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>

tests/test_network.py

@@ -1,5 +1,8 @@
+import copy
+
 import pytest
 import torch
+from giant import config as gconfig
 from giant.constants import CONT_SLOT_DIM, COND_DIM, PARTICLE_PHYS_DIM, SEC_SLOT_DIM
 from giant.model.network import (
     AttentionHistory,
@@ -9,6 +12,7 @@ from giant.model.network import (
     Stage1Model,
     Stage2Autoregressive,
     Stage2OneShot,
+    build_models,
     cat_col_layout,
     stage2_trunk_sec_dim,
     stage2_type_dim,
@@ -628,3 +632,52 @@ def test_stage2_autoregressive_history_step_matches_parallel_history_encoder():
 def test_stage2_autoregressive_init_history_cache_is_none_for_markov():
     model = _build_stage2_ar("physical", "wgan", history="markov")
     assert model.init_history_cache() is None
+
+
+# ── build_models: conditioning.share_stages ─────────────────────────────────
+
+
+def _minimal_model_config(share_stages: bool) -> dict:
+    cfg = copy.deepcopy(gconfig.DEFAULT_CONFIG)
+    cfg["conditioning"]["share_stages"] = share_stages
+    cfg["conditioning"]["particle"]["emb_dim"] = 4
+    cfg["conditioning"]["material"]["emb_dim"] = 4
+    cfg["stage1_model"].update({"hidden_dim": 8, "n_res_blocks": 1})
+    cfg["stage2_model"].update({"hidden_dim": 8, "n_res_blocks": 1, "k_max": 3})
+    return {
+        "pdg_vocab": 3,
+        "mat_vocab": 2,
+        "conditioning": cfg["conditioning"],
+        "stage1_model": cfg["stage1_model"],
+        "stage2_model": cfg["stage2_model"],
+    }
+
+
+def test_build_models_share_stages_true_shares_condition_encoder_instance():
+    built = build_models(_minimal_model_config(share_stages=True))
+    stage1, stage2 = built["stage1"], built["stage2"]
+    assert stage1 is not None and stage2 is not None
+    assert stage1.cond_enc is stage2.cond_enc
+
+
+def test_build_models_share_stages_false_builds_independent_condition_encoders():
+    built = build_models(_minimal_model_config(share_stages=False))
+    stage1, stage2 = built["stage1"], built["stage2"]
+    assert stage1 is not None and stage2 is not None
+    assert stage1.cond_enc is not stage2.cond_enc
+
+
+def test_build_models_share_stages_true_shared_params_are_in_both_stage_parameter_lists():
+    """The shared encoder's parameters must actually appear in both stages'
+    own `.parameters()` — that's what makes each stage's independent
+    optimizer include (and update) them, which is the actual mechanism behind
+    "shared weights, forced common representation" (docs/v0.3.0-design.md
+    §3.1), not just object identity on `.cond_enc`."""
+    built = build_models(_minimal_model_config(share_stages=True))
+    stage1, stage2 = built["stage1"], built["stage2"]
+    assert stage1 is not None and stage2 is not None
+
+    shared_ids = {id(p) for p in stage1.cond_enc.parameters()}
+    assert shared_ids
+    assert shared_ids <= {id(p) for p in stage1.parameters()}
+    assert shared_ids <= {id(p) for p in stage2.parameters()}