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>

scripts/dwarf.py

@@ -522,10 +522,21 @@ def warm_cache(
             "--seed", "-s", help="Must match the `giant train` run(s) to warm for"
         ),
     ] = 0,
-    conditioning: Annotated[
+    particle_conditioning: Annotated[
         Conditioning,
         typer.Option(
-            "--conditioning", help="Must match the `giant train` run(s) to warm for"
+            "--particle-conditioning",
+            help="Must match the `giant train` run(s)' conditioning.particle.type "
+            "to warm for",
+        ),
+    ] = Conditioning.physical,
+    material_conditioning: Annotated[
+        Conditioning,
+        typer.Option(
+            "--material-conditioning",
+            help="Must match the `giant train` run(s)' conditioning.material.type "
+            "to warm for — independent of --particle-conditioning "
+            "(docs/v0.3.0-design.md §3.1: the two axes may differ)",
         ),
     ] = Conditioning.physical,
     router: Annotated[
@@ -552,15 +563,17 @@ def warm_cache(
     """Precompute `giant train`'s setup-stage sidecar for `data` ahead of time.
 
     Warms the vocab maps, event-id split index, and the normalizer entry for
-    the given --val-fraction/--seed/--conditioning, so a later `giant train`
-    run (or a `dwarf hparam-scan` sweep, which shares one such entry across
-    every run) skips straight to training. See giant/data/setup_cache.py.
+    the given --val-fraction/--seed/--particle-conditioning/
+    --material-conditioning, so a later `giant train` run (or a `dwarf
+    hparam-scan` sweep, which shares one such entry across every run) skips
+    straight to training. See giant/data/setup_cache.py.
     """
     run_warm_setup_cache(
         data=str(data),
         val_fraction=val_fraction,
         seed=seed,
-        conditioning=conditioning.value,
+        particle_conditioning=particle_conditioning.value,
+        material_conditioning=material_conditioning.value,
         router_enabled=router,
         router_type=router_type,
         n_experts=n_experts,