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>

giant/data/setup_cache.py

@@ -96,10 +96,22 @@ def fingerprint_files(files: list[Path]) -> list[list]:
     return out
 
 
-def normalizer_key(val_fraction: float, seed: int, conditioning: str) -> str:
+def normalizer_key(
+    val_fraction: float,
+    seed: int,
+    particle_conditioning: str,
+    material_conditioning: str,
+) -> str:
     # .6g avoids float-repr drift (e.g. 0.1 vs 0.10000000000000002) causing
-    # spurious cache misses between runs with the "same" val_fraction.
-    return f"valfrac={val_fraction:.6g}_seed={seed}_cond={conditioning}"
+    # spurious cache misses between runs with the "same" val_fraction. The two
+    # conditioning axes are independent (docs/v0.3.0-design.md §3.1) and both
+    # affect which cond_cont columns are computed for real vs. zero-filled
+    # (giant.data.transforms._physical_cond_columns), so both must be part of
+    # the key or two mixed-axis runs could collide on the same cache entry.
+    return (
+        f"valfrac={val_fraction:.6g}_seed={seed}_pcond={particle_conditioning}"
+        f"_mcond={material_conditioning}"
+    )
 
 
 # Top-N-map axes (docs/v0.3.0-design.md §8): "pdg" keys match pdg_map's int