Make config dataclasses the single source of truth for DEFAULT_CONFIG

DEFAULT_CONFIG and build_models/build_critics/StageSpec.from_config's
inline .get(key, default) fallbacks had already drifted: two keys
(stage2_model.decoder, stage2_model.particle_type.target) resolved
differently depending on whether a config dict came from
merge_cli_overrides (fully populated, correct) or was hand-built and
partial (fell back to stale v0.2-shaped literals). Introduce frozen
dataclasses (GiantConfig and its nested blocks) in giant/config.py as
the actual single declaration of every default; DEFAULT_CONFIG is now
generated from them instead of hand-maintained, and build_models,
build_critics, and StageSpec.from_config consume the dataclasses
instead of duplicating literal fallbacks, so this class of drift can't
recur. Router/n_sec sub-blocks keep an `extra` catch-all for their
genuinely dynamic keys (composed-router axes, runtime-seeded
centers_init, legacy_owner).

Fixing the fallback surfaced the same latent bug in two existing
partial-config callers that had been silently depending on it: a
test fixture in test_train.py and scripts/warm_setup_cache.py's
minimal cfg (now merged against DEFAULT_CONFIG instead of hand-rolled,
closing the gap for good). See issues.md Issue 1.

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

tests/test_train.py

@@ -265,6 +265,12 @@ def _base_cfg():
             "k_max": K_MAX,
             "context_dim": 16,
             "n_sec": {"mode": "head", "lambda": 0.1},
+            # Explicit, not relying on the fallback default (which is
+            # "onehot", matching DEFAULT_CONFIG — see issues.md Issue 1):
+            # the "physical"-labelled cases below (and this fixture's own
+            # comment history) intend this as the base "physical" case,
+            # with "*_onehot"/"*_embedding" cases opting in explicitly.
+            "particle_type": {"target": "physical", "lambda": 1.0},
             "flow": {"time_dim": 16},
             "ddpm": {"time_dim": 16, "n_steps": 50},
             "wgan": {
@@ -566,6 +572,20 @@ def test_flow_stage_trainer_ddpm_not_implemented_for_stage2():
         FlowDDPMStageTrainer(spec, torch.nn.Linear(1, 1), torch.device("cpu"))
 
 
+def test_stage_spec_from_config_omitted_decoder_and_particle_type_match_default_config():
+    """Regression for issues.md Issue 1: StageSpec.from_config's own fallback
+    defaults for stage2_model.decoder/particle_type must equal
+    DEFAULT_CONFIG's ("autoregressive" / "onehot"), not the old, now-wrong
+    ("one_shot" / "physical") literals a .get(key, default) call used to
+    supply when a hand-built cfg omitted these keys."""
+    cfg = _base_cfg()
+    del cfg["stage2_model"]["decoder"]
+    del cfg["stage2_model"]["particle_type"]
+    spec = StageSpec.from_config(cfg, "stage2", is_stage2=True, steps_per_epoch=1)
+    assert spec.decoder == "autoregressive"
+    assert spec.particle_type.target == "onehot"
+
+
 # --- AR trainer wiring (v0.3.0 step 5) --------------------------------------
 
 
@@ -664,7 +684,7 @@ def test_wgan_onehot_one_shot_also_gets_grad_norm_instrumentation():
 
 
 def test_wgan_physical_omits_grad_norm_slice_columns():
-    cfg = _base_cfg()  # default stage2_model has no particle_type -> "physical"
+    cfg = _base_cfg()  # _base_cfg's stage2_model.particle_type.target is "physical"
     with tempfile.TemporaryDirectory() as tmp:
         out_dir = Path(tmp) / "run"
         _run_train(cfg, out_dir)