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_transforms.py

@@ -574,6 +574,51 @@ def test_vectorized_map_lookup_raises_keyerror_on_missing_value():
         _vectorized_map_lookup(values, mapping)
 
 
+def test_vectorized_map_lookup_strict_false_dummy_indexes_unmapped_values():
+    """strict=False must leave found values untouched and only dummy-index
+    (0) the unmapped ones — never raise, and never disturb a value that IS
+    in the mapping (e.g. one that happens to map to a nonzero index)."""
+    mapping = {1: 5, 2: 7}
+    values = np.array([1, 99, 2, 100])
+    result = _vectorized_map_lookup(values, mapping, strict=False)
+    np.testing.assert_array_equal(result, [5, 0, 7, 0])
+
+
+def test_build_cond_features_physical_mode_tolerates_out_of_vocab_pdg_and_material():
+    """conditioning="physical" must not KeyError on a pdg/material outside
+    the training-dataset vocab (mat_map/pdg_map) — that's the entire point
+    of the mode (see giant.rollout's known_pdg gate for the paired fix).
+    "embedding" mode must still raise, since cond_cat IS the conditioning
+    signal there. Note this is specifically about the dataset-scoped
+    vocab index, not giant.materials' physical-properties table — a
+    material must still be a real, known Geant4 material (e.g. "G4_Pb",
+    just not one *this* mat_map happened to include) for "physical" mode
+    to derive its Z_eff/A_eff/density/X0/λ_int; a genuinely unknown
+    material name correctly still raises via giant.materials, same as the
+    documented G4_LYSO precedent — that's a separate, intentional guard."""
+    pdg_map = {11: 0, 22: 1}
+    mat_map = {"G4_AIR": 0}
+    data = {
+        "pre_pos": np.zeros((1, 3), dtype=np.float32),
+        "pre_E": np.array([10.0], dtype=np.float32),
+        "pre_dir": np.array([[0.0, 0.0, 1.0]], dtype=np.float32),
+        "layer_id": np.array([0], dtype=np.int32),
+        "pdg": np.array([13], dtype=np.int64),  # not in pdg_map
+        "material": np.array(["G4_Pb"], dtype=object),  # not in mat_map
+        "mass": np.array([105.7], dtype=np.float32),
+        "charge": np.array([-1.0], dtype=np.float32),
+    }
+
+    cond_cont, cond_cat = build_cond_features(
+        data, pdg_map, mat_map, conditioning="physical"
+    )
+    assert cond_cont.shape[-1] == COND_DIM
+    np.testing.assert_array_equal(cond_cat, [[0, 0]])  # dummy indices, no raise
+
+    with pytest.raises(KeyError):
+        build_cond_features(data, pdg_map, mat_map, conditioning="embedding")
+
+
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