-implementation audit: resolve all 9 tracked discrepancies

Works through docs/v0.3.0-followups.md item by item, closing the gap
between the design doc and the shipped v0.3.0-stage2-autoregressive code:

1. validate.py: 7-tuple batch unpacking, sample_stage1/sample_stage2
   dispatch, stage-2 particle-type-class marginal.
2. Stage-prefixed --stage1-*/--stage2-* CLI flags for train/new-run.
3. Thread stage2_model.k_max through loader/transforms/dataset/pipeline/
   train instead of the hardcoded K_MAX constant.
4. Mixed conditioning.particle.type / conditioning.material.type support
   end-to-end (data pipeline + dwarf warm-cache).
5. conditioning.share_stages = true: one shared ConditionEncoder instance
   across both stages.
6. stage2_model.generator = "ddpm" formally deferred into design doc §11.2
   (was silently unimplemented).
7. giant predict/rollout: implement conditioning.*.type = "onehot" via the
   checkpoint's saved pdg_topn_map/mat_topn_map.
8. network.py's checkpoint-path model_config migration now fails loudly on
   non-zero legacy expert_hidden_dim/expert_n_blocks, matching config.py's
   TOML-load path (§4.2).
9. validate_config now rejects stage2_model.n_sec.mode = "truth" for a
   rollout-capable checkpoint (§9).

Also cleared all pre-existing `ty check` noise (44 -> 0 diagnostics),
mostly a test-helper dict-unpack pattern that made every unrelated
constructor keyword look like a type error.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

docs/v0.3.0-design.md

@@ -892,6 +892,13 @@ All in `giant/config.py`:
   undesigned:** the mechanism should be worked out on its own terms when it is
   taken up, not pre-shaped by choices made for this refactor. Adding the block
   later is a config addition, not a break.
+- **`stage2_model.generator = "ddpm"`.** The value is **accepted by the
+  schema** (§3.3 lists `"flow" | "ddpm" | "wgan"` with no caveat) but
+  `FlowDDPMStageTrainer.__init__` (`giant/train.py`) raises
+  `NotImplementedError` for stage 2 — only `"flow"` and `"wgan"` have a
+  stage-2 secondary-decoder loss implemented. `stage1_model.generator =
+  "ddpm"` is unaffected; this restriction is stage-2-only. Landing stage-2
+  ddpm later is a trainer addition, not a config break.
 
 ### 11.3 Tracked as implementation work
 

docs/v0.3.0-followups.md

@@ -0,0 +1,37 @@
+# v0.3.0 — post-implementation audit: open discrepancies
+
+**Status:** steps 1–7 of `docs/v0.3.0-design.md` §12 are implemented (branch
+`v0.3.0-stage2-autoregressive`, commits `eb6dd27`..`200c6d2`). This document
+tracked discrepancies found between that implementation and the design contract
+during a 2026-08-07 audit, as concrete work items. **All items (1-9) are now
+resolved** — either implemented (1-5, 7-9) or explicitly deferred into
+`docs/v0.3.0-design.md` §11.2 (6: `stage2_model.generator = "ddpm"`). Step 8
+(`estimate_batch_size` recalibration) was intentionally still outstanding per
+§12 and was never tracked here.
+
+---
+
+## Confirmed correct during the audit (no action needed)
+
+For reference — these were explicitly checked against the design doc and
+match it, including two spots the doc itself flagged as likely stale that
+turned out fine:
+
+- Config schema, `DEFAULT_CONFIG`, `migrate_config` table (§4), `save_config`/
+  `merge_cli_overrides` recursion, `default_out_dir_name`, `Conditioning` enum,
+  `n_sec.mode = "stop_token"` error (§9, §11.2).
+- `network.py`'s full class decomposition (§5.3), dict-returning
+  `build_models`/`build_critics` (§5.4), `ExpertTrunk` separate in/out dims,
+  ST-Gumbel wiring (§2.1), AR token layout (§6.1), Markov/Attention history
+  encoders (§6.2).
+- Shared PDG top-N type map (one map, not two, per §8), `other_policy`
+  sample/modal/drop (§11.1), embedding L1-nearest decode, and the L1-distance
+  diagnostic surfaced in `giant analyze` (§11.3).
+- `analysis/render.py`/`analysis/router_gating.py` correctly branch
+  old-flat vs new-nested `model_config["router"]` location — doc flagged this
+  as a likely stale spot (§10) but it's actually fine.
+- `train.py`'s per-stage trainers, mixed flow+wgan runs, WGAN critic cadence,
+  per-stage router auxiliary losses, stage-prefixed metrics, stage-2-only
+  training via ground-truth `x1_s1` (§7).
+- `pipeline.py`'s deleted wgan+router rejection, per-stage `centers_init`
+  seeding, removed stale expert-size warning (§9, §10).