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,

scripts/warm_setup_cache.py

@@ -9,6 +9,7 @@ for the sidecar itself.
 
 from pathlib import Path
 
+from giant.constants import K_MAX
 from giant.pipeline import run_setup_stage
 
 
@@ -16,7 +17,8 @@ def run_warm_setup_cache(
     data: str,
     val_fraction: float = 0.1,
     seed: int = 0,
-    conditioning: str = "physical",
+    particle_conditioning: str = "physical",
+    material_conditioning: str = "physical",
     router_enabled: bool = False,
     router_type: str = "energy",
     n_experts: int = 4,
@@ -25,10 +27,12 @@ def run_warm_setup_cache(
 ) -> None:
     """Populate (or refresh) the setup cache sidecar for `data`.
 
-    `val_fraction`/`seed`/`conditioning` select the normalizer cache entry
+    `val_fraction`/`seed`/`particle_conditioning`/`material_conditioning`
+    select the normalizer cache entry
     (`giant.data.setup_cache.normalizer_key`) — pass the same values a later
-    `giant train` invocation will use so it hits this warmed entry.
-    `router_enabled`/`router_type`/`n_experts` only matter for
+    `giant train` invocation will use so it hits this warmed entry. The two
+    conditioning axes are independent (docs/v0.3.0-design.md §3.1) and may
+    differ. `router_enabled`/`router_type`/`n_experts` only matter for
     `router_type == "process"` (warms that `n_experts`'s process map); the
     energy-router quantile summary is always collected regardless, so a
     later `--router-type energy` run never needs to rescan just to seed
@@ -40,16 +44,17 @@ def run_warm_setup_cache(
         "n_experts": n_experts,
     }
     # A minimal v0.3 cfg — only the keys run_setup_stage actually reads
-    # (conditioning.particle.type, stage{1,2}_model.router). This CLI only
-    # ever configures one router (matching today's single --router-type
-    # flag), so it's placed on stage1_model; stage2_model's stays disabled.
+    # (conditioning.{particle,material}.type, stage{1,2}_model.router). This
+    # CLI only ever configures one router (matching today's single
+    # --router-type flag), so it's placed on stage1_model; stage2_model's
+    # stays disabled.
     cfg = {
         "conditioning": {
-            "particle": {"type": conditioning},
-            "material": {"type": conditioning},
+            "particle": {"type": particle_conditioning},
+            "material": {"type": material_conditioning},
         },
         "stage1_model": {"router": router_cfg},
-        "stage2_model": {"router": {"enabled": False}},
+        "stage2_model": {"router": {"enabled": False}, "k_max": K_MAX},
     }
     run_setup_stage(
         Path(data),