chore: update changelog for v0.3.6 [skip ci]

Reviewed-on: #74

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.3.4"
+current_version = "0.3.6"
 parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
 serialize = ["{major}.{minor}.{patch}"]
 search = "{current_version}"

CHANGELOG.md

@@ -1,5 +1,17 @@
 # Changelog
 
+## [0.3.6] - 2026-08-24
+
+### Changed
+
+- Give CriticModel a registry-built trunk and StageModel base [gitea #57](https://git.larsbogner.de/lars/giant/issues/57)
+
+## [0.3.5] - 2026-08-24
+
+### Added
+
+- Add "none" variants for router, history, and trunk [gitea #45](https://git.larsbogner.de/lars/giant/issues/45)
+
 ## [0.3.4] - 2026-08-23
 
 ### Added

giant/model/builders.py

@@ -202,6 +202,8 @@ def build_critics(model_config: dict) -> dict[str, nn.Module | None]:
             cond_out_dim=cond_out_dim,
             dropout=s1_spec.dropout,
             stage="stage1",
+            trunk_type=s1_spec.trunk.type,
+            block_conditioning=s1_spec.trunk.block_conditioning,
         )
 
     if s2_spec.active and build_objective(s2_spec.generator).is_adversarial:
@@ -225,6 +227,8 @@ def build_critics(model_config: dict) -> dict[str, nn.Module | None]:
             dropout=s2_spec.dropout,
             stage="stage2",
             context_dim=s2_spec.context_dim,
+            trunk_type=s2_spec.trunk.type,
+            block_conditioning=s2_spec.trunk.block_conditioning,
         )
 
     return result

giant/model/history.py

@@ -63,6 +63,23 @@ def build_history(name: str, in_dim: int, out_dim: int, **kwargs) -> HistoryEnco
     return cls(in_dim, out_dim, **filtered)
 
 
+@register_history("none")
+class NoHistory(HistoryEncoder):
+    """No history signal at all — ignores feat/has_prev entirely and always
+    returns zeros. Ablates whether the AR decoder's history conditioning is
+    earning its parameters. `init_cache`/`step` use the base class's O(1)
+    defaults unmodified (this encoder's own `forward` is already O(1) per
+    call regardless of prefix length)."""
+
+    def __init__(self, in_dim: int, out_dim: int) -> None:
+        super().__init__()
+        self.out_dim = out_dim
+
+    def forward(self, feat: torch.Tensor, has_prev: torch.Tensor) -> torch.Tensor:
+        B, K, _ = feat.shape
+        return torch.zeros(B, K, self.out_dim, device=feat.device, dtype=feat.dtype)
+
+
 @register_history("markov")
 class MarkovHistory(HistoryEncoder):
     """Summarizes the previous secondary's own `(energy_fraction, direction,

giant/model/models.py

@@ -8,7 +8,7 @@ from giant.config import ConditioningAxisConfig, HeadConfig, ParticleTypeConfig
 from giant.constants import CONT_SLOT_DIM, K_MAX, PARTICLE_PHYS_DIM, SEC_DIM, SEC_SLOT_DIM, X_DIM
 from giant.model.encoders import ConditionEncoder
 from giant.model.history import HistoryEncoder, build_history
-from giant.model.layers import ContextAdapter, ResBlock, SinusoidalEmbedding, build_mlp_head
+from giant.model.layers import ContextAdapter, SinusoidalEmbedding, build_mlp_head
 from giant.model.objectives import build_objective
 from giant.model.routers import Router
 from giant.model.trunks import build_trunk
@@ -188,6 +188,26 @@ class StageModel(nn.Module):
             hidden = max(1, round(hidden_dim * head_cfg.hidden_ratio))
             self.stop_head = build_mlp_head(cond_out_dim, 1, hidden, head_cfg.depth)
 
+    def _build_context_fusion(self, x_dim: int, context_dim: int, cond_out_dim: int) -> None:
+        """Builds `self.context_adapter`/`self.fuse` — the stage-2-style
+        context-fusion pattern (project the previous stage's outcome down to
+        `context_dim` via `ContextAdapter`, concat onto the base conditioning,
+        project back to `cond_out_dim`) shared by `Stage2OneShot` and a
+        `stage="stage2"` `CriticModel` (gitea #57). Call from a subclass's
+        `__init__` before using `_cond_embed`."""
+        self.context_adapter = ContextAdapter(x_dim, context_dim)
+        self.fuse = nn.Sequential(
+            nn.Linear(cond_out_dim + context_dim, cond_out_dim),
+            nn.SiLU(),
+        )
+
+    def _cond_embed(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor) -> torch.Tensor:
+        """Fuses base conditioning with the previous stage's outcome — pairs
+        with `_build_context_fusion`."""
+        base = self.cond_enc(cond_cont, cond_cat)
+        ctx = self.context_adapter(stage1_out)
+        return self.fuse(torch.cat([base, ctx], dim=-1))
+
     def _require_n_sec_head(self) -> None:
         if self.n_sec_head is None:
             raise RuntimeError(
@@ -363,11 +383,7 @@ class Stage2OneShot(StageModel):
             particle_type_cfg=particle_type_cfg,
             cond_enc=cond_enc,
         )
-        self.context_adapter = ContextAdapter(x_dim, context_dim)
-        self.fuse = nn.Sequential(
-            nn.Linear(cond_out_dim + context_dim, cond_out_dim),
-            nn.SiLU(),
-        )
+        self._build_context_fusion(x_dim, context_dim, cond_out_dim)
         target = self.particle_type_cfg.target
         type_head_out_dim = None if target == "physical" else k_max * self.type_dim
         self._build_trunk_and_heads(
@@ -386,11 +402,6 @@ class Stage2OneShot(StageModel):
             type_head_cfg=type_head_cfg,
         )
 
-    def _cond_embed(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor, stage1_out: torch.Tensor) -> torch.Tensor:
-        base = self.cond_enc(cond_cont, cond_cat)
-        ctx = self.context_adapter(stage1_out)
-        return self.fuse(torch.cat([base, ctx], dim=-1))
-
     def forward(
         self,
         x_t: torch.Tensor,
@@ -702,11 +713,25 @@ class Stage2Autoregressive(StageModel):
         return self.stop_head(c_emb.reshape(B * K, -1)).view(B, K)
 
 
-class CriticModel(nn.Module):
+class CriticModel(StageModel):
     """Generator-agnostic WGAN-GP critic body: a scalar realism score, for
     either stage (`stage="stage1"` mirrors v0.2 `Critic`; `stage="stage2"`
     mirrors v0.2 `SecondaryCritic`, adding the same context-fusion path as
-    `Stage2OneShot`). Used only when that stage's `generator == "wgan"`."""
+    `Stage2OneShot`, via `StageModel._build_context_fusion`/`_cond_embed`).
+    Used only when that stage's `generator == "wgan"`.
+
+    Subclasses `StageModel` for the `cond_enc` construction and (stage 2)
+    context-fusion scaffolding only — its trunk is built directly via
+    `build_trunk` (output width 1) rather than through
+    `_build_trunk_and_heads`, since that helper is shaped around a
+    generator's `Objective`/time-embedding/flow-matching concerns
+    (`forward`'s `(x_t, cond) -> vector` shape) that don't apply to a critic's
+    `(x, cond) -> scalar` (gitea #57). `generator="wgan"` is passed to the
+    base purely because that's factually when a critic exists; nothing here
+    ever calls `_build_trunk_and_heads`, so no head/time-embedding machinery
+    is built from it. Never routed (MoE) — that's a separate, unrequested
+    axis of scope; see gitea #57's proposal, which covers only the trunk/
+    block registries."""
 
     def __init__(
         self,
@@ -722,22 +747,26 @@ class CriticModel(nn.Module):
         stage: str = "stage1",
         context_dim: int = 64,
         context_in_dim: int = X_DIM,
+        trunk_type: str = "resmlp",
+        block_conditioning: str = "add",
     ) -> None:
-        super().__init__()
+        super().__init__(
+            pdg_vocab,
+            mat_vocab,
+            particle_cfg,
+            material_cfg,
+            cond_out_dim=cond_out_dim,
+            generator="wgan",
+            noise_dim=0,
+        )
         if stage not in ("stage1", "stage2"):
             raise ValueError(f"stage must be 'stage1' or 'stage2', got {stage!r}")
         self.stage = stage
-        self.cond_enc = ConditionEncoder(pdg_vocab, mat_vocab, particle_cfg, material_cfg, out_dim=cond_out_dim)
         if stage == "stage2":
-            self.context_adapter = ContextAdapter(context_in_dim, context_dim)
-            self.fuse = nn.Sequential(
-                nn.Linear(cond_out_dim + context_dim, cond_out_dim),
-                nn.SiLU(),
-            )
-        self.input_proj = nn.Linear(in_dim, hidden_dim)
-        self.blocks = nn.ModuleList([ResBlock(hidden_dim, cond_out_dim, dropout=dropout) for _ in range(n_res_blocks)])
-        self.out_norm = nn.LayerNorm(hidden_dim)
-        self.out_proj = nn.Linear(hidden_dim, 1)
+            self._build_context_fusion(context_in_dim, context_dim, cond_out_dim)
+        self.trunk = build_trunk(
+            None, trunk_type, in_dim, 1, hidden_dim, n_res_blocks, cond_out_dim, dropout, block_conditioning
+        )
 
     def forward(
         self,
@@ -746,13 +775,9 @@ class CriticModel(nn.Module):
         cond_cat: torch.Tensor,
         stage1_out: torch.Tensor | None = None,
     ) -> torch.Tensor:
-        base = self.cond_enc(cond_cont, cond_cat)
         if self.stage == "stage2":
-            ctx = self.context_adapter(stage1_out)
-            cond = self.fuse(torch.cat([base, ctx], dim=-1))
+            assert stage1_out is not None, "stage='stage2' CriticModel requires stage1_out"
+            cond = self._cond_embed(cond_cont, cond_cat, stage1_out)
         else:
-            cond = base
-        h = self.input_proj(x)
-        for block in self.blocks:
-            h = block(h, cond)
-        return self.out_proj(self.out_norm(h)).squeeze(-1)
+            cond = self.cond_enc(cond_cont, cond_cat)
+        return self.trunk(x, cond, cond_cont, cond_cat).squeeze(-1)

giant/model/network.py

@@ -15,6 +15,7 @@ from giant.model.history import (
     AttentionHistory,
     HistoryEncoder,
     MarkovHistory,
+    NoHistory,
     _CausalAttnBlock,
     build_history,
     register_history,
@@ -54,6 +55,7 @@ from giant.model.routers import (
     ROUTER_REGISTRY,
     ComposedRouter,
     EnergyRouter,
+    NoneRouter,
     PdgRouter,
     ProcessRouter,
     Router,
@@ -67,6 +69,7 @@ from giant.model.routers import (
 from giant.model.trunks import (
     TRUNK_REGISTRY,
     ExpertTrunk,
+    LinearTrunk,
     RoutedTrunk,
     Trunk,
     _route_forward,
@@ -90,7 +93,10 @@ __all__ = [
     "FlowObjective",
     "HISTORY_REGISTRY",
     "HistoryEncoder",
+    "LinearTrunk",
     "MarkovHistory",
+    "NoHistory",
+    "NoneRouter",
     "OBJECTIVE_REGISTRY",
     "Objective",
     "PdgRouter",

giant/model/routers.py

@@ -144,6 +144,24 @@ def _inverse_bounded_interp(value: float, lo: float, hi: float) -> float:
     return math.log(p / (1 - p))
 
 
+@register_router("none")
+class NoneRouter(Router):
+    """Uniform 1/n_experts gate — no learned routing signal at all.
+
+    Still builds n_experts expert trunks via RoutedTrunk (same parameter
+    budget as a real router), but every row gets an identical weight
+    regardless of conditioning. Ablates whether the *learned routing
+    signal* — as opposed to simply having multiple experts — is earning
+    its parameters. `top1()` (the base class default) always dispatches to
+    expert 0 (argmax of a uniform vector), which still exercises
+    RoutedTrunk's real per-expert grouped-dispatch code path at eval time.
+    """
+
+    def gate(self, cond_cont: torch.Tensor, cond_cat: torch.Tensor) -> torch.Tensor:
+        B = cond_cont.shape[0]
+        return torch.full((B, self.n_experts), 1.0 / self.n_experts, device=cond_cont.device)
+
+
 @register_router("energy")
 class EnergyRouter(Router):
     """Soft turn-on gate over normalized pre-step log-energy.

giant/model/trunks.py

@@ -94,6 +94,49 @@ class ExpertTrunk(nn.Module):
         return self.out_proj(x)
 
 
+@register_trunk("linear")
+class LinearTrunk(nn.Module):
+    """`nn.Linear(in_dim + cond_dim, out_dim)` over `concat([x, cond])` —
+    the trivial trunk body: no hidden layer, no ResBlock stack, no
+    nonlinearity. Ablates whether trunk depth/nonlinearity is earning its
+    parameters, holding everything else (heads, ConditionEncoder,
+    generator, ...) fixed. Composes for free with `router.enabled = true`
+    (gitea #33): a RoutedTrunk of n_experts linear bodies is "mixture of
+    trivial linear experts". `hidden_dim`/`n_blocks`/`dropout`/
+    `block_conditioning` are accepted and ignored, matching
+    `build_expert_body`'s shared factory signature.
+
+    `x` — the trunk's own input (e.g. the noised primary vector for flow
+    matching) — does not already carry conditioning; that's fused in
+    per-body via `cond`. So this concatenates `x` and `cond` itself to
+    remain a valid, conditioning-dependent model.
+    """
+
+    def __init__(
+        self,
+        in_dim: int,
+        out_dim: int,
+        hidden_dim: int,
+        n_blocks: int,
+        cond_dim: int,
+        dropout: float = 0.0,
+        block_conditioning: str = "add",
+    ) -> None:
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+        self.linear = nn.Linear(in_dim + cond_dim, out_dim)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cond: torch.Tensor,
+        cond_cont: torch.Tensor | None = None,
+        cond_cat: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        return self.linear(torch.cat([x, cond], dim=-1))
+
+
 def _route_forward(
     experts: nn.ModuleList,
     router: Router,

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "giant"
-version = "0.3.4"
+version = "0.3.6"
 description = "Geant4 step-function surrogate via conditional flow matching"
 readme = "README.md"
 requires-python = ">=3.12"

tests/test_network.py

@@ -8,8 +8,12 @@ from giant.model.network import (
     HISTORY_REGISTRY,
     AttentionHistory,
     ConditionEncoder,
+    CriticModel,
+    FilmResBlock,
     HistoryEncoder,
+    LinearTrunk,
     MarkovHistory,
+    NoHistory,
     SinusoidalEmbedding,
     Stage1Model,
     Stage2Autoregressive,
@@ -465,16 +469,52 @@ def test_attention_history_step_matches_forward():
     assert torch.allclose(stepped, expected, atol=1e-5)
 
 
+# --- NoHistory (gitea #45) ----------------------------------------------------
+
+
+def test_no_history_shape():
+    hist = NoHistory(in_dim=7, out_dim=12)
+    B, K = 3, 5
+    feat = torch.randn(B, K, 7)
+    has_prev = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1)
+    out = hist(feat, has_prev)
+    assert out.shape == (B, K, 12)
+
+
+def test_no_history_ignores_feat_and_has_prev():
+    hist = NoHistory(in_dim=4, out_dim=6)
+    B, K = 2, 3
+    has_prev_a = (torch.arange(K) >= 1).unsqueeze(0).expand(B, -1)
+    has_prev_b = torch.zeros(B, K, dtype=torch.bool)
+    feat_a = torch.randn(B, K, 4)
+    feat_b = torch.randn(B, K, 4) * 100
+    out_a = hist(feat_a, has_prev_a)
+    out_b = hist(feat_b, has_prev_b)
+    assert torch.equal(out_a, torch.zeros(B, K, 6))
+    assert torch.equal(out_a, out_b)
+
+
+def test_no_history_uses_base_class_o1_defaults():
+    hist = NoHistory(in_dim=4, out_dim=6)
+    assert hist.init_cache() is None
+    feat = torch.randn(2, 1, 4)
+    has_prev = torch.ones(2, 1, dtype=torch.bool)
+    out, cache = hist.step(feat, has_prev, "unused-cache")
+    assert torch.equal(out, torch.zeros(2, 1, 6))
+    assert cache == "unused-cache"
+
+
 # --- HISTORY_REGISTRY / build_history (gitea #35) ----------------------------
 
 
-def test_history_registry_has_exactly_the_two_known_histories():
-    assert set(HISTORY_REGISTRY) == {"markov", "attention"}
+def test_history_registry_has_exactly_the_known_histories():
+    assert set(HISTORY_REGISTRY) == {"markov", "attention", "none"}
 
 
 def test_build_history_returns_correct_concrete_type():
     assert isinstance(build_history("markov", 4, 6), MarkovHistory)
     assert isinstance(build_history("attention", 4, 8), AttentionHistory)
+    assert isinstance(build_history("none", 4, 6), NoHistory)
 
 
 def test_build_history_unknown_name_raises():
@@ -605,7 +645,7 @@ def test_stage2_autoregressive_n_sec_head_and_type_head_cfg_control_hidden_width
 
 @pytest.mark.parametrize("target", ["physical", "onehot", "embedding"])
 @pytest.mark.parametrize("generator", ["wgan", "flow"])
-@pytest.mark.parametrize("history", ["markov", "attention"])
+@pytest.mark.parametrize("history", ["markov", "attention", "none"])
 def test_stage2_autoregressive_forward_shape(target, generator, history):
     B, K, emb_dim = 4, 5, 6
     model = _build_stage2_ar(target, generator, emb_dim=emb_dim, k_max=K, history=history)
@@ -907,7 +947,7 @@ def test_build_critics_particle_type_n_classes_overrides_conditioning_emb_dim():
     assert wider_critic is not None
     # k_max=3 slots, each CONT_SLOT_DIM + n_classes wide under wgan folding —
     # widening n_classes alone (emb_dim stays 4) must widen the critic input.
-    assert wider_critic.input_proj.in_features > default_n_classes_critic.input_proj.in_features
+    assert wider_critic.trunk.input_proj.in_features > default_n_classes_critic.trunk.input_proj.in_features
 
 
 # ── build_models/build_critics: DEFAULT_CONFIG fallback drift (issues.md #1) ─
@@ -984,12 +1024,12 @@ def test_build_critics_omitted_particle_type_matches_default_config():
     cfg["stage2_model"]["generator"] = "wgan"
     onehot_critic = build_critics(cfg)["stage2"]
     assert onehot_critic is not None
-    onehot_in_dim = onehot_critic.input_proj.in_features
+    onehot_in_dim = onehot_critic.trunk.input_proj.in_features
 
     cfg["stage2_model"]["particle_type"] = {"target": "physical"}
     physical_critic = build_critics(cfg)["stage2"]
     assert physical_critic is not None
-    physical_in_dim = physical_critic.input_proj.in_features
+    physical_in_dim = physical_critic.trunk.input_proj.in_features
 
     # onehot's per-slot type width is emb_dim classes vs. physical's fixed
     # (log-mass, charge) pair — different unless emb_dim happens to be 2, so
@@ -1009,15 +1049,15 @@ def test_build_critics_stage1_critic_hidden_dim_and_n_res_blocks_override_genera
 
     inherited = build_critics(cfg)["stage1"]
     assert inherited is not None
-    assert inherited.input_proj.out_features == 8
-    assert len(inherited.blocks) == 1
+    assert inherited.trunk.input_proj.out_features == 8
+    assert len(inherited.trunk.blocks) == 1
 
     cfg["stage1_model"]["wgan"]["critic_hidden_dim"] = 16
     cfg["stage1_model"]["wgan"]["critic_n_res_blocks"] = 3
     overridden = build_critics(cfg)["stage1"]
     assert overridden is not None
-    assert overridden.input_proj.out_features == 16
-    assert len(overridden.blocks) == 3
+    assert overridden.trunk.input_proj.out_features == 16
+    assert len(overridden.trunk.blocks) == 3
 
 
 def test_build_critics_stage2_critic_hidden_dim_and_n_res_blocks_override_generator_size():
@@ -1028,15 +1068,15 @@ def test_build_critics_stage2_critic_hidden_dim_and_n_res_blocks_override_genera
 
     inherited = build_critics(cfg)["stage2"]
     assert inherited is not None
-    assert inherited.input_proj.out_features == 8
-    assert len(inherited.blocks) == 1
+    assert inherited.trunk.input_proj.out_features == 8
+    assert len(inherited.trunk.blocks) == 1
 
     cfg["stage2_model"]["wgan"]["critic_hidden_dim"] = 16
     cfg["stage2_model"]["wgan"]["critic_n_res_blocks"] = 3
     overridden = build_critics(cfg)["stage2"]
     assert overridden is not None
-    assert overridden.input_proj.out_features == 16
-    assert len(overridden.blocks) == 3
+    assert overridden.trunk.input_proj.out_features == 16
+    assert len(overridden.trunk.blocks) == 3
 
 
 # ── StageModel base (gitea #39): Stage1Model/Stage2OneShot/Stage2Autoregressive
@@ -1197,3 +1237,34 @@ def test_stagemodel_time_emb_matches_objective_needs_time(cls, generator):
     assert model.generator_kind == generator
     assert model.noise_dim == 8
     assert (model.time_emb is not None) == build_objective(generator).needs_time
+
+
+# ── CriticModel uses the trunk/block registries + StageModel base (gitea #57) ─
+
+
+def test_critic_model_is_stagemodel_subclass():
+    assert issubclass(CriticModel, StageModel)
+
+
+@pytest.mark.parametrize("stage", ["stage1", "stage2"])
+def test_build_critics_threads_trunk_type_from_generator_config(stage):
+    cfg = _minimal_model_config(share_stages=False)
+    cfg["stage1_model"]["generator"] = "wgan"
+    cfg["stage2_model"]["generator"] = "wgan"
+    cfg[f"{stage}_model"]["trunk"] = {"type": "linear"}
+
+    critic = build_critics(cfg)[stage]
+    assert critic is not None
+    assert isinstance(critic.trunk, LinearTrunk)
+
+
+@pytest.mark.parametrize("stage", ["stage1", "stage2"])
+def test_build_critics_threads_block_conditioning_from_generator_config(stage):
+    cfg = _minimal_model_config(share_stages=False)
+    cfg["stage1_model"]["generator"] = "wgan"
+    cfg["stage2_model"]["generator"] = "wgan"
+    cfg[f"{stage}_model"]["trunk"] = {"block_conditioning": "film"}
+
+    critic = build_critics(cfg)[stage]
+    assert critic is not None
+    assert all(isinstance(block, FilmResBlock) for block in critic.trunk.blocks)

tests/test_router.py

@@ -13,6 +13,8 @@ from giant.model.network import (
     EnergyRouter,
     ExpertTrunk,
     FilmResBlock,
+    LinearTrunk,
+    NoneRouter,
     PdgRouter,
     ProcessRouter,
     ROUTER_REGISTRY,
@@ -73,6 +75,34 @@ def test_energy_router_registered():
     assert ROUTER_REGISTRY["energy"] is EnergyRouter
 
 
+# ── NoneRouter (gitea #45) ──────────────────────────────────────────────────
+
+
+def test_none_router_registered():
+    assert ROUTER_REGISTRY["none"] is NoneRouter
+
+
+def test_none_router_gate_is_uniform():
+    router = NoneRouter(n_experts=4)
+    cond_cont, cond_cat = _cond(16)
+    g = router.gate(cond_cont, cond_cat)
+    assert g.shape == (16, 4)
+    torch.testing.assert_close(g, torch.full((16, 4), 0.25))
+
+
+def test_none_router_gate_ignores_conditioning():
+    router = NoneRouter(n_experts=3)
+    cond_cont_a, cond_cat_a = _cond(8)
+    cond_cont_b, cond_cat_b = _cond(8)
+    torch.testing.assert_close(router.gate(cond_cont_a, cond_cat_a), router.gate(cond_cont_b, cond_cat_b))
+
+
+def test_none_router_top1_always_expert_zero():
+    router = NoneRouter(n_experts=4)
+    cond_cont, cond_cat = _cond(16)
+    assert torch.equal(router.top1(cond_cont, cond_cat), torch.zeros(16, dtype=torch.long))
+
+
 def test_energy_router_gate_partition_of_unity():
     router = EnergyRouter(n_experts=4)
     cond_cont, cond_cat = _cond(16)
@@ -168,6 +198,47 @@ def test_build_expert_body_unknown_type_raises():
     raise AssertionError("expected ValueError for unknown trunk type")
 
 
+# ── LinearTrunk (gitea #45) ──────────────────────────────────────────────────
+
+
+def test_trunk_registry_has_linear():
+    assert "linear" in TRUNK_REGISTRY
+    assert TRUNK_REGISTRY["linear"] is LinearTrunk
+
+
+def test_linear_trunk_forward_shape():
+    trunk = build_expert_body("linear", in_dim=9, out_dim=9, hidden_dim=64, n_blocks=6, cond_dim=12)
+    assert trunk.in_dim == 9
+    assert trunk.out_dim == 9
+    x = torch.randn(5, 9)
+    cond = torch.randn(5, 12)
+    out = trunk(x, cond)
+    assert out.shape == (5, 9)
+
+
+def test_linear_trunk_depends_on_x_and_cond():
+    trunk = build_expert_body("linear", in_dim=9, out_dim=9, hidden_dim=64, n_blocks=6, cond_dim=12)
+    x = torch.randn(5, 9)
+    cond_a = torch.randn(5, 12)
+    cond_b = torch.randn(5, 12)
+    assert not torch.allclose(trunk(x, cond_a), trunk(x, cond_b))
+
+
+def test_routed_linear_trunk_is_mixture_of_trivial_experts():
+    """trunk.type = 'linear' composes for free with router.enabled = true
+    (gitea #33's comment on this issue) — a RoutedTrunk of n_experts linear
+    bodies."""
+    router = build_router("energy", n_experts=3)
+    trunk = RoutedTrunk(router, "linear", in_dim=9, out_dim=9, hidden_dim=64, n_res_blocks=6, cond_dim=12)
+    assert len(trunk.experts) == 3
+    assert all(isinstance(e, LinearTrunk) for e in trunk.experts)
+    x = torch.randn(5, 9)
+    cond = torch.randn(5, 12)
+    cond_cont, cond_cat = _cond(5)
+    out = trunk(x, cond, cond_cont, cond_cat)
+    assert out.shape == (5, 9)
+
+
 # ── BLOCK_REGISTRY / build_block (gitea #34) ────────────────────────────────
 
 

tests/test_wgan.py

@@ -2,7 +2,7 @@ import torch
 
 from giant.config import ConditioningAxisConfig
 from giant.constants import COND_DIM, K_MAX, SEC_DIM, SEC_SLOT_DIM, X_DIM
-from giant.model.network import CriticModel, Stage1Model, Stage2OneShot
+from giant.model.network import CriticModel, LinearTrunk, Stage1Model, Stage2OneShot
 from giant.model.wgan import critic_loss, generator_loss, gradient_penalty
 from giant.sample import sample_secondaries_wgan, sample_wgan
 
@@ -115,6 +115,33 @@ def test_critic_output_shape():
     assert out.shape == (B,)
 
 
+def test_critic_model_honours_trunk_type_and_block_conditioning():
+    """gitea #57: CriticModel routes its body through build_trunk/build_block
+    like every generator stage model, instead of hand-rolling a plain
+    ResBlock stack."""
+    B = 8
+    critic = CriticModel(
+        pdg_vocab=3,
+        mat_vocab=2,
+        particle_cfg=PARTICLE_CFG,
+        material_cfg=MATERIAL_CFG,
+        in_dim=X_DIM,
+        hidden_dim=32,
+        n_res_blocks=2,
+        stage="stage1",
+        trunk_type="linear",
+        block_conditioning="adaln",
+    )
+    assert isinstance(critic.trunk, LinearTrunk)
+    cond_cont, cond_cat = _cond(B)
+    real = torch.randn(B, X_DIM)
+    fake = torch.randn(B, X_DIM)
+    loss = critic_loss(lambda x: critic(x, cond_cont, cond_cat), real, fake.detach(), gp_weight=10.0)
+    loss.backward()
+    for name, p in critic.named_parameters():
+        assert p.grad is not None, f"no grad for {name}"
+
+
 def test_sample_wgan_shape():
     B = 6
     model = _small_generator()

uv.lock

@@ -675,7 +675,7 @@ wheels = [
 
 [[package]]
 name = "giant"
-version = "0.3.4"
+version = "0.3.6"
 source = { editable = "." }
 dependencies = [
     { name = "numpy" },