Fix conditioning="physical" so it can actually generalize past training vocab

The whole point of conditioning="physical" is generalizing to a
species/material outside the training menu, but two independent code
paths still hard-required training-vocab membership:

- giant/data/transforms.py: build_cond_features unconditionally raised
  KeyError on an out-of-vocab pdg/material. _vectorized_map_lookup
  gains a strict=False mode (dummy index instead of raising), used only
  under conditioning="physical" where ConditionEncoder never reads
  cond_cat anyway; "embedding" mode is untouched and still raises,
  since cond_cat IS the conditioning signal there.
- giant/rollout.py: the known_pdg termination gate still killed a track
  on step 1 for any pdg outside pdg_map, regardless of conditioning
  mode. Now skipped entirely under conditioning="physical".
- giant/model/network.py: PdgRouter/ProcessRouter always build their
  own training-vocab nn.Embedding independent of conditioning, silently
  reintroducing the same limitation at the routing layer. build_models
  now raises loudly if conditioning="physical" is paired with either
  router type, rather than silently building a model that can't
  generalize the way it claims to.

This unblocks the held-out-species/material generalization experiment
against the multi-material dataset (see CLAUDE.md roadmap). Each fix
has a regression test, including an end-to-end rollout test seeded
with a resolvable-but-out-of-vocab PDG code.

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

tests/test_router.py

@@ -1,5 +1,6 @@
 """Tests for the mixture-of-experts routing prototype (giant/model/network.py)."""
 
+import pytest
 import torch
 
 from giant.constants import COND_DIM, K_MAX, SEC_DIM, X_DIM
@@ -484,6 +485,49 @@ def test_build_models_routed_with_pdg_router():
     assert stage1.router.pdg_emb.num_embeddings == 4
 
 
+def test_build_models_rejects_pdg_router_with_physical_conditioning():
+    """conditioning="physical" is meant to generalize beyond the training PDG
+    vocab; PdgRouter always uses a training-vocab nn.Embedding regardless of
+    conditioning, so the combination must raise rather than silently building
+    a model that can't actually generalize the way it claims to."""
+    model_config = dict(
+        pdg_vocab=4,
+        mat_vocab=2,
+        emb_dim=16,
+        dropout=0.1,
+        k_max=K_MAX,
+        expert_hidden_dim=16,
+        expert_n_blocks=2,
+        conditioning="physical",
+        router={"enabled": True, "type": "pdg", "n_experts": 3},
+    )
+    with pytest.raises(ValueError, match="physical"):
+        build_models(model_config)
+
+
+def test_build_models_rejects_composed_router_with_pdg_axis_and_physical_conditioning():
+    model_config = dict(
+        pdg_vocab=4,
+        mat_vocab=2,
+        emb_dim=16,
+        dropout=0.1,
+        k_max=K_MAX,
+        expert_hidden_dim=16,
+        expert_n_blocks=2,
+        conditioning="physical",
+        router={
+            "enabled": True,
+            "type": "composed",
+            "axis0_type": "energy",
+            "axis0_n_experts": 2,
+            "axis1_type": "pdg",
+            "axis1_n_experts": 3,
+        },
+    )
+    with pytest.raises(ValueError, match="physical"):
+        build_models(model_config)
+
+
 # ── ProcessRouter ────────────────────────────────────────────────────────────