diff --git a/tests/unit/test_qwen3_5_adapter.py b/tests/unit/test_qwen3_5_adapter.py
index 8fd885174..d1a4a7b6a 100644
--- a/tests/unit/test_qwen3_5_adapter.py
+++ b/tests/unit/test_qwen3_5_adapter.py
@@ -18,11 +18,23 @@
 )
 from transformer_lens.tools.model_registry import HF_SUPPORTED_ARCHITECTURES
 
+try:
+    from transformers import Qwen3_5ForCausalLM as _Qwen3_5ForCausalLM
+    from transformers import Qwen3_5TextConfig
+
+    _QWEN3_5_AVAILABLE = True
+except ImportError:
+    _QWEN3_5_AVAILABLE = False
+
 # ============================================================================
 # Test: Registration
 # ============================================================================
 
 
+@pytest.mark.skipif(
+    not _QWEN3_5_AVAILABLE,
+    reason="Qwen3_5TextConfig / Qwen3_5ForCausalLM not available in installed transformers",
+)
 class TestQwen3_5Registration:
     """Verify the adapter is properly registered in all lookup tables."""
 
@@ -79,6 +91,10 @@ def _make_bridge_cfg(**overrides):
 # ============================================================================
 
 
+@pytest.mark.skipif(
+    not _QWEN3_5_AVAILABLE,
+    reason="Qwen3_5TextConfig / Qwen3_5ForCausalLM not available in installed transformers",
+)
 class TestQwen3_5ComponentMapping:
     """Verify the component_mapping structure for Qwen3_5.
 
@@ -134,18 +150,28 @@ def test_unembed_path(self, adapter):
     # ---- Block submodules ----
 
     def test_block_submodules_keys(self, adapter):
-        """blocks submodules must contain ln1, ln2, mlp but NOT attn.
+        """blocks submodules must contain ln1, ln2, mlp, and optional attn + linear_attn."""
+        submodules = adapter.component_mapping["blocks"].submodules
+        assert set(submodules.keys()) == {"ln1", "ln2", "mlp", "attn", "linear_attn"}
 
-        Critical correctness test: self_attn is absent on linear-attention
-        layers, so mapping attn as a block submodule would crash on those layers.
-        """
+    def test_attn_is_optional(self, adapter):
+        """attn must be marked optional (absent on linear-attention layers)."""
+        submodules = adapter.component_mapping["blocks"].submodules
+        assert submodules["attn"].optional is True
+
+    def test_linear_attn_is_optional(self, adapter):
+        """linear_attn must be marked optional (absent on full-attention layers)."""
         submodules = adapter.component_mapping["blocks"].submodules
-        assert set(submodules.keys()) == {"ln1", "ln2", "mlp"}
+        assert submodules["linear_attn"].optional is True
+
+    def test_linear_attn_bridge_type(self, adapter):
+        """linear_attn must be a GatedDeltaNetBridge."""
+        from transformer_lens.model_bridge.generalized_components.gated_delta_net import (
+            GatedDeltaNetBridge,
+        )
 
-    def test_no_attn_in_block_submodules(self, adapter):
-        """attn must NOT appear as a block submodule (hybrid architecture safety check)."""
         submodules = adapter.component_mapping["blocks"].submodules
-        assert "attn" not in submodules
+        assert isinstance(submodules["linear_attn"], GatedDeltaNetBridge)
 
     def test_ln1_path(self, adapter):
         """ln1 maps to input_layernorm."""
@@ -257,6 +283,10 @@ def test_weight_processing_conversions_empty(self, adapter):
 # ============================================================================
 
 
+@pytest.mark.skipif(
+    not _QWEN3_5_AVAILABLE,
+    reason="Qwen3_5TextConfig / Qwen3_5ForCausalLM not available in installed transformers",
+)
 class TestQwen3_5ConfigAttributes:
     """Verify all cfg attributes are set correctly by the adapter."""
 
@@ -341,6 +371,10 @@ def test_n_key_value_heads_not_set_when_absent(self):
 # ============================================================================
 
 
+@pytest.mark.skipif(
+    not _QWEN3_5_AVAILABLE,
+    reason="Qwen3_5TextConfig / Qwen3_5ForCausalLM not available in installed transformers",
+)
 class TestQwen3_5PreprocessWeights:
     """Verify preprocess_weights correctly slices q_proj.weight per-head.
 
@@ -478,14 +512,6 @@ def test_weight_processing_conversions_is_empty_dict(self, adapter):
 # Test: Integration (Phase A+B)
 # ============================================================================
 
-try:
-    from transformers import Qwen3_5ForCausalLM as _Qwen3_5ForCausalLM
-    from transformers import Qwen3_5TextConfig
-
-    _QWEN3_5_AVAILABLE = True
-except ImportError:
-    _QWEN3_5_AVAILABLE = False
-
 
 def _make_tiny_hf_model():
     """Create a tiny Qwen3_5ForCausalLM for integration testing.
diff --git a/tests/unit/test_qwen3_next_adapter.py b/tests/unit/test_qwen3_next_adapter.py
index 1a2842e7b..516d7a8b5 100644
--- a/tests/unit/test_qwen3_next_adapter.py
+++ b/tests/unit/test_qwen3_next_adapter.py
@@ -135,19 +135,28 @@ def test_unembed_path(self, adapter):
     # ---- Block submodules ----
 
     def test_block_submodules_keys(self, adapter):
-        """blocks submodules must contain ln1, ln2, mlp but NOT attn.
+        """blocks submodules must contain ln1, ln2, mlp, and optional attn + linear_attn."""
+        submodules = adapter.component_mapping["blocks"].submodules
+        assert set(submodules.keys()) == {"ln1", "ln2", "mlp", "attn", "linear_attn"}
 
-        This is a critical correctness test: self_attn is absent on
-        linear-attention layers, so mapping attn as a block submodule
-        would crash on those layers.
-        """
+    def test_attn_is_optional(self, adapter):
+        """attn must be marked optional (absent on linear-attention layers)."""
+        submodules = adapter.component_mapping["blocks"].submodules
+        assert submodules["attn"].optional is True
+
+    def test_linear_attn_is_optional(self, adapter):
+        """linear_attn must be marked optional (absent on full-attention layers)."""
         submodules = adapter.component_mapping["blocks"].submodules
-        assert set(submodules.keys()) == {"ln1", "ln2", "mlp"}
+        assert submodules["linear_attn"].optional is True
+
+    def test_linear_attn_bridge_type(self, adapter):
+        """linear_attn must be a GatedDeltaNetBridge."""
+        from transformer_lens.model_bridge.generalized_components.gated_delta_net import (
+            GatedDeltaNetBridge,
+        )
 
-    def test_no_attn_in_block_submodules(self, adapter):
-        """attn must NOT appear as a block submodule (hybrid architecture safety check)."""
         submodules = adapter.component_mapping["blocks"].submodules
-        assert "attn" not in submodules
+        assert isinstance(submodules["linear_attn"], GatedDeltaNetBridge)
 
     def test_ln1_path(self, adapter):
         """ln1 maps to input_layernorm."""
diff --git a/transformer_lens/benchmarks/component_outputs.py b/transformer_lens/benchmarks/component_outputs.py
index ba2d03edd..504825ce1 100644
--- a/transformer_lens/benchmarks/component_outputs.py
+++ b/transformer_lens/benchmarks/component_outputs.py
@@ -311,8 +311,12 @@ def benchmark_all_components(
                 n_layers = self.cfg.n_layers
 
                 for layer_idx in range(n_layers):
-                    # Recursively test each subcomponent and its nested subcomponents
+                    # Get the actual block to check which submodules were bound
+                    actual_block = getattr(self.bridge_model, block_type)[layer_idx]
                     for subcomp_name, subcomponent in blocks_component.submodules.items():
+                        # Skip optional submodules absent on this layer (hybrid architectures)
+                        if subcomp_name not in actual_block._modules:
+                            continue
                         comp_path = f"{block_type}.{layer_idx}.{subcomp_name}"
                         self._test_component_recursive(
                             comp_path, subcomponent, test_inputs, results, skip_components
diff --git a/transformer_lens/benchmarks/weight_processing.py b/transformer_lens/benchmarks/weight_processing.py
index 5a7fafd65..62e561b25 100644
--- a/transformer_lens/benchmarks/weight_processing.py
+++ b/transformer_lens/benchmarks/weight_processing.py
@@ -638,10 +638,24 @@ def benchmark_mlp_output_centering(
                 message="Skipped for tiny/test model (random weights don't center meaningfully)",
             )
 
-        # Check if this is an MoE model - MoE models don't have a single W_out weight
+        # Find an MLP-like submodule (may be "mlp", "shared_mlp", etc.)
         from transformer_lens.model_bridge.generalized_components.moe import MoEBridge
 
-        if isinstance(bridge.blocks[0].mlp, MoEBridge):
+        mlp_module = None
+        block = bridge.blocks[0]
+        for name in ("mlp", "shared_mlp"):
+            if name in block._modules:
+                mlp_module = block._modules[name]
+                break
+        if mlp_module is None:
+            return BenchmarkResult(
+                name="mlp_output_centering",
+                severity=BenchmarkSeverity.WARNING,
+                message="No MLP submodule found on block 0",
+                passed=False,
+            )
+
+        if isinstance(mlp_module, MoEBridge):
             return BenchmarkResult(
                 name="mlp_output_centering",
                 severity=BenchmarkSeverity.INFO,
@@ -651,11 +665,10 @@ def benchmark_mlp_output_centering(
 
         # Check if W_out exists and is accessible (HT format or bridge format)
         w_out = None
-        if hasattr(bridge.blocks[0].mlp, "W_out"):
-            w_out = bridge.blocks[0].mlp.W_out
-        elif hasattr(bridge.blocks[0].mlp, "out"):
-            # Bridge format: mlp.out is a LinearBridge wrapping nn.Linear
-            out_module = bridge.blocks[0].mlp.out
+        if hasattr(mlp_module, "W_out"):
+            w_out = mlp_module.W_out
+        elif hasattr(mlp_module, "out"):
+            out_module = mlp_module.out
             if hasattr(out_module, "original_component") and hasattr(
                 out_module.original_component, "weight"
             ):
diff --git a/transformer_lens/model_bridge/component_setup.py b/transformer_lens/model_bridge/component_setup.py
index a2986d585..7821d0354 100644
--- a/transformer_lens/model_bridge/component_setup.py
+++ b/transformer_lens/model_bridge/component_setup.py
@@ -100,9 +100,10 @@ def setup_submodules(
                 else:
                     remote_path = submodule.name
                     is_optional = getattr(submodule, "optional", False)
-                    # Fast path: first segment absent → skip without entering get_remote_component
+                    # Fast path: first segment absent or None → skip
                     first_segment = remote_path.split(".")[0]
-                    if is_optional and not hasattr(original_model, first_segment):
+                    first_value = getattr(original_model, first_segment, None)
+                    if is_optional and first_value is None:
                         logger.debug(
                             "Optional '%s' (path '%s') absent on %s",
                             module_name,
diff --git a/transformer_lens/model_bridge/generalized_components/__init__.py b/transformer_lens/model_bridge/generalized_components/__init__.py
index fb789cc30..c2c7a121b 100644
--- a/transformer_lens/model_bridge/generalized_components/__init__.py
+++ b/transformer_lens/model_bridge/generalized_components/__init__.py
@@ -35,6 +35,9 @@
 from transformer_lens.model_bridge.generalized_components.alibi_joint_qkv_attention import (
     ALiBiJointQKVAttentionBridge,
 )
+from transformer_lens.model_bridge.generalized_components.gated_delta_net import (
+    GatedDeltaNetBridge,
+)
 from transformer_lens.model_bridge.generalized_components.gated_mlp import (
     GatedMLPBridge,
 )
diff --git a/transformer_lens/model_bridge/generalized_components/attention.py b/transformer_lens/model_bridge/generalized_components/attention.py
index 05d5e0982..5608ca2d8 100644
--- a/transformer_lens/model_bridge/generalized_components/attention.py
+++ b/transformer_lens/model_bridge/generalized_components/attention.py
@@ -59,6 +59,7 @@ def __init__(
         requires_position_embeddings: bool = False,
         requires_attention_mask: bool = False,
         attention_mask_4d: bool = False,
+        optional: bool = False,
     ):
         """Initialize the attention bridge.
 
@@ -82,7 +83,11 @@ def __init__(
         if conversion_rule is None:
             conversion_rule = AttentionAutoConversion(config)
         super().__init__(
-            name, config=config, submodules=submodules or {}, conversion_rule=conversion_rule
+            name,
+            config=config,
+            submodules=submodules or {},
+            conversion_rule=conversion_rule,
+            optional=optional,
         )
         self.hook_attn_scores = HookPoint()
         self.hook_pattern = HookPoint()
diff --git a/transformer_lens/model_bridge/generalized_components/gated_delta_net.py b/transformer_lens/model_bridge/generalized_components/gated_delta_net.py
new file mode 100644
index 000000000..1e13fe4bf
--- /dev/null
+++ b/transformer_lens/model_bridge/generalized_components/gated_delta_net.py
@@ -0,0 +1,303 @@
+"""GatedDeltaNet bridge for Qwen3.5/Qwen3Next linear-attention layers.
+
+Reimplements forward (prefill only) to expose mech-interp-relevant intermediate
+states. Falls back to HF native forward during autoregressive generation where
+cache state management is required.
+"""
+from typing import TYPE_CHECKING, Any, Dict, Optional
+
+import torch
+import torch.nn.functional as F
+
+from transformer_lens.hook_points import HookPoint
+from transformer_lens.model_bridge.generalized_components.base import (
+    GeneralizedComponent,
+)
+
+if TYPE_CHECKING:
+    from transformer_lens.ActivationCache import ActivationCache
+
+
+class GatedDeltaNetBridge(GeneralizedComponent):
+    """Bridge for GatedDeltaNet linear-attention with full hook decomposition.
+
+    Hooks (prefill, in execution order):
+        hook_in: input hidden_states [batch, seq, d_model]
+        hook_q_pre_conv: Q after projection, before conv [batch, seq, n_k_heads, head_k_dim]
+        hook_k_pre_conv: K after projection, before conv [batch, seq, n_k_heads, head_k_dim]
+        hook_v_pre_conv: V after projection, before conv [batch, seq, n_v_heads, head_v_dim]
+        hook_q: Q after conv, pre-GQA-expansion [batch, seq, n_k_heads, head_k_dim]
+            Note: on standard attn layers, hook_q is post-projection. Here it's
+            post-conv — use hook_q_pre_conv for the projection-only output.
+        hook_k: K after conv [batch, seq, n_k_heads, head_k_dim]
+        hook_v: V after conv [batch, seq, n_v_heads, head_v_dim]
+        hook_beta_logit: pre-sigmoid write gate logit, per v-head [batch, seq, n_v_heads]
+        hook_beta: write strength sigmoid(b), per v-head [batch, seq, n_v_heads]
+        hook_log_decay: log-space decay g (NEGATIVE; multiplicative decay = exp(g)),
+            per v-head [batch, seq, n_v_heads]
+        hook_recurrence_out: output of linear recurrence [batch, seq, n_v_heads, head_v_dim]
+        hook_gate_input: z tensor (pre-silu) for GatedRMSNorm [batch, seq, n_v_heads, head_v_dim]
+        hook_out: final output to residual stream [batch, seq, d_model]
+
+    During generation (cache_params present), only hook_in/hook_out fire.
+
+    Property aliases:
+        W_in_proj_qkvz, W_in_proj_ba, W_out_proj, A_log, dt_bias
+    """
+
+    hook_aliases = {
+        "hook_linear_attn_in": "hook_in",
+        "hook_linear_attn_out": "hook_out",
+    }
+
+    property_aliases = {
+        "W_in_proj_qkvz": "in_proj_qkvz.weight",
+        "W_in_proj_ba": "in_proj_ba.weight",
+        "W_out_proj": "out_proj.weight",
+        "A_log": "A_log",
+        "dt_bias": "dt_bias",
+    }
+
+    def __init__(
+        self,
+        name: str,
+        config: Optional[Any] = None,
+        submodules: Optional[Dict[str, GeneralizedComponent]] = None,
+        **kwargs,
+    ):
+        super().__init__(name, config=config, submodules=submodules or {}, **kwargs)
+        # Pre-conv (after projection split, before causal conv mixes positions)
+        self.hook_q_pre_conv = HookPoint()
+        self.hook_k_pre_conv = HookPoint()
+        self.hook_v_pre_conv = HookPoint()
+        # Post-conv (pre-GQA-expansion, pre-recurrence)
+        self.hook_q = HookPoint()
+        self.hook_k = HookPoint()
+        self.hook_v = HookPoint()
+        # Gate parameters (per v-head)
+        self.hook_beta_logit = HookPoint()
+        self.hook_beta = HookPoint()
+        self.hook_log_decay = HookPoint()
+        # Recurrence output + gated norm input
+        self.hook_recurrence_out = HookPoint()
+        self.hook_gate_input = HookPoint()
+
+    def forward(self, *args: Any, **kwargs: Any) -> Any:
+        if self.original_component is None:
+            raise RuntimeError(f"Original component not set for {self.name}.")
+
+        if kwargs.get("cache_params") is not None:
+            return self._native_forward(*args, **kwargs)
+        return self._hooked_forward(*args, **kwargs)
+
+    def _native_forward(self, *args: Any, **kwargs: Any) -> Any:
+        """Delegate to HF with hook_in/hook_out only (generation path)."""
+        assert self.original_component is not None
+        if "hidden_states" in kwargs:
+            kwargs["hidden_states"] = self.hook_in(kwargs["hidden_states"])
+        elif len(args) > 0 and isinstance(args[0], torch.Tensor):
+            args = (self.hook_in(args[0]),) + args[1:]
+
+        output = self.original_component(*args, **kwargs)
+
+        if isinstance(output, tuple) and len(output) > 0:
+            first = output[0]
+            if isinstance(first, torch.Tensor):
+                return (self.hook_out(first),) + output[1:]
+            return output
+        if isinstance(output, torch.Tensor):
+            return self.hook_out(output)
+        return output
+
+    def _hooked_forward(self, *args: Any, **kwargs: Any) -> Any:
+        """Reimplemented forward exposing all intermediate states (prefill)."""
+        hf: Any = self.original_component
+
+        if "hidden_states" in kwargs:
+            hidden_states = kwargs["hidden_states"]
+        elif len(args) > 0 and isinstance(args[0], torch.Tensor):
+            hidden_states = args[0]
+        else:
+            raise ValueError("Could not find hidden_states")
+
+        attention_mask = kwargs.get("attention_mask")
+        if attention_mask is not None:
+            # Inline masking — avoids hard dependency on qwen3_next module
+            hidden_states = hidden_states * attention_mask.unsqueeze(-1)
+
+        hidden_states = self.hook_in(hidden_states)
+        batch_size, seq_len, _ = hidden_states.shape
+
+        # --- Projections (two layouts: fused vs split) ---
+        if hasattr(hf, "in_proj_qkvz"):
+            # Qwen3Next: fused Q+K+V+Z projection, fused beta+alpha
+            projected_qkvz = hf.in_proj_qkvz(hidden_states)
+            projected_ba = hf.in_proj_ba(hidden_states)
+            query, key, value, z, b, a = hf.fix_query_key_value_ordering(
+                projected_qkvz, projected_ba
+            )
+        else:
+            # Qwen3.5: separate projections (in_proj_qkv, in_proj_z, in_proj_b, in_proj_a)
+            mixed_qkv_flat = hf.in_proj_qkv(hidden_states)
+            z = hf.in_proj_z(hidden_states).reshape(batch_size, seq_len, -1, hf.head_v_dim)
+            b = hf.in_proj_b(hidden_states)
+            a = hf.in_proj_a(hidden_states)
+            # Split QKV and reshape to per-head for pre-conv hooks
+            q_flat, k_flat, v_flat = torch.split(
+                mixed_qkv_flat, [hf.key_dim, hf.key_dim, hf.value_dim], dim=-1
+            )
+            query = q_flat.reshape(batch_size, seq_len, -1, hf.head_k_dim)
+            key = k_flat.reshape(batch_size, seq_len, -1, hf.head_k_dim)
+            value = v_flat.reshape(batch_size, seq_len, -1, hf.head_v_dim)
+
+        # --- Pre-conv hooks (per-head shape, before conv mixes positions) ---
+        query = self.hook_q_pre_conv(query)
+        key = self.hook_k_pre_conv(key)
+        value = self.hook_v_pre_conv(value)
+
+        # Flatten for conv
+        query, key, value = (x.reshape(x.shape[0], x.shape[1], -1) for x in (query, key, value))
+
+        # --- Causal Convolution ---
+        mixed_qkv = torch.cat((query, key, value), dim=-1).transpose(1, 2)
+        if hf.causal_conv1d_fn is not None:
+            mixed_qkv = hf.causal_conv1d_fn(
+                x=mixed_qkv,
+                weight=hf.conv1d.weight.squeeze(1),
+                bias=hf.conv1d.bias,
+                activation=hf.activation,
+                seq_idx=None,
+            )
+        else:
+            mixed_qkv = F.silu(hf.conv1d(mixed_qkv)[:, :, :seq_len])
+        mixed_qkv = mixed_qkv.transpose(1, 2)
+
+        # Split post-conv into per-head Q, K, V
+        query, key, value = torch.split(
+            mixed_qkv,
+            [hf.key_dim, hf.key_dim, hf.value_dim],
+            dim=-1,
+        )
+        query = query.reshape(batch_size, seq_len, -1, hf.head_k_dim)
+        key = key.reshape(batch_size, seq_len, -1, hf.head_k_dim)
+        value = value.reshape(batch_size, seq_len, -1, hf.head_v_dim)
+
+        # --- Post-conv hooks (pre-GQA-expansion, pre-recurrence) ---
+        query = self.hook_q(query)
+        key = self.hook_k(key)
+        value = self.hook_v(value)
+
+        # --- Gate parameters (per v-head) ---
+        b = self.hook_beta_logit(b)
+        beta = self.hook_beta(b.sigmoid())
+
+        # g is log-space decay (NEGATIVE); multiplicative decay = exp(g)
+        g = -hf.A_log.float().exp() * F.softplus(a.float() + hf.dt_bias)
+        g = self.hook_log_decay(g)
+
+        # GQA expansion (Q/K from n_k_heads → n_v_heads)
+        if hf.num_v_heads // hf.num_k_heads > 1:
+            repeat = hf.num_v_heads // hf.num_k_heads
+            query = query.repeat_interleave(repeat, dim=2)
+            key = key.repeat_interleave(repeat, dim=2)
+
+        # --- Core linear recurrence (opaque fused kernel) ---
+        core_out, _ = hf.chunk_gated_delta_rule(
+            query,
+            key,
+            value,
+            g=g,
+            beta=beta,
+            initial_state=None,
+            output_final_state=False,
+            use_qk_l2norm_in_kernel=True,
+        )
+        core_out = self.hook_recurrence_out(core_out)
+
+        # --- Gated RMSNorm: norm(core_out) * silu(z) ---
+        z = self.hook_gate_input(z)
+        z_shape = z.shape
+        core_out = hf.norm(
+            core_out.reshape(-1, core_out.shape[-1]),
+            z.reshape(-1, z.shape[-1]),
+        )
+        core_out = core_out.reshape(z_shape).reshape(batch_size, seq_len, -1)
+
+        # --- Output projection ---
+        output = hf.out_proj(core_out)
+        return self.hook_out(output)
+
+    def compute_effective_attention(
+        self,
+        cache: "ActivationCache",
+        layer_idx: int,
+    ) -> torch.Tensor:
+        """Materialize the effective attention matrix from cached hook values.
+
+        The gated delta rule recurrence is::
+
+            S_t = exp(g_t) * S_{t-1} + beta_t * v_t @ k_t^T
+            o_t = S_t^T @ q_t
+
+        The effective attention M[i,j] = contribution of input j to output i::
+
+            M[i,j] = (q_i^T @ k_j) * beta_j * prod_{t=j+1}^{i} exp(g_t)
+
+        **Approximation note:** The fused kernel applies L2-normalization to Q
+        and K internally (``use_qk_l2norm_in_kernel=True``). The hooked Q/K are
+        pre-normalization, so this reconstruction diverges when Q/K norms vary
+        significantly across positions/heads. Accuracy is best when Q/K norms
+        are roughly uniform (common after training converges).
+
+        Args:
+            cache: ActivationCache from ``run_with_cache``.
+            layer_idx: Block index for this linear_attn layer.
+
+        Returns:
+            ``[batch, n_v_heads, seq, seq]`` causal matrix (upper triangle zero).
+
+        Cost is O(batch * n_heads * seq^2); use on short sequences.
+        """
+        prefix = f"blocks.{layer_idx}.linear_attn"
+        q_key = f"{prefix}.hook_q"
+        k_key = f"{prefix}.hook_k"
+        beta_key = f"{prefix}.hook_beta"
+        decay_key = f"{prefix}.hook_log_decay"
+
+        for key in [q_key, k_key, beta_key, decay_key]:
+            if key not in cache:
+                raise RuntimeError(
+                    f"compute_effective_attention needs {key!r} in cache. "
+                    "Run run_with_cache() on the bridge first."
+                )
+
+        # [batch, seq, n_k_heads, head_k_dim] — pre-GQA-expansion
+        q = cache[q_key].float()
+        k = cache[k_key].float()
+        beta = cache[beta_key].float()  # [batch, seq, n_v_heads]
+        g = cache[decay_key].float()  # [batch, seq, n_v_heads]
+
+        # GQA expansion to match n_v_heads
+        if q.shape[2] < beta.shape[-1]:
+            repeat = beta.shape[-1] // q.shape[2]
+            q = q.repeat_interleave(repeat, dim=2)
+            k = k.repeat_interleave(repeat, dim=2)
+
+        batch, seq, n_heads, d_head = q.shape
+
+        # QK similarity: [batch, n_heads, seq_i, seq_j]
+        q_perm = q.permute(0, 2, 1, 3)
+        k_perm = k.permute(0, 2, 1, 3)
+        qk = torch.matmul(q_perm, k_perm.transpose(-2, -1))
+
+        # Cumulative decay: L[i,j] = exp(sum g[j+1..i])
+        g_perm = g.permute(0, 2, 1)  # [batch, n_heads, seq]
+        cumsum_g = torch.cumsum(g_perm, dim=-1)
+        L_log = cumsum_g[:, :, :, None] - cumsum_g[:, :, None, :]
+
+        causal_mask = torch.tril(torch.ones(seq, seq, dtype=torch.bool, device=q.device))
+        L = torch.where(causal_mask[None, None], torch.exp(L_log), torch.zeros_like(L_log))
+
+        # M[i,j] = qk[i,j] * beta[j] * L[i,j]
+        beta_col = beta.permute(0, 2, 1)[:, :, None, :]
+        return qk * beta_col * L
diff --git a/transformer_lens/model_bridge/generalized_components/position_embeddings_attention.py b/transformer_lens/model_bridge/generalized_components/position_embeddings_attention.py
index 33f6dd21f..135ab0d17 100644
--- a/transformer_lens/model_bridge/generalized_components/position_embeddings_attention.py
+++ b/transformer_lens/model_bridge/generalized_components/position_embeddings_attention.py
@@ -15,6 +15,7 @@
 import torch
 import transformers.models.gemma2.modeling_gemma2 as gemma2_module
 
+from transformer_lens.hook_points import HookPoint
 from transformer_lens.model_bridge.generalized_components.attention import (
     AttentionBridge,
 )
@@ -112,21 +113,29 @@ class PositionEmbeddingsAttentionBridge(PositionEmbeddingHooksMixin, AttentionBr
     """
 
     def __init__(
-        self, name: str, config: Any, submodules: Optional[Dict[str, Any]] = None, **kwargs
+        self,
+        name: str,
+        config: Any,
+        submodules: Optional[Dict[str, Any]] = None,
+        optional: bool = False,
+        # Accepted for caller compatibility (Granite passes these explicitly)
+        # but always forced to True — this bridge reimplements attention.
+        requires_attention_mask: bool = True,
+        requires_position_embeddings: bool = True,
+        **kwargs,  # absorb any other AttentionBridge kwargs callers may pass
     ):
-        """Initialize Gemma-3 attention bridge.
-
-        Args:
-            name: Component name
-            config: Model configuration
-            submodules: Dictionary of subcomponents
-            **kwargs: Additional arguments passed to AttentionBridge
-        """
-        kwargs["requires_position_embeddings"] = True
-        kwargs["requires_attention_mask"] = True
-        kwargs["maintain_native_attention"] = True
-        super().__init__(name, config, submodules, **kwargs)
+        super().__init__(
+            name,
+            config,
+            submodules,
+            requires_position_embeddings=True,
+            requires_attention_mask=True,
+            maintain_native_attention=True,
+            optional=optional,
+        )
         self._init_position_embedding_hooks()
+        if getattr(config, "gated_q_proj", False):
+            self.hook_q_gate = HookPoint()
 
     def set_original_component(self, component: torch.nn.Module) -> None:
         """Set the original HF component and register for rotary hook firing.
@@ -201,19 +210,34 @@ def forward(self, *args: Any, **kwargs: Any) -> Any:
         key_states = hf_attn.k_proj(hidden_states)
         value_states = hf_attn.v_proj(hidden_states)
 
+        # Gated q_proj (Qwen3.5/Qwen3Next): q_proj outputs [Q|gate] interleaved
+        # per head. cfg.gated_q_proj is set by the adapter. The actual split only
+        # triggers if the output is 2x the standard width (n_heads * head_dim).
+        # In processed mode, preprocess_weights slices q_proj to standard width
+        # so this naturally passes through.
+        q_gate = None
+        if getattr(self.config, "gated_q_proj", False):
+            q_dim = query_states.shape[-1]
+            n_heads = getattr(self.config, "n_heads", q_dim // head_dim)
+            standard_q_dim = n_heads * head_dim
+            if q_dim == standard_q_dim * 2:
+                query_states, q_gate = torch.chunk(
+                    query_states.view(*input_shape, -1, head_dim * 2), 2, dim=-1
+                )
+                q_gate = q_gate.reshape(*input_shape, -1)
+                query_states = query_states.reshape(*input_shape, -1)
+
         has_q_norm = hasattr(hf_attn, "q_norm") and hf_attn.q_norm is not None
         has_k_norm = hasattr(hf_attn, "k_norm") and hf_attn.k_norm is not None
         applied_pre_reshape_norm = False
 
         if has_q_norm:
             try:
-                # Try pre-reshape norm (OLMo 2 style: norm on flat [batch, seq, hidden])
                 query_states = hf_attn.q_norm(query_states)
                 if has_k_norm:
                     key_states = hf_attn.k_norm(key_states)
                 applied_pre_reshape_norm = True
             except RuntimeError:
-                # Shape mismatch — this model uses post-reshape norms
                 pass
 
         query_states = query_states.view(hidden_shape).transpose(1, 2)
@@ -306,6 +330,12 @@ def forward(self, *args: Any, **kwargs: Any) -> Any:
         attn_output = attn_output.transpose(1, 2).contiguous()
         attn_output = attn_output.reshape(*input_shape, -1)
 
+        # --- Gated attention (Qwen3.5/Qwen3Next) ---
+        if q_gate is not None:
+            if hasattr(self, "hook_q_gate"):
+                q_gate = self.hook_q_gate(q_gate)
+            attn_output = attn_output * torch.sigmoid(q_gate)
+
         # --- Output Projection ---
         # Different architectures name this differently: o_proj (Llama, Gemma, Qwen),
         # dense (Phi), out_proj (others)
diff --git a/transformer_lens/model_bridge/supported_architectures/granite.py b/transformer_lens/model_bridge/supported_architectures/granite.py
index f85ef850d..c46081b0b 100644
--- a/transformer_lens/model_bridge/supported_architectures/granite.py
+++ b/transformer_lens/model_bridge/supported_architectures/granite.py
@@ -51,6 +51,7 @@ def _setup_common_config(self, cfg: Any) -> None:
         self.cfg.gated_mlp = True
         self.cfg.attn_only = False
         self.cfg.uses_rms_norm = True
+        self.cfg.default_prepend_bos = False
         self.cfg.eps_attr = "variance_epsilon"
 
         self.default_config = {
@@ -65,11 +66,12 @@ def _setup_common_config(self, cfg: Any) -> None:
             self.default_config["n_key_value_heads"] = cfg.n_key_value_heads
             self.cfg.n_key_value_heads = cfg.n_key_value_heads
 
-    def _build_attention_bridge(self) -> PositionEmbeddingsAttentionBridge:
+    def _build_attention_bridge(self, optional: bool = False) -> PositionEmbeddingsAttentionBridge:
         """Build the standard Granite attention bridge."""
         return PositionEmbeddingsAttentionBridge(
             name="self_attn",
             config=self.cfg,
+            optional=optional,
             submodules={
                 "q": LinearBridge(name="q_proj"),
                 "k": LinearBridge(name="k_proj"),
@@ -124,11 +126,11 @@ def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> No
 
         if bridge_model is not None and hasattr(bridge_model, "blocks"):
             for block in bridge_model.blocks:
-                if hasattr(block, "attn"):
+                if "attn" in block._modules:
                     block.attn.set_rotary_emb(rotary_emb)
 
         try:
             attn_bridge = self.get_generalized_component("blocks.0.attn")
             attn_bridge.set_rotary_emb(rotary_emb)
-        except (AttributeError, KeyError):
+        except (AttributeError, KeyError, ValueError):
             pass
diff --git a/transformer_lens/model_bridge/supported_architectures/granite_moe_hybrid.py b/transformer_lens/model_bridge/supported_architectures/granite_moe_hybrid.py
index 2c776365b..53229252e 100644
--- a/transformer_lens/model_bridge/supported_architectures/granite_moe_hybrid.py
+++ b/transformer_lens/model_bridge/supported_architectures/granite_moe_hybrid.py
@@ -1,13 +1,11 @@
 """Granite MoE Hybrid architecture adapter.
 
-GraniteMoeHybridForCausalLM is a hybrid Mamba + Attention architecture with
-Sparse Mixture of Experts. Layers alternate between Mamba SSM blocks and
-standard attention blocks, with a shared MLP and optional sparse MoE on
-every layer.
-
-Since self_attn is None on Mamba layers and mamba is None on attention
-layers, we only map submodules that exist on ALL layers (norms, shared_mlp,
-block_sparse_moe). The HF native forward handles mamba/attention dispatch.
+Hybrid Mamba2 + Attention with Sparse MoE. Most layers are Mamba SSM blocks;
+a few are standard attention (determined by config.layer_types). Every layer
+has a shared MLP and optional sparse MoE.
+
+Both attention and Mamba are mapped as optional — each present only on its
+respective layer type. Mamba hooks expose in_proj, conv1d, and inner_norm.
 """
 
 from typing import Any
@@ -21,53 +19,55 @@
     MoEBridge,
     RMSNormalizationBridge,
     RotaryEmbeddingBridge,
+    SSM2MixerBridge,
     UnembeddingBridge,
 )
+from transformer_lens.model_bridge.generalized_components.depthwise_conv1d import (
+    DepthwiseConv1DBridge,
+)
 from transformer_lens.model_bridge.supported_architectures.granite import (
     GraniteArchitectureAdapter,
 )
 
 
 class GraniteMoeHybridArchitectureAdapter(GraniteArchitectureAdapter):
-    """Architecture adapter for IBM Granite MoE Hybrid models.
-
-    Hybrid Mamba2 + Attention architecture with Sparse MoE. Most layers are Mamba
-    SSM blocks; a few are standard attention (determined by config.layer_types).
+    """Hybrid Mamba2 + Attention with Sparse MoE.
 
-    Since self_attn is None on Mamba layers and mamba is None on attention layers,
-    we only map submodules present on ALL layers (norms, shared_mlp, MoE). The HF
-    native forward handles mamba/attention dispatch internally.
-
-    Hook coverage:
-    - Block-level: hook_resid_pre, hook_resid_post on every layer
-    - Normalization: ln1 (input_layernorm), ln2 (post_attention_layernorm)
-    - MLP: shared_mlp input/output hooks
-    - MoE: block_sparse_moe input/output and router_scores hooks
-    - Attention/Mamba internals are NOT individually hooked (conditional per layer)
+    Attention is optional (absent on Mamba layers). shared_mlp and MoE are
+    universal. Inherits Granite config and attention bridge construction.
     """
 
     def __init__(self, cfg: Any) -> None:
-        """Initialize the Granite MoE Hybrid architecture adapter."""
-        # Call ArchitectureAdapter.__init__ directly, not GraniteArchitectureAdapter.__init__,
-        # because we need to customize the setup sequence
         ArchitectureAdapter.__init__(self, cfg)
-
         self._setup_common_config(cfg)
 
-        # Hybrid may use "rope" or "nope" (no positional embeddings)
         pos_emb_type = getattr(cfg, "position_embedding_type", "rope")
         if pos_emb_type != "rope":
             self.cfg.positional_embedding_type = "none"
 
-        # No attention weight conversions — attn Q/K/V aren't mapped as submodules
+        self.supports_fold_ln = False
         self.weight_processing_conversions = {}
         self.component_mapping = self._build_component_mapping()
 
+    def _build_mamba_bridge(self) -> SSM2MixerBridge:
+        """Mamba-2 mixer bridge with in_proj, conv1d, inner_norm hooks."""
+        return SSM2MixerBridge(
+            name="mamba",
+            config=self.cfg,
+            optional=True,
+            submodules={
+                "in_proj": LinearBridge(name="in_proj"),
+                "conv1d": DepthwiseConv1DBridge(name="conv1d"),
+                "inner_norm": LinearBridge(name="norm"),
+            },
+        )
+
     def _build_component_mapping(self) -> dict:
-        """Build component mapping with only universal (all-layer) submodules."""
-        block_submodules = {
+        block_submodules: dict = {
             "ln1": RMSNormalizationBridge(name="input_layernorm", config=self.cfg),
             "ln2": RMSNormalizationBridge(name="post_attention_layernorm", config=self.cfg),
+            "attn": self._build_attention_bridge(optional=True),
+            "mamba": self._build_mamba_bridge(),
             "shared_mlp": MLPBridge(
                 name="shared_mlp",
                 config=self.cfg,
@@ -87,12 +87,9 @@ def _build_component_mapping(self) -> dict:
                 config=self.cfg,
             )
 
-        mapping = {
+        mapping: dict = {
             "embed": EmbeddingBridge(name="model.embed_tokens"),
-            "blocks": BlockBridge(
-                name="model.layers",
-                submodules=block_submodules,
-            ),
+            "blocks": BlockBridge(name="model.layers", submodules=block_submodules),
             "ln_final": RMSNormalizationBridge(name="model.norm", config=self.cfg),
             "unembed": UnembeddingBridge(name="lm_head", config=self.cfg),
         }
@@ -101,10 +98,3 @@ def _build_component_mapping(self) -> dict:
             mapping["rotary_emb"] = RotaryEmbeddingBridge(name="model.rotary_emb", config=self.cfg)
 
         return mapping
-
-    def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> None:
-        """No-op for hybrid models.
-
-        Hybrid models don't map attention as a submodule (it's conditional per
-        layer), so there are no rotary embedding references to set up.
-        """
diff --git a/transformer_lens/model_bridge/supported_architectures/qwen3.py b/transformer_lens/model_bridge/supported_architectures/qwen3.py
index 8dcc1d6d3..4676d1175 100644
--- a/transformer_lens/model_bridge/supported_architectures/qwen3.py
+++ b/transformer_lens/model_bridge/supported_architectures/qwen3.py
@@ -1,7 +1,14 @@
-"""Qwen3 architecture adapter."""
+"""Qwen3 architecture adapter.
+
+Base adapter for the Qwen3 model family. Provides shared config setup,
+attention bridge construction, and setup_component_testing used by
+Qwen3, Qwen3.5, and Qwen3Next variants.
+"""
 
 from typing import Any
 
+import torch
+
 from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter
 from transformer_lens.model_bridge.generalized_components import (
     BlockBridge,
@@ -12,33 +19,33 @@
     RotaryEmbeddingBridge,
     UnembeddingBridge,
 )
+from transformer_lens.model_bridge.generalized_components.gated_delta_net import (
+    GatedDeltaNetBridge,
+)
 from transformer_lens.model_bridge.generalized_components.position_embeddings_attention import (
     PositionEmbeddingsAttentionBridge,
 )
 
 
 class Qwen3ArchitectureAdapter(ArchitectureAdapter):
-    """Architecture adapter for Qwen3 models.
-
-    Qwen3 is architecturally similar to Gemma3:
-    - Uses RMSNorm for all normalizations
-    - Has Q/K normalization within attention (RMSNorm on head dimension)
-    - Uses rotary position embeddings (RoPE)
-    - Requires position_embeddings and attention_mask in forward pass
-    - Uses gated MLP (gate_proj + up_proj -> down_proj)
-    - No biases on any linear layers
-
-    Key differences from Qwen2:
-    - Qwen3 has q_norm and k_norm layers in attention (Qwen2 doesn't)
-    - Qwen3 requires position_embeddings parameter (like Gemma3)
-    - Uses PositionEmbeddingsAttentionBridge instead of AttentionBridge
+    """Architecture adapter for Qwen3 dense models.
+
+    RMSNorm, RoPE, GQA, Q/K head norms, gated MLP. No biases.
+    Serves as base class for Qwen3.5 and Qwen3Next hybrid variants.
     """
 
-    def __init__(self, cfg: Any) -> None:
-        """Initialize the Qwen3 architecture adapter."""
+    def __init__(self, cfg: Any, *, hybrid: bool = False) -> None:
         super().__init__(cfg)
-
-        # Set config variables for weight processing
+        self._setup_qwen3_config(cfg)
+        if hybrid:
+            self.supports_fold_ln = False
+            self.weight_processing_conversions: dict = {}
+        else:
+            self.weight_processing_conversions = {**self._qkvo_weight_conversions()}
+        self.component_mapping = self._build_component_mapping(hybrid=hybrid)
+
+    def _setup_qwen3_config(self, cfg: Any) -> None:
+        """Config shared across all Qwen3 variants (dense, hybrid, MoE)."""
         self.cfg.normalization_type = "RMS"
         self.cfg.positional_embedding_type = "rotary"
         self.cfg.final_rms = True
@@ -46,85 +53,105 @@ def __init__(self, cfg: Any) -> None:
         self.cfg.attn_only = False
         self.cfg.uses_rms_norm = True
         self.cfg.default_prepend_bos = False
-
-        # Use eager attention to support output_attentions for hook_attn_scores and hook_pattern
-        # SDPA doesn't support output_attentions, which is required for HookedTransformer compatibility
         self.cfg.attn_implementation = "eager"
 
-        self.weight_processing_conversions = {
-            **self._qkvo_weight_conversions(),
+        if hasattr(cfg, "n_key_value_heads") and cfg.n_key_value_heads is not None:
+            self.cfg.n_key_value_heads = cfg.n_key_value_heads
+
+    def _build_attention_bridge(self, optional: bool = False) -> PositionEmbeddingsAttentionBridge:
+        """Standard Qwen3 attention bridge with Q/K norms."""
+        return PositionEmbeddingsAttentionBridge(
+            name="self_attn",
+            config=self.cfg,
+            optional=optional,
+            submodules={
+                "q": LinearBridge(name="q_proj"),
+                "k": LinearBridge(name="k_proj"),
+                "v": LinearBridge(name="v_proj"),
+                "o": LinearBridge(name="o_proj"),
+                "q_norm": RMSNormalizationBridge(name="q_norm", config=self.cfg),
+                "k_norm": RMSNormalizationBridge(name="k_norm", config=self.cfg),
+            },
+        )
+
+    def _build_mlp_bridge(self):
+        """Dense gated MLP (gate_proj + up_proj -> down_proj). Override for MoE."""
+        return GatedMLPBridge(
+            name="mlp",
+            config=self.cfg,
+            submodules={
+                "gate": LinearBridge(name="gate_proj"),
+                "in": LinearBridge(name="up_proj"),
+                "out": LinearBridge(name="down_proj"),
+            },
+        )
+
+    def _build_linear_attn_bridge(self, optional: bool = False) -> GatedDeltaNetBridge:
+        """GatedDeltaNet linear-attention bridge for hybrid variants."""
+        return GatedDeltaNetBridge(
+            name="linear_attn",
+            config=self.cfg,
+            optional=optional,
+        )
+
+    def _build_component_mapping(self, *, hybrid: bool = False) -> dict:
+        """Parametric component mapping. hybrid=True adds optional linear_attn."""
+        block_submodules: dict = {
+            "ln1": RMSNormalizationBridge(name="input_layernorm", config=self.cfg),
+            "ln2": RMSNormalizationBridge(name="post_attention_layernorm", config=self.cfg),
+            "attn": self._build_attention_bridge(optional=hybrid),
+            "mlp": self._build_mlp_bridge(),
         }
-
-        # Set up component mapping
-        self.component_mapping = {
+        if hybrid:
+            block_submodules["linear_attn"] = self._build_linear_attn_bridge(optional=True)
+        return {
             "embed": EmbeddingBridge(name="model.embed_tokens"),
             "rotary_emb": RotaryEmbeddingBridge(name="model.rotary_emb", config=self.cfg),
-            "blocks": BlockBridge(
-                name="model.layers",
-                submodules={
-                    "ln1": RMSNormalizationBridge(name="input_layernorm", config=self.cfg),
-                    "ln2": RMSNormalizationBridge(name="post_attention_layernorm", config=self.cfg),
-                    "attn": PositionEmbeddingsAttentionBridge(
-                        name="self_attn",
-                        config=self.cfg,
-                        submodules={
-                            "q": LinearBridge(name="q_proj"),
-                            "k": LinearBridge(name="k_proj"),
-                            "v": LinearBridge(name="v_proj"),
-                            "o": LinearBridge(name="o_proj"),
-                            "q_norm": RMSNormalizationBridge(name="q_norm", config=self.cfg),
-                            "k_norm": RMSNormalizationBridge(name="k_norm", config=self.cfg),
-                        },
-                    ),
-                    "mlp": GatedMLPBridge(
-                        name="mlp",
-                        config=self.cfg,
-                        submodules={
-                            "gate": LinearBridge(name="gate_proj"),
-                            "in": LinearBridge(name="up_proj"),
-                            "out": LinearBridge(name="down_proj"),
-                        },
-                    ),
-                },
-            ),
+            "blocks": BlockBridge(name="model.layers", submodules=block_submodules),
             "ln_final": RMSNormalizationBridge(name="model.norm", config=self.cfg),
             "unembed": UnembeddingBridge(name="lm_head"),
         }
 
     def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> None:
-        """Set up rotary embedding references for Qwen3 component testing.
-
-        Qwen3 uses RoPE (Rotary Position Embeddings). We set the rotary_emb on
-        all attention bridge instances for component testing.
-
-        We also force the HF model to use "eager" attention to match the bridge's
-        implementation. The bridge uses "eager" to support output_attentions for hooks.
-
-        Args:
-            hf_model: The HuggingFace Qwen3 model instance
-            bridge_model: The TransformerBridge model (if available, set rotary_emb on actual instances)
-        """
-        # Get rotary embedding instance from the model
+        """Set eager attn on HF model and rotary_emb on attention bridges."""
         rotary_emb = hf_model.model.rotary_emb
 
-        # Force HF model to use "eager" attention to match bridge implementation
-        # Bridge uses "eager" to support output_attentions for hook compatibility
         if hasattr(hf_model, "config") and hasattr(hf_model.config, "_attn_implementation"):
             hf_model.config._attn_implementation = "eager"
 
-        # Also set on all attention layers
         if hasattr(hf_model, "model") and hasattr(hf_model.model, "layers"):
             for layer in hf_model.model.layers:
                 if hasattr(layer, "self_attn") and hasattr(layer.self_attn, "config"):
                     layer.self_attn.config._attn_implementation = "eager"
 
-        # Set rotary_emb on actual bridge instances in bridge_model if available
         if bridge_model is not None and hasattr(bridge_model, "blocks"):
-            # Set on each layer's actual attention bridge instance
             for block in bridge_model.blocks:
-                if hasattr(block, "attn"):
+                if "attn" in block._modules:
                     block.attn.set_rotary_emb(rotary_emb)
 
-        # Also set on the template for get_generalized_component() calls
-        attn_bridge = self.get_generalized_component("blocks.0.attn")
-        attn_bridge.set_rotary_emb(rotary_emb)
+        # Set on template for get_generalized_component() calls
+        # Set on template — may not exist in hybrid adapters
+        mapping = self.component_mapping or {}
+        blocks_template = mapping.get("blocks") if isinstance(mapping, dict) else None
+        if blocks_template and "attn" in getattr(blocks_template, "submodules", {}):
+            try:
+                attn_template = self.get_generalized_component("blocks.0.attn")
+                attn_template.set_rotary_emb(rotary_emb)
+            except (ValueError, AttributeError, KeyError):
+                pass
+
+    @staticmethod
+    def _preprocess_gated_q_proj(
+        state_dict: dict[str, torch.Tensor], n_heads: int, d_head: int
+    ) -> dict[str, torch.Tensor]:
+        """Slice query half from gated q_proj.weight (interleaved per-head layout).
+
+        q_proj.weight has shape (n_heads * d_head * 2, hidden_size) with
+        interleaved [query, gate] rows per head. Extracts query-only half.
+        """
+        keys_to_update = [k for k in state_dict if k.endswith(".self_attn.q_proj.weight")]
+        for key in keys_to_update:
+            w = state_dict[key]
+            w = w.view(n_heads, d_head * 2, -1)
+            state_dict[key] = w[:, :d_head, :].reshape(n_heads * d_head, -1)
+        return state_dict
diff --git a/transformer_lens/model_bridge/supported_architectures/qwen3_5.py b/transformer_lens/model_bridge/supported_architectures/qwen3_5.py
index b1e71e9f3..1ef0913bf 100644
--- a/transformer_lens/model_bridge/supported_architectures/qwen3_5.py
+++ b/transformer_lens/model_bridge/supported_architectures/qwen3_5.py
@@ -1,175 +1,57 @@
-"""Qwen3_5 architecture adapter.
+"""Qwen3.5 architecture adapter.
 
-Qwen3_5ForCausalLM is a hybrid linear-attention + full-attention architecture
-with a dense gated MLP on every layer. Layers follow a repeating pattern of
-3 GatedDeltaNet (linear attention) layers followed by 1 standard full-attention
-layer (every 4th layer by default).
-
-Since self_attn is absent on linear-attention layers, we only map submodules
-that exist on ALL layers (norms, MLP). The HF native forward handles
-linear/full attention dispatch internally, and GatedMLPBridge maps the dense
-gate_proj/up_proj/down_proj structure on every layer.
-
-Hook coverage:
-- Block-level: hook_resid_pre, hook_resid_post on every layer
-- Normalization: ln1 (input_layernorm), ln2 (post_attention_layernorm)
-- MLP: hook_in, hook_out via GatedMLPBridge (gate_proj, up_proj, down_proj)
-- Attention internals are NOT individually hooked (self_attn absent on
-  linear-attention layers; mapping it would crash on those layers)
-
-Optional parameters:
-- n_key_value_heads: only set when using GQA (num_key_value_heads != num_attention_heads)
+Hybrid linear-attention (GatedDeltaNet) + full-attention with dense gated MLP.
+3 linear-attn layers per 1 full-attn layer. Extends Qwen3 base with
+optional attention mapping and fold_ln disabled.
 """
 
 from typing import Any
 
 import torch
 
-from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter
-from transformer_lens.model_bridge.generalized_components import (
-    BlockBridge,
-    EmbeddingBridge,
-    GatedMLPBridge,
-    LinearBridge,
-    RMSNormalizationBridge,
-    RotaryEmbeddingBridge,
-    UnembeddingBridge,
+from transformer_lens.model_bridge.supported_architectures.qwen3 import (
+    Qwen3ArchitectureAdapter,
 )
 
 
-class Qwen3_5ArchitectureAdapter(ArchitectureAdapter):
-    """Architecture adapter for Qwen3_5 models.
-
-    Qwen3_5ForCausalLM is a hybrid linear-attention + full-attention
-    architecture with dense gated MLPs, sharing the same hybrid design as
-    Qwen3Next but replacing the sparse MoE MLP with a standard dense MLP:
-    - Uses RMSNorm for all normalizations
-    - Uses rotary position embeddings (RoPE) with partial rotation
-    - Every 4th layer is a full-attention layer (self_attn); the rest are
-      GatedDeltaNet linear-attention layers (linear_attn)
-    - Uses dense gated MLP (gate_proj + up_proj -> down_proj) on ALL layers
-    - No biases on any linear layers
-    - Full-attention layers have Q/K normalization (q_norm, k_norm)
-    - Full-attention q_proj outputs n_heads * head_dim * 2 (interleaved
-      query+gate layout); the preprocess_weights method slices the query half
-
-    Since self_attn is absent on linear-attention layers, only universally
-    present submodules (norms, MLP) are mapped as block submodules. The HF
-    native forward handles per-layer attention dispatch internally.
+class Qwen3_5ArchitectureAdapter(Qwen3ArchitectureAdapter):
+    """Hybrid linear-attention + full-attention with dense gated MLP.
 
-    Optional parameters:
-    - n_key_value_heads: set when num_key_value_heads != num_attention_heads (GQA)
+    Inherits Qwen3 config/attention/MLP structure. Differences:
+    - Attention + linear_attn are optional (per-layer type)
+    - Gated q_proj (2x wide) sliced by preprocess_weights for weight analysis
     """
 
-    def __init__(self, cfg: Any) -> None:
-        """Initialize the Qwen3_5 architecture adapter."""
-        super().__init__(cfg)
-
-        # Core config attributes
-        self.cfg.normalization_type = "RMS"
-        self.cfg.positional_embedding_type = "rotary"
-        self.cfg.final_rms = True
-        self.cfg.gated_mlp = True
-        self.cfg.attn_only = False
-        self.cfg.uses_rms_norm = True
-        self.cfg.default_prepend_bos = False
-
-        # Disable fold_ln: ln1 is followed by self_attn on full-attention
-        # layers and by linear_attn (GatedDeltaNet) on linear-attention layers,
-        # but neither is mapped as a bridge submodule (see class docstring for
-        # why). With no bridge-mapped target to fold into, the standard fold_ln
-        # pass leaves LN weights in an inconsistent state and the processed
-        # bridge output diverges from the unprocessed / HF output. Skipping
-        # fold_ln keeps processed-mode forward passes numerically equivalent.
-        self.supports_fold_ln = False
+    _MIN_TRANSFORMERS_VERSION = "5.2.0"
 
-        # Use eager attention to support output_attentions for hook_attn_scores
-        # and hook_pattern. SDPA doesn't support output_attentions.
-        self.cfg.attn_implementation = "eager"
-
-        # GQA: only set n_key_value_heads when using grouped-query attention
-        if hasattr(cfg, "n_key_value_heads") and cfg.n_key_value_heads is not None:
-            self.cfg.n_key_value_heads = cfg.n_key_value_heads
+    def __init__(self, cfg: Any) -> None:
+        import transformers
 
-        self.weight_processing_conversions: dict = {}
-        self.component_mapping: dict = {
-            "embed": EmbeddingBridge(name="model.embed_tokens"),
-            "rotary_emb": RotaryEmbeddingBridge(name="model.rotary_emb", config=self.cfg),
-            "blocks": BlockBridge(
-                name="model.layers",
-                submodules={
-                    "ln1": RMSNormalizationBridge(name="input_layernorm", config=self.cfg),
-                    "ln2": RMSNormalizationBridge(name="post_attention_layernorm", config=self.cfg),
-                    # Dense gated MLP present on every layer (unlike Qwen3Next's MoE).
-                    # gate_proj + up_proj feed into down_proj via SwiGLU activation.
-                    "mlp": GatedMLPBridge(
-                        name="mlp",
-                        config=self.cfg,
-                        submodules={
-                            "gate": LinearBridge(name="gate_proj"),
-                            "in": LinearBridge(name="up_proj"),
-                            "out": LinearBridge(name="down_proj"),
-                        },
-                    ),
-                },
-            ),
-            "ln_final": RMSNormalizationBridge(name="model.norm", config=self.cfg),
-            "unembed": UnembeddingBridge(name="lm_head"),
-        }
+        if transformers.__version__ < self._MIN_TRANSFORMERS_VERSION:
+            raise ImportError(
+                f"Qwen3.5 requires transformers >= {self._MIN_TRANSFORMERS_VERSION} "
+                f"(installed: {transformers.__version__}). "
+                f"Upgrade with: pip install 'transformers>={self._MIN_TRANSFORMERS_VERSION}'"
+            )
+        setattr(cfg, "gated_q_proj", True)
+        super().__init__(cfg, hybrid=True)
 
     def prepare_loading(self, model_name: str, model_kwargs: dict) -> None:
-        """Swap the multimodal Qwen3_5Config for its text-only Qwen3_5TextConfig.
-
-        Published Qwen3.5 checkpoints (e.g. Qwen/Qwen3.5-0.8B) carry
-        model_type='qwen3_5' and architectures=['Qwen3_5ForConditionalGeneration'].
-        AutoModelForCausalLM would load the full VLM (Qwen3_5ForConditionalGeneration)
-        with its vision tower, wasting memory and failing the bridge.
+        """Swap multimodal Qwen3_5Config for text-only Qwen3_5TextConfig.
 
-        Instead we replace model_kwargs['config'] with the nested text_config so
-        AutoModelForCausalLM loads Qwen3_5ForCausalLM (text only).
+        Published checkpoints carry architectures=['Qwen3_5ForConditionalGeneration'].
+        We replace config with text_config so AutoModelForCausalLM loads the
+        text-only Qwen3_5ForCausalLM.
         """
         config = model_kwargs.get("config")
         if config is not None and hasattr(config, "text_config"):
             model_kwargs["config"] = config.text_config
 
-    def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> None:
-        """No-op for hybrid models.
-
-        Hybrid models don't map attention as a block submodule (self_attn is
-        absent on linear-attention layers), so there are no rotary embedding
-        references to set up.
-
-        Note: to find which layers are full_attention at runtime, use:
-            layer_types = getattr(hf_model.config, "layer_types", [])
-            first_full_attn_idx = next(
-                i for i, t in enumerate(layer_types) if t == "full_attention"
-            )
-        Do NOT use hf_model.config.full_attention_interval -- it is not stored
-        on the config object (consumed during __init__ to build layer_types).
-        """
-
     def preprocess_weights(self, state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
-        """Slice query half from q_proj.weight (interleaved per-head layout).
-
-        In Qwen3_5, q_proj.weight has shape (n_heads * head_dim * 2, hidden_size).
-        Rows are organized as per-head interleaved:
-          head_0_query (d_head rows), head_0_gate (d_head rows),
-          head_1_query (d_head rows), head_1_gate (d_head rows), ...
-
-        A naive first-half slice would be wrong. We must reshape by head, then
-        take the first d_head rows of each head (the query half).
+        """Slice query half from gated q_proj.weight for weight-space analysis.
 
-        Note: since self_attn is NOT currently mapped as a bridge submodule,
-        these weights will not be loaded by the bridge. This method is included
-        for correctness and forward-compatibility.
+        In processed mode, W_Q is the pure query projection (for composition
+        scores, logit lens). Gate signal available in unprocessed mode on
+        full-attention layers via blocks.N.attn.hook_q_gate.
         """
-        n_heads = self.cfg.n_heads
-        d_head = self.cfg.d_head
-        keys_to_update = [k for k in state_dict if k.endswith(".self_attn.q_proj.weight")]
-        for key in keys_to_update:
-            w = state_dict[key]  # shape: (n_heads * d_head * 2, hidden_size)
-            # Reshape to expose per-head layout
-            w = w.view(n_heads, d_head * 2, -1)
-            # Take only the first d_head rows of each head (query half)
-            state_dict[key] = w[:, :d_head, :].reshape(n_heads * d_head, -1)
-        return state_dict
+        return self._preprocess_gated_q_proj(state_dict, self.cfg.n_heads, self.cfg.d_head)
diff --git a/transformer_lens/model_bridge/supported_architectures/qwen3_next.py b/transformer_lens/model_bridge/supported_architectures/qwen3_next.py
index 53e18dce1..31e1be3cd 100644
--- a/transformer_lens/model_bridge/supported_architectures/qwen3_next.py
+++ b/transformer_lens/model_bridge/supported_architectures/qwen3_next.py
@@ -1,163 +1,34 @@
 """Qwen3Next architecture adapter.
 
-Qwen3NextForCausalLM is a hybrid linear-attention + full-attention architecture
-with a sparse Mixture-of-Experts MLP on every layer. Layers alternate between
-GatedDeltaNet (linear attention) and standard full attention blocks, while the
-MLP is always a Qwen3NextSparseMoeBlock (gate router + batched experts +
-shared expert).
-
-Since self_attn is absent on linear-attention layers, we only map submodules
-that exist on ALL layers (norms, MLP). The HF native forward handles
-linear/full attention dispatch internally, and MoEBridge delegates the entire
-MoE forward (including router, experts, and shared expert) to the native
-implementation.
-
-Hook coverage:
-- Block-level: hook_resid_pre, hook_resid_post on every layer
-- Normalization: ln1 (input_layernorm), ln2 (post_attention_layernorm)
-- MLP: hook_in, hook_out on the MoE block (MoEBridge)
-- Attention internals are NOT individually hooked (self_attn absent on
-  linear-attention layers; mapping it would crash on those layers)
-- Expert-level internals are NOT individually hooked (batched expert params
-  live inside Qwen3NextExperts; MoEBridge delegates to HF forward)
-
-Optional parameters:
-- n_key_value_heads: only set when using GQA (num_key_value_heads != num_attention_heads)
+Hybrid linear-attention (GatedDeltaNet) + full-attention with sparse MoE MLP.
+3 linear-attn layers per 1 full-attn layer. Extends Qwen3 base with
+optional attention mapping, MoE MLP, and fold_ln disabled.
 """
 
 from typing import Any
 
 import torch
 
-from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter
-from transformer_lens.model_bridge.generalized_components import (
-    BlockBridge,
-    EmbeddingBridge,
-    MoEBridge,
-    RMSNormalizationBridge,
-    RotaryEmbeddingBridge,
-    UnembeddingBridge,
+from transformer_lens.model_bridge.generalized_components import MoEBridge
+from transformer_lens.model_bridge.supported_architectures.qwen3 import (
+    Qwen3ArchitectureAdapter,
 )
 
 
-class Qwen3NextArchitectureAdapter(ArchitectureAdapter):
-    """Architecture adapter for Qwen3Next models.
-
-    Qwen3NextForCausalLM is a hybrid linear-attention + full-attention
-    architecture with sparse MoE MLPs, sharing the same design as Qwen3.5:
-    - Uses RMSNorm for all normalizations
-    - Uses rotary position embeddings (RoPE) with partial rotation
-    - Every 4th layer is a full-attention layer (self_attn); the rest are
-      GatedDeltaNet linear-attention layers (linear_attn)
-    - Uses Qwen3NextSparseMoeBlock on ALL layers (decoder_sparse_step=1 and
-      mlp_only_layers=[] on every real checkpoint). The MoE block contains a
-      top-K router, batched Qwen3NextExperts (experts.gate_up_proj /
-      experts.down_proj as 3D tensors), plus a shared_expert (gated MLP) and
-      shared_expert_gate. Each expert is internally a gated MLP.
-    - No biases on any linear layers
-    - Full-attention layers have Q/K normalization (q_norm, k_norm)
-    - Full-attention q_proj outputs n_heads * head_dim * 2 (interleaved
-      query+gate layout); the preprocess_weights method slices the query half
+class Qwen3NextArchitectureAdapter(Qwen3ArchitectureAdapter):
+    """Hybrid linear-attention + full-attention with sparse MoE MLP.
 
-    Since self_attn is absent on linear-attention layers, only universally
-    present submodules (norms, MLP) are mapped as block submodules. The HF
-    native forward handles per-layer attention dispatch internally, and
-    MoEBridge delegates the MoE forward pass (including router + experts +
-    shared expert) to the native Qwen3NextSparseMoeBlock implementation.
-
-    Optional parameters:
-    - n_key_value_heads: set when num_key_value_heads != num_attention_heads (GQA)
+    Same hybrid design as Qwen3.5 but with MoE instead of dense MLP.
     """
 
     def __init__(self, cfg: Any) -> None:
-        """Initialize the Qwen3Next architecture adapter."""
-        super().__init__(cfg)
-
-        # Core config attributes
-        self.cfg.normalization_type = "RMS"
-        self.cfg.positional_embedding_type = "rotary"
-        self.cfg.final_rms = True
-        self.cfg.gated_mlp = True
-        self.cfg.attn_only = False
-        self.cfg.uses_rms_norm = True
-        self.cfg.default_prepend_bos = False
-
-        # Disable fold_ln: ln1 is followed by self_attn on full-attention
-        # layers and by linear_attn (GatedDeltaNet) on linear-attention layers,
-        # but neither is mapped as a bridge submodule (see class docstring for
-        # why). With no bridge-mapped target to fold into, the standard fold_ln
-        # pass leaves LN weights in an inconsistent state and the processed
-        # bridge output diverges from the unprocessed / HF output. Skipping
-        # fold_ln keeps processed-mode forward passes numerically equivalent.
-        self.supports_fold_ln = False
-
-        # Use eager attention to support output_attentions for hook_attn_scores
-        # and hook_pattern. SDPA doesn't support output_attentions.
-        self.cfg.attn_implementation = "eager"
+        setattr(cfg, "gated_q_proj", True)
+        super().__init__(cfg, hybrid=True)
 
-        # GQA: only set n_key_value_heads when using grouped-query attention
-        if hasattr(cfg, "n_key_value_heads") and cfg.n_key_value_heads is not None:
-            self.cfg.n_key_value_heads = cfg.n_key_value_heads
-
-        self.weight_processing_conversions: dict = {}
-        self.component_mapping: dict = {
-            "embed": EmbeddingBridge(name="model.embed_tokens"),
-            "rotary_emb": RotaryEmbeddingBridge(name="model.rotary_emb", config=self.cfg),
-            "blocks": BlockBridge(
-                name="model.layers",
-                submodules={
-                    "ln1": RMSNormalizationBridge(name="input_layernorm", config=self.cfg),
-                    "ln2": RMSNormalizationBridge(name="post_attention_layernorm", config=self.cfg),
-                    # Qwen3NextSparseMoeBlock has a custom Qwen3NextTopKRouter
-                    # (not an nn.Linear) as `gate`, plus batched experts and a
-                    # shared expert. MoEBridge wraps the whole MoE module and
-                    # delegates to HF's native forward, so we don't enumerate
-                    # the internal structure here.
-                    "mlp": MoEBridge(name="mlp", config=self.cfg),
-                },
-            ),
-            "ln_final": RMSNormalizationBridge(name="model.norm", config=self.cfg),
-            "unembed": UnembeddingBridge(name="lm_head"),
-        }
-
-    def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> None:
-        """No-op for hybrid models.
-
-        Hybrid models don't map attention as a block submodule (self_attn is
-        absent on linear-attention layers), so there are no rotary embedding
-        references to set up.
-
-        Note: to find which layers are full_attention at runtime, use:
-            layer_types = getattr(hf_model.config, "layer_types", [])
-            first_full_attn_idx = next(
-                i for i, t in enumerate(layer_types) if t == "full_attention"
-            )
-        Do NOT use hf_model.config.full_attention_interval -- it is not stored
-        on the config object (consumed during __init__ to build layer_types).
-        """
+    def _build_mlp_bridge(self):
+        """Sparse MoE MLP (router + batched experts + shared expert)."""
+        return MoEBridge(name="mlp", config=self.cfg)
 
     def preprocess_weights(self, state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
-        """Slice query half from q_proj.weight (interleaved per-head layout).
-
-        In Qwen3Next, q_proj.weight has shape (n_heads * head_dim * 2, hidden_size).
-        Rows are organized as per-head interleaved:
-          head_0_query (d_head rows), head_0_gate (d_head rows),
-          head_1_query (d_head rows), head_1_gate (d_head rows), ...
-
-        A naive first-half slice would be wrong. We must reshape by head, then
-        take the first d_head rows of each head (the query half).
-
-        Note: since self_attn is NOT currently mapped as a bridge submodule,
-        these weights will not be loaded by the bridge. This method is included
-        for correctness and forward-compatibility.
-        """
-        n_heads = self.cfg.n_heads
-        d_head = self.cfg.d_head
-        keys_to_update = [k for k in state_dict if k.endswith(".self_attn.q_proj.weight")]
-        for key in keys_to_update:
-            w = state_dict[key]  # shape: (n_heads * d_head * 2, hidden_size)
-            # Reshape to expose per-head layout
-            w = w.view(n_heads, d_head * 2, -1)
-            # Take only the first d_head rows of each head (query half)
-            state_dict[key] = w[:, :d_head, :].reshape(n_heads * d_head, -1)
-        return state_dict
+        """Slice query half from gated q_proj.weight for weight-space analysis."""
+        return self._preprocess_gated_q_proj(state_dict, self.cfg.n_heads, self.cfg.d_head)
diff --git a/transformer_lens/tools/model_registry/data/architecture_gaps.json b/transformer_lens/tools/model_registry/data/architecture_gaps.json
index f3eb11de9..6261a9a65 100644
--- a/transformer_lens/tools/model_registry/data/architecture_gaps.json
+++ b/transformer_lens/tools/model_registry/data/architecture_gaps.json
@@ -1,18 +1,18 @@
 {
-  "generated_at": "2026-04-10",
+  "generated_at": "2026-04-14",
   "scan_info": {
-    "total_scanned": 5436,
+    "total_scanned": 5633,
     "task_filter": "text-generation",
     "min_downloads": 500,
-    "scan_duration_seconds": 3.9
+    "scan_duration_seconds": 4.2
   },
-  "total_unsupported_architectures": 401,
-  "total_unsupported_models": 1459,
+  "total_unsupported_architectures": 416,
+  "total_unsupported_models": 1400,
   "gaps": [
     {
       "architecture_id": "Qwen3_5ForConditionalGeneration",
-      "total_models": 67,
-      "total_downloads": 140710,
+      "total_models": 72,
+      "total_downloads": 146334,
       "min_param_count": 211968832,
       "sample_models": [
         "Tesslate/OmniCoder-9B",
@@ -20,18 +20,37 @@
         "nightmedia/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-qx64-hi-mlx",
         "Brooooooklyn/Qwen3.5-27B-unsloth-mlx",
         "aifeifei798/Qwen3.5-Queen-27B",
-        "Jackrong/Qwen3.5-2B-Claude-4.6-Opus-Reasoning-Distilled",
         "Brooooooklyn/Qwen3.5-9B-unsloth-mlx",
-        "alexcovo/qwen35-9b-mlx-turboquant-tq3",
         "bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX",
-        "Jackrong/Qwen3.5-9B-Neo"
+        "Jackrong/Qwen3.5-2B-Claude-4.6-Opus-Reasoning-Distilled",
+        "alexcovo/qwen35-9b-mlx-turboquant-tq3",
+        "Oysiyl/qwen3.5-27b-unslop-good-lora-v1"
+      ],
+      "relevancy_score": 91.0
+    },
+    {
+      "architecture_id": "Gemma4ForConditionalGeneration",
+      "total_models": 64,
+      "total_downloads": 90296,
+      "min_param_count": 738022691,
+      "sample_models": [
+        "dealignai/Gemma-4-31B-JANG_4M-Uncensored",
+        "0xSero/gemma-4-21b-a4b-it-REAP",
+        "InfinimindCreations/gemma-4-E4B-it-uncensored",
+        "TrevorJS/gemma-4-26B-A4B-it-uncensored",
+        "WWTCyberLab/gemma-4-31B-it-abliterated",
+        "WWTCyberLab/gemma-4-26B-A4B-it-abliterated",
+        "TrevorJS/gemma-4-31B-it-uncensored",
+        "TrevorJS/gemma-4-E4B-it-uncensored",
+        "InfinimindCreations/gemma-4-31B-it-uncensored",
+        "TrevorJS/gemma-4-E2B-it-uncensored"
       ],
-      "relevancy_score": 91.5
+      "relevancy_score": 84.9
     },
     {
       "architecture_id": "DeepseekV3ForCausalLM",
-      "total_models": 48,
-      "total_downloads": 6449394,
+      "total_models": 46,
+      "total_downloads": 6840308,
       "min_param_count": 1656048,
       "sample_models": [
         "deepseek-ai/DeepSeek-R1",
@@ -39,43 +58,24 @@
         "deepseek-ai/DeepSeek-V3",
         "deepseek-ai/DeepSeek-V3-0324",
         "moonshotai/Kimi-K2-Instruct-0905",
-        "deepseek-ai/DeepSeek-V3.1",
-        "ai-sage/GigaChat3-10B-A1.8B",
         "moonshotai/Kimi-K2-Instruct",
+        "deepseek-ai/DeepSeek-V3.1",
         "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
-        "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528"
-      ],
-      "relevancy_score": 87.2
-    },
-    {
-      "architecture_id": "Qwen3MoeForCausalLM",
-      "total_models": 45,
-      "total_downloads": 5469133,
-      "min_param_count": 2574656,
-      "sample_models": [
-        "Qwen/Qwen3-30B-A3B",
-        "Qwen/Qwen3-Coder-30B-A3B-Instruct",
-        "Qwen/Qwen3-30B-A3B-Instruct-2507",
-        "Qwen/Qwen3-235B-A22B",
-        "trl-internal-testing/tiny-Qwen3MoeForCausalLM",
-        "Qwen/Qwen3-30B-A3B-Thinking-2507",
-        "Qwen/Qwen3-235B-A22B-Instruct-2507",
-        "Qwen/Qwen3-Coder-480B-A35B-Instruct",
-        "Qwen/Qwen3-235B-A22B-Thinking-2507",
-        "NVFP4/Qwen3-30B-A3B-Instruct-2507-FP4"
+        "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528",
+        "moonshotai/Moonlight-16B-A3B-Instruct"
       ],
-      "relevancy_score": 84.9
+      "relevancy_score": 83.1
     },
     {
       "architecture_id": "NemotronHForCausalLM",
-      "total_models": 40,
-      "total_downloads": 3187865,
+      "total_models": 41,
+      "total_downloads": 3587883,
       "min_param_count": 4221480,
       "sample_models": [
         "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
-        "nvidia/NVIDIA-Nemotron-Nano-9B-v2-Japanese",
         "nvidia/NVIDIA-Nemotron-Nano-9B-v2",
         "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
+        "nvidia/NVIDIA-Nemotron-Nano-9B-v2-Japanese",
         "nvidia/Nemotron-Cascade-2-30B-A3B",
         "nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
         "unsloth/NVIDIA-Nemotron-3-Nano-4B",
@@ -83,69 +83,31 @@
         "empero-ai/openNemo-9B",
         "cpagac/Nemotron-Nano-9B-v2-heretic"
       ],
-      "relevancy_score": 80.3
-    },
-    {
-      "architecture_id": "Qwen3_5ForCausalLM",
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-      "min_param_count": 752393024,
-      "sample_models": [
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-        "GoodStartLabs/gin-rummy-hbc-qwen3.5-0.8b",
-        "aifeifei798/Darkidol-Ballad-27B",
-        "brocchirodrigo/anotaai-ajuda-qwen3_5_Q4",
-        "kai-os/Carnice-9b",
-        "llmfan46/Darkidol-Ballad-27B-ultra-uncensored-heretic-v1",
-        "llmfan46/Darkidol-Ballad-27B-ultra-uncensored-heretic-v2",
-        "Phonsiri/Qwen3.5-9B-Thai-Law-Base",
-        "continuum-ai/qwen3.5-4b-code-forged",
-        "aifeifei798/Darkidol-Ballad-9B"
-      ],
-      "relevancy_score": 80.2
+      "relevancy_score": 78.6
     },
     {
       "architecture_id": "Lfm2ForCausalLM",
       "total_models": 40,
-      "total_downloads": 1395683,
+      "total_downloads": 1626247,
       "min_param_count": 274754048,
       "sample_models": [
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         "LiquidAI/LFM2.5-1.2B-Instruct",
         "LiquidAI/LFM2-1.2B",
-        "LiquidAI/LFM2-350M",
         "LiquidAI/LFM2.5-350M",
+        "LiquidAI/LFM2-350M",
         "LiquidAI/LFM2.5-1.2B-Thinking",
         "LiquidAI/LFM2-2.6B-Exp",
         "LiquidAI/LFM2.5-1.2B-Base",
         "LiquidAI/LFM2-700M",
         "unsloth/LFM2.5-1.2B-Instruct"
       ],
-      "relevancy_score": 78.4
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-    {
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-      "total_models": 37,
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-      "min_param_count": 2084387402,
-      "sample_models": [
-        "dealignai/Gemma-4-31B-JANG_4M-Uncensored",
-        "0xSero/gemma-4-21b-a4b-it-REAP",
-        "InfinimindCreations/gemma-4-E4B-it-uncensored",
-        "lthn/lemma",
-        "TrevorJS/gemma-4-26B-A4B-it-uncensored",
-        "livadies/gemma-4-E2B-Ghetto-NF4",
-        "Greytechai/Gemma-4-31B-JANG_4M-CRACK",
-        "WWTCyberLab/gemma-4-31B-it-abliterated",
-        "WWTCyberLab/gemma-4-26B-A4B-it-abliterated",
-        "InfinimindCreations/gemma-4-31B-it-uncensored"
-      ],
-      "relevancy_score": 65.1
+      "relevancy_score": 76.3
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     {
       "architecture_id": "QWenLMHeadModel",
       "total_models": 22,
-      "total_downloads": 495498,
+      "total_downloads": 522223,
       "min_param_count": 19545408,
       "sample_models": [
         "cckevinn/SeeClick",
@@ -156,91 +118,53 @@
         "Qwen/Qwen-1_8B-Chat",
         "Qwen/Qwen-14B-Chat",
         "Qwen/Qwen-14B",
-        "Xingyu-Zheng/Qwen-VL-Chat",
-        "Qwen/Qwen-72B"
-      ],
-      "relevancy_score": 64.0
-    },
-    {
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-      "sample_models": [
-        "internlm/internlm2-chat-7b",
-        "internlm/internlm2_5-7b-chat",
-        "internlm/internlm2-7b",
-        "internlm/internlm2-20b",
-        "internlm/internlm2-base-7b",
-        "internlm/internlm2-chat-20b",
-        "internlm/internlm2-base-20b",
-        "chujiezheng/internlm2-chat-20b-ExPO",
-        "chujiezheng/internlm2-chat-7b-ExPO",
-        "internlm/internlm2-1_8b"
+        "Qwen/Qwen-Audio-Chat",
+        "Xingyu-Zheng/Qwen-VL-Chat"
       ],
-      "relevancy_score": 63.2
+      "relevancy_score": 62.5
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     {
-      "architecture_id": "GPTBigCodeForCausalLM",
-      "total_models": 24,
-      "total_downloads": 109509,
-      "min_param_count": 1845928,
+      "architecture_id": "DeepseekV32ForCausalLM",
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+      "min_param_count": 136559748,
       "sample_models": [
-        "bigcode/gpt_bigcode-santacoder",
-        "bigcode/tiny_starcoder_py",
-        "bigcode/starcoder",
-        "bigcode/starcoderbase-1b",
-        "ibm-granite/granite-20b-code-base-8k",
-        "GeorgiaTechResearchInstitute/starcoder-gpteacher-code-instruct",
-        "HuggingFaceH4/starchat-alpha",
-        "defog/sqlcoder2",
-        "HuggingFaceH4/starchat-beta",
-        "ibm-granite/granite-20b-code-instruct-8k"
+        "deepseek-ai/DeepSeek-V3.2",
+        "deepseek-ai/DeepSeek-V3.2-Exp",
+        "deepseek-ai/DeepSeek-V3.2-Speciale",
+        "deepseek-ai/DeepSeek-Math-V2",
+        "zianglih/DeepSeek-V3.2-6layer-last-1-bf16",
+        "exolabs/DeepSeek-V3.2_bf16",
+        "deepseek-ai/DeepSeek-V3.2-Exp-Base",
+        "cs2764/DeepSeek-V3.2_dq4-mlx",
+        "zianglih/DeepSeek-V3.2-MXFP8",
+        "hyper-accel/tiny-random-deepseek-v32"
       ],
-      "relevancy_score": 62.0
+      "relevancy_score": 62.5
     },
     {
-      "architecture_id": "T5GemmaForConditionalGeneration",
-      "total_models": 14,
-      "total_downloads": 1037477,
-      "min_param_count": 312517632,
+      "architecture_id": "JambaForCausalLM",
+      "total_models": 28,
+      "total_downloads": 48565,
+      "min_param_count": 127679344,
       "sample_models": [
-        "google/t5gemma-s-s-prefixlm",
-        "google/t5gemma-9b-9b-ul2",
-        "google/t5gemma-b-b-ul2",
-        "google/t5gemma-2b-2b-prefixlm",
-        "google/t5gemma-2b-2b-ul2",
-        "google/t5gemma-l-l-ul2-it",
-        "google/t5gemma-ml-ml-ul2-it",
-        "google/t5gemma-b-b-prefixlm",
-        "google/t5gemma-s-s-prefixlm-it",
-        "google/t5gemma-9b-9b-prefixlm"
-      ],
-      "relevancy_score": 60.3
-    },
-    {
-      "architecture_id": "XGLMForCausalLM",
-      "total_models": 18,
-      "total_downloads": 223424,
-      "min_param_count": 162256896,
-      "sample_models": [
-        "facebook/xglm-564M",
-        "facebook/incoder-1B",
-        "facebook/xglm-7.5B",
-        "facebook/xglm-4.5B",
-        "facebook/xglm-1.7B",
-        "KoboldAI/fairseq-dense-2.7B",
-        "KoboldAI/fairseq-dense-125M",
-        "KoboldAI/fairseq-dense-355M",
-        "KoboldAI/fairseq-dense-13B",
-        "KoboldAI/fairseq-dense-1.3B"
+        "ai21labs/AI21-Jamba-Mini-1.5",
+        "ai21labs/Jamba-tiny-random",
+        "ai21labs/AI21-Jamba-Mini-1.6",
+        "ai21labs/AI21-Jamba-Large-1.5",
+        "ai21labs/AI21-Jamba2-3B",
+        "ai21labs/AI21-Jamba-Large-1.6",
+        "ai21labs/Jamba-v0.1",
+        "ai21labs/AI21-Jamba2-Mini",
+        "ai21labs/AI21-Jamba-Reasoning-3B",
+        "microsoft/Dayhoff-170M-GRS-112000"
       ],
-      "relevancy_score": 59.6
+      "relevancy_score": 61.1
     },
     {
       "architecture_id": "Glm4MoeForCausalLM",
-      "total_models": 14,
-      "total_downloads": 742282,
+      "total_models": 16,
+      "total_downloads": 751068,
       "min_param_count": 2572352,
       "sample_models": [
         "zai-org/GLM-4.5-Air",
@@ -249,72 +173,55 @@
         "zai-org/GLM-4.5",
         "zai-org/GLM-4.6",
         "np-cr/testing-glm4-moe",
-        "ArliAI/GLM-4.6-Derestricted-v3",
         "PrimeIntellect/GLM-0.5B",
+        "ArliAI/GLM-4.6-Derestricted-v3",
         "zai-org/GLM-4.5-Air-Base",
         "PrimeIntellect/INTELLECT-3"
       ],
       "relevancy_score": 59.6
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     {
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-      "sample_models": [
-        "ai21labs/AI21-Jamba-Mini-1.5",
-        "ai21labs/Jamba-tiny-random",
-        "ai21labs/AI21-Jamba-Mini-1.6",
-        "ai21labs/AI21-Jamba-Large-1.5",
-        "ai21labs/AI21-Jamba-Large-1.6",
-        "ai21labs/AI21-Jamba2-3B",
-        "ai21labs/Jamba-v0.1",
-        "ai21labs/AI21-Jamba2-Mini",
-        "ai21labs/AI21-Jamba-Reasoning-3B",
-        "microsoft/Dayhoff-170m-GR"
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-    },
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-      "architecture_id": "DeepseekV32ForCausalLM",
-      "total_models": 8,
-      "total_downloads": 1446699,
-      "min_param_count": 136559748,
+      "architecture_id": "T5GemmaForConditionalGeneration",
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       "sample_models": [
-        "deepseek-ai/DeepSeek-V3.2",
-        "deepseek-ai/DeepSeek-V3.2-Exp",
-        "deepseek-ai/DeepSeek-V3.2-Speciale",
-        "deepseek-ai/DeepSeek-Math-V2",
-        "exolabs/DeepSeek-V3.2_bf16",
-        "deepseek-ai/DeepSeek-V3.2-Exp-Base",
-        "hyper-accel/tiny-random-deepseek-v32",
-        "cs2764/DeepSeek-V3.2_dq4-mlx"
+        "google/t5gemma-s-s-prefixlm",
+        "google/t5gemma-b-b-ul2",
+        "google/t5gemma-9b-9b-ul2",
+        "google/t5gemma-2b-2b-prefixlm",
+        "google/t5gemma-2b-2b-ul2",
+        "google/t5gemma-l-l-ul2-it",
+        "google/t5gemma-ml-ml-ul2-it",
+        "google/t5gemma-b-b-prefixlm",
+        "google/t5gemma-s-s-prefixlm-it",
+        "google/t5gemma-9b-9b-prefixlm"
       ],
-      "relevancy_score": 57.0
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     {
-      "architecture_id": "BaichuanForCausalLM",
-      "total_models": 15,
-      "total_downloads": 115111,
-      "min_param_count": 16204352,
+      "architecture_id": "GPTBigCodeForCausalLM",
+      "total_models": 24,
+      "total_downloads": 39369,
+      "min_param_count": 1845928,
       "sample_models": [
-        "baichuan-inc/Baichuan2-7B-Chat",
-        "baichuan-inc/Baichuan2-13B-Chat",
-        "baichuan-inc/Baichuan-13B-Chat",
-        "baichuan-inc/Baichuan2-7B-Base",
-        "baichuan-inc/Baichuan2-13B-Base",
-        "sakuraumi/Sakura-13B-Galgame",
-        "zxbsmk/NSFW_13B_sft",
-        "katuni4ka/tiny-random-baichuan2",
-        "baichuan-inc/Baichuan-13B-Base",
-        "FreedomIntelligence/HuatuoGPT2-7B"
+        "bigcode/starcoder",
+        "bigcode/starcoderbase-1b",
+        "ibm-granite/granite-20b-code-base-8k",
+        "GeorgiaTechResearchInstitute/starcoder-gpteacher-code-instruct",
+        "HuggingFaceH4/starchat-alpha",
+        "defog/sqlcoder2",
+        "ibm-granite/granite-20b-code-instruct-8k",
+        "HuggingFaceH4/starchat-beta",
+        "LoupGarou/WizardCoder-Guanaco-15B-V1.0",
+        "openchat/opencoderplus"
       ],
-      "relevancy_score": 56.1
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     },
     {
       "architecture_id": "SmolLM3ForCausalLM",
-      "total_models": 7,
-      "total_downloads": 1155432,
+      "total_models": 8,
+      "total_downloads": 1123995,
       "min_param_count": 8245568,
       "sample_models": [
         "HuggingFaceTB/SmolLM3-3B",
@@ -322,15 +229,35 @@
         "optimum-internal-testing/tiny-random-SmolLM3ForCausalLM",
         "unsloth/SmolLM3-3B",
         "onnx-internal-testing/tiny-random-SmolLM3ForCausalLM",
-        "MInAlA/smollm3-dpo-merged",
-        "N-Bot-Int/SmolSam3-MEMGRPO"
+        "MInAlA/SmolLM3-3B-DPO-merged",
+        "N-Bot-Int/SmolSam3-MEMGRPO",
+        "yujiepan/smollm3-tiny-random"
+      ],
+      "relevancy_score": 55.5
+    },
+    {
+      "architecture_id": "GlmMoeDsaForCausalLM",
+      "total_models": 10,
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+      "min_param_count": 162774148,
+      "sample_models": [
+        "zai-org/GLM-5",
+        "zai-org/GLM-5.1",
+        "cs2764/GLM-5-abliterated-dq4-mlx",
+        "livadies/GLM-5.1-Ghetto-MoE-2-Experts",
+        "unsloth/GLM-5",
+        "JANGQ-AI/GLM-5.1-JANG_2S",
+        "0xSero/GLM-5-REAP-381B",
+        "JANGQ-AI/GLM-5.1-JANG_1L",
+        "cs2764/GLM-5-abliterated-dq3-mlx",
+        "hyper-accel/tiny-random-glm-moe-dsa"
       ],
-      "relevancy_score": 55.9
+      "relevancy_score": 55.3
     },
     {
       "architecture_id": "BartForConditionalGeneration",
       "total_models": 9,
-      "total_downloads": 599134,
+      "total_downloads": 692599,
       "min_param_count": 6044480,
       "sample_models": [
         "KomeijiForce/bart-large-emojilm",
@@ -343,12 +270,31 @@
         "Tianlin668/MentalBART",
         "KomeijiForce/bart-large-emojilm-e2t"
       ],
-      "relevancy_score": 55.7
+      "relevancy_score": 55.0
+    },
+    {
+      "architecture_id": "BaichuanForCausalLM",
+      "total_models": 15,
+      "total_downloads": 117761,
+      "min_param_count": 16204352,
+      "sample_models": [
+        "baichuan-inc/Baichuan2-7B-Chat",
+        "baichuan-inc/Baichuan2-13B-Chat",
+        "baichuan-inc/Baichuan-13B-Chat",
+        "baichuan-inc/Baichuan2-7B-Base",
+        "baichuan-inc/Baichuan2-13B-Base",
+        "zxbsmk/NSFW_13B_sft",
+        "sakuraumi/Sakura-13B-Galgame",
+        "baichuan-inc/Baichuan-13B-Base",
+        "katuni4ka/tiny-random-baichuan2",
+        "FreedomIntelligence/HuatuoGPT2-7B"
+      ],
+      "relevancy_score": 54.9
     },
     {
       "architecture_id": "FalconH1ForCausalLM",
       "total_models": 15,
-      "total_downloads": 76731,
+      "total_downloads": 77408,
       "min_param_count": 91131072,
       "sample_models": [
         "tiiuae/Falcon-H1-0.5B-Base",
@@ -359,80 +305,45 @@
         "tiiuae/Falcon-H1-1.5B-Base",
         "tiiuae/Falcon-H1-Tiny-90M-Instruct",
         "tiiuae/Falcon-H1R-7B",
-        "tiiuae/Falcon-H1-1.5B-Deep-Instruct",
-        "tiiuae/Falcon-H1-3B-Instruct"
-      ],
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-    },
-    {
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-      "sample_models": [
-        "trl-internal-testing/tiny-CohereForCausalLM",
-        "CohereLabs/aya-expanse-8b",
-        "CohereLabs/c4ai-command-r-v01",
-        "CohereLabs/aya-23-8B",
-        "NLPark/AnFeng_v3_Avocet",
-        "CohereLabs/aya-expanse-32b",
-        "CohereLabs/aya-23-35B",
-        "CohereLabs/c4ai-command-r-plus-08-2024",
-        "CohereLabs/c4ai-command-r-08-2024",
-        "CohereLabs/c4ai-command-r-plus"
+        "tiiuae/Falcon-H1-3B-Instruct",
+        "tiiuae/Falcon-H1-1.5B-Deep-Instruct"
       ],
-      "relevancy_score": 53.9
+      "relevancy_score": 54.0
     },
     {
       "architecture_id": "H2OVLChatModel",
       "total_models": 2,
-      "total_downloads": 2131755,
+      "total_downloads": 2009160,
       "min_param_count": 826295808,
       "sample_models": [
         "h2oai/h2ovl-mississippi-800m",
         "h2oai/h2ovl-mississippi-2b"
       ],
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-    },
-    {
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-      "sample_models": [
-        "openbmb/MiniCPM-2B-sft-bf16",
-        "openbmb/MiniCPM4.1-8B",
-        "openbmb/MiniCPM-1B-sft-bf16",
-        "openbmb/MiniCPM4-0.5B",
-        "openbmb/MiniCPM-MoE-8x2B",
-        "openbmb/MiniCPM-S-1B-sft",
-        "katuni4ka/tiny-random-minicpm",
-        "openbmb/MiniCPM4-8B",
-        "openbmb/MiniCPM-2B-sft-fp32",
-        "openbmb/MiniCPM-2B-dpo-bf16"
-      ],
-      "relevancy_score": 53.6
+      "relevancy_score": 53.0
     },
     {
-      "architecture_id": "GlmMoeDsaForCausalLM",
-      "total_models": 7,
-      "total_downloads": 411962,
-      "min_param_count": 162774148,
+      "architecture_id": "DFlashDraftModel",
+      "total_models": 11,
+      "total_downloads": 131573,
+      "min_param_count": 473995264,
       "sample_models": [
-        "zai-org/GLM-5",
-        "zai-org/GLM-5.1",
-        "cs2764/GLM-5-abliterated-dq4-mlx",
-        "0xSero/GLM-5-REAP-381B",
-        "unsloth/GLM-5",
-        "cs2764/GLM-5-abliterated-dq3-mlx",
-        "hyper-accel/tiny-random-glm-moe-dsa"
+        "z-lab/Qwen3-4B-DFlash-b16",
+        "z-lab/Qwen3-8B-DFlash-b16",
+        "z-lab/Qwen3.5-27B-DFlash",
+        "z-lab/Qwen3.5-9B-DFlash",
+        "z-lab/Qwen3.5-4B-DFlash",
+        "z-lab/Qwen3.5-35B-A3B-DFlash",
+        "z-lab/gpt-oss-120b-DFlash",
+        "z-lab/gpt-oss-20b-DFlash",
+        "z-lab/Qwen3-Coder-30B-A3B-DFlash",
+        "z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat"
       ],
-      "relevancy_score": 53.6
+      "relevancy_score": 52.6
     },
     {
       "architecture_id": "Cohere2ForCausalLM",
       "total_models": 9,
-      "total_downloads": 207703,
+      "total_downloads": 220158,
       "min_param_count": 2090024,
       "sample_models": [
         "trl-internal-testing/tiny-Cohere2ForCausalLM",
@@ -445,12 +356,12 @@
         "CohereLabs/tiny-aya-fire",
         "CohereLabs/tiny-aya-earth"
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         "microsoft/Phi-tiny-MoE-instruct",
@@ -458,37 +369,37 @@
         "microsoft/Phi-3.5-MoE-instruct",
         "optimum-intel-internal-testing/phi-3.5-moe-tiny-random"
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-        "vinai/PhoGPT-4B-Chat",
-        "vinai/PhoGPT-4B",
-        "anas-awadalla/mpt-7b",
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-        "replit/replit-code-v1-3b",
-        "echarlaix/tiny-mpt-random-remote-code",
-        "wtang06/mpt-125m-c4",
-        "lightblue/japanese-mpt-7b",
-        "gl198976/mpt-7b",
-        "TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge"
+        "openbmb/MiniCPM-2B-sft-bf16",
+        "openbmb/MiniCPM4.1-8B",
+        "openbmb/MiniCPM-1B-sft-bf16",
+        "openbmb/MiniCPM4-0.5B",
+        "openbmb/MiniCPM-MoE-8x2B",
+        "openbmb/MiniCPM-S-1B-sft",
+        "openbmb/MiniCPM4-8B",
+        "katuni4ka/tiny-random-minicpm",
+        "openbmb/MiniCPM-2B-dpo-bf16",
+        "openbmb/MiniCPM-2B-sft-fp32"
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       "architecture_id": "RwkvForCausalLM",
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       "sample_models": [
         "RWKV/v5-Eagle-7B-HF",
         "RWKV/rwkv-4-169m-pile",
-        "beomi/KoRWKV-6B",
         "RWKV/rwkv-4-430m-pile",
+        "beomi/KoRWKV-6B",
         "RWKV/rwkv-4-1b5-pile",
         "RWKV/rwkv-4-3b-pile",
         "RWKV/rwkv-raven-1b5",
@@ -496,12 +407,12 @@
         "RWKV/rwkv-raven-3b",
         "RWKV/rwkv-raven-14b"
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@@ -515,47 +426,43 @@
         "intelia-lab-uah/mt0-base_QG_SQAC",
         "UBC-NLP/toucan-1.2B"
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     {
       "architecture_id": "Qwen2MoeForCausalLM",
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         "Qwen/Qwen2-57B-A14B-Instruct",
         "Qwen/Qwen2-57B-A14B",
-        "katuni4ka/tiny-random-qwen1.5-moe",
         "yujiepan/qwen1.5-moe-tiny-random",
+        "katuni4ka/tiny-random-qwen1.5-moe",
         "xd2010/Qwen1.5-MOE-sft-math7k-densemixer"
       ],
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+    {
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+      "sample_models": [
+        "trl-internal-testing/tiny-FalconMambaForCausalLM",
+        "tiiuae/falcon-mamba-7b-instruct",
+        "tiiuae/falcon-mamba-7b",
+        "tiiuae/falcon-mamba-tiny-dev",
+        "tiiuae/Falcon3-Mamba-7B-Instruct",
+        "tiiuae/Falcon3-Mamba-7B-Base"
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     {
       "architecture_id": "Phi3VForCausalLM",
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       "sample_models": [
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@@ -565,12 +472,12 @@
         "Desm0nt/Phi-3-HornyVision-128k-instruct",
         "failspy/Phi-3-vision-128k-instruct-abliterated-alpha"
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     {
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@@ -581,26 +488,31 @@
         "LGAI-EXAONE/EXAONE-Deep-32B",
         "LGAI-EXAONE/EXAONE-Deep-2.4B"
       ],
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     {
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-        "tiiuae/falcon-mamba-7b-instruct",
-        "tiiuae/falcon-mamba-7b",
-        "tiiuae/falcon-mamba-tiny-dev",
-        "tiiuae/Falcon3-Mamba-7B-Instruct"
+        "zai-org/GLM-4-9B-0414",
+        "zai-org/GLM-4-32B-0414",
+        "zai-org/GLM-Z1-9B-0414",
+        "MCult01/glm-muse-v2",
+        "zai-org/GLM-Z1-32B-0414",
+        "MCult01/glm-muse-v1",
+        "zai-org/GLM-4-32B-Base-0414",
+        "yujiepan/glm-4-tiny-random",
+        "llmfan46/GLM-4-32B-0414-uncensored-heretic-v1",
+        "ccui46/cookingworld_per_chunk_act_glm_tokfix_diffPrompt_5000"
       ],
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     {
       "architecture_id": "LlavaQwenForCausalLM",
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       "sample_models": [
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@@ -608,24 +520,62 @@
         "lmms-lab/llava-onevision-qwen2-7b-si",
         "lmms-lab/llava-onevision-qwen2-0.5b-si"
       ],
-      "relevancy_score": 49.5
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+        "cerebras/MiniMax-M2.1-REAP-139B-A10B",
+        "MiniMaxAI/MiniMax-M2",
+        "MiniMaxAI/MiniMax-M2.7",
+        "MiniMaxAI/MiniMax-M2.1",
+        "cerebras/MiniMax-M2.5-REAP-139B-A10B",
+        "JANGQ-AI/MiniMax-M2.7-JANG_2L",
+        "aspctu/MiniMax-M2.5",
+        "JANGQ-AI/MiniMax-M2.7-JANG_3L",
+        "dealignai/MiniMax-M2.5-UNCENSORED-JANG_2L"
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+        "nvidia/gpt-oss-120b-Eagle3-long-context",
+        "chankhavu/c2.eagle3-test",
+        "Zjcxy-SmartAI/Eagle3-Qwen3-32B-zh",
+        "nvidia/gpt-oss-120b-Eagle3-short-context",
+        "Zjcxy-SmartAI/Eagle3-Qwen3-8B-zh",
+        "nvidia/gpt-oss-120b-Eagle3-throughput",
+        "thoughtworks/Gemma-4-31B-Eagle3",
+        "ruipeterpan/Qwen2.5-7B-Instruct_EAGLE3_UltraChat",
+        "thoughtworks/MiniMax-M2.5-Eagle3"
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     {
       "architecture_id": "BambaForCausalLM",
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       "min_param_count": 33110760,
       "sample_models": [
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         "ibm-ai-platform/Bamba-9B-v2"
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     {
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@@ -634,41 +584,41 @@
         "RedHatAI/Qwen3-32B-speculator.eagle3",
         "RedHatAI/Qwen3-14B-speculator.eagle3"
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     {
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         "tencent/Hunyuan-0.5B-Pretrain",
         "tencent/Hunyuan-1.8B-Pretrain",
-        "tencent/Hunyuan-4B-Pretrain",
         "tencent/Hunyuan-7B-Instruct-0124",
+        "tencent/Hunyuan-4B-Pretrain",
         "tencent/Hunyuan-7B-Pretrain",
         "tencent/Hunyuan-1.8B-Instruct",
         "tencent/Hunyuan-0.5B-Instruct",
         "tencent/Hunyuan-4B-Instruct"
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     {
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       "sample_models": [
         "bigscience/bigscience-small-testing",
@@ -680,30 +630,12 @@
         "Muennighoff/bloom-tiny-random",
         "TurkuNLP/gpt3-finnish-xl"
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-        "nvidia/gpt-oss-120b-Eagle3-long-context",
-        "nvidia/gpt-oss-120b-Eagle3-short-context",
-        "Zjcxy-SmartAI/Eagle3-Qwen3-32B-zh",
-        "chankhavu/c2.eagle3-test",
-        "Zjcxy-SmartAI/Eagle3-Qwen3-8B-zh",
-        "nvidia/gpt-oss-120b-Eagle3-throughput",
-        "thoughtworks/Gemma-4-31B-Eagle3",
-        "ruipeterpan/Qwen2.5-7B-Instruct_EAGLE3_UltraChat"
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     {
       "architecture_id": "NemotronForCausalLM",
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       "sample_models": [
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@@ -712,89 +644,57 @@
         "badaoui/tiny-random-NemotronForCausalLM",
         "thhaus/nemotron3-8b"
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-        "zai-org/GLM-Z1-9B-0414",
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     {
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         "LongSafari/hyenadna-medium-450k-seqlen-hf",
-        "LongSafari/hyenadna-large-1m-seqlen-hf",
         "LongSafari/hyenadna-tiny-1k-seqlen-hf",
+        "LongSafari/hyenadna-large-1m-seqlen-hf",
         "LongSafari/hyenadna-medium-160k-seqlen-hf",
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     {
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         "Rostlab/prot_t5_xl_bfd",
-        "unicamp-dl/ptt5-small-portuguese-vocab",
         "gagan3012/k2t",
+        "unicamp-dl/ptt5-small-portuguese-vocab",
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+        "hugohrban/progen2-small",
+        "hugohrban/progen2-medium",
+        "hugohrban/progen2-large",
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     {
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         "Zyphra/Zamba2-1.2B-instruct",
@@ -803,14 +703,63 @@
         "EchoLabs33/zamba2-1.2b-hxq",
         "Zyphra/Zamba2-2.7B-instruct",
         "EchoLabs33/zamba2-2.7b-instruct-hxq",
-        "EchoLabs33/zamba2-7b-instruct-hxq"
+        "EchoLabs33/zamba2-7b-instruct-hxq",
+        "Zyphra/Zamba2-2.7B-Instruct-v2"
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+    {
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+        "caiovicentino1/Qwopus-MoE-35B-A3B-HLWQ-Q5",
+        "nightmedia/Qwen3.5-35B-A3B-Text-qx64-hi-mlx",
+        "Brooooooklyn/Qwen3.5-35B-A3B-UD-Q4_K_XL-mlx",
+        "nivvis/Qwen3.5-35B-A3B-EQ-v5",
+        "JANGQ-AI/Qwen3.5-397B-A17B-JANG_1L",
+        "Jackrong/MLX-Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-bf16",
+        "nightmedia/Qwen3.5-122B-A10B-Text-qx85-mlx",
+        "JANGQ-AI/Qwen3.5-397B-A17B-JANG_2L",
+        "Kevletesteur/Qwen3.5-35B-A3B-Chimere-Distilled-BF16"
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+        "baidu/ERNIE-4.5-21B-A3B-Thinking",
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+        "RedHatAI/Qwen3-30B-A3B-Thinking-2507-speculator.eagle3",
+        "RedHatAI/Qwen3-235B-A22B-Instruct-2507-speculator.eagle3",
+        "RedHatAI/Qwen3-30B-A3B-Instruct-2507-speculator.eagle3",
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@@ -821,28 +770,12 @@
         "BAAI/AquilaChat2-34B-16K",
         "BAAI/Aquila2-70B-Expr"
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@@ -850,31 +783,28 @@
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+        "xverse/XVERSE-7B-Chat",
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@@ -883,12 +813,26 @@
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@@ -898,43 +842,25 @@
         "JetLM/SDAR-4B-Chat",
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@@ -943,31 +869,43 @@
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@@ -976,24 +914,50 @@
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@@ -1003,234 +967,221 @@
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@@ -1239,48 +1190,36 @@
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@@ -1290,79 +1229,85 @@
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@@ -1371,818 +1316,905 @@
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@@ -2191,86 +2223,89 @@
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@@ -2278,140 +2313,140 @@
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@@ -2420,194 +2455,211 @@
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@@ -2615,123 +2667,124 @@
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@@ -2739,54 +2792,53 @@
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@@ -2794,97 +2846,75 @@
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@@ -2892,226 +2922,286 @@
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@@ -3120,1317 +3210,1350 @@
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       "total_models": 1,
-      "total_downloads": 527,
+      "total_downloads": 523,
       "min_param_count": null,
       "sample_models": [
-        "NousResearch/Nous-Hermes-2-Vision-Alpha"
+        "jiwonsong/SeerAttention-Qwen3-8B-AttnGates"
       ],
-      "relevancy_score": 14.7
+      "relevancy_score": 14.3
     },
     {
       "architecture_id": "MedHemoModel",
@@ -4670,27 +4803,27 @@
       "sample_models": [
         "amewebstudio/medhemo-earcp"
       ],
-      "relevancy_score": 14.6
+      "relevancy_score": 14.3
     },
     {
-      "architecture_id": "HgrnForCausalLM",
+      "architecture_id": "VSMForCausalLM",
       "total_models": 1,
-      "total_downloads": 513,
+      "total_downloads": 521,
       "min_param_count": null,
       "sample_models": [
-        "OpenNLPLab/HGRN-150M"
+        "craigwu/seal_vsm_7b"
       ],
-      "relevancy_score": 14.6
+      "relevancy_score": 14.3
     },
     {
-      "architecture_id": "M2M100ForConditionalGeneration",
+      "architecture_id": "LlavaMistralForCausalLM",
       "total_models": 1,
-      "total_downloads": 501,
+      "total_downloads": 510,
       "min_param_count": null,
       "sample_models": [
-        "dsfsi/nso-en-m2m100-gov"
+        "NousResearch/Nous-Hermes-2-Vision-Alpha"
       ],
-      "relevancy_score": 14.6
+      "relevancy_score": 14.3
     }
   ]
 }
\ No newline at end of file
diff --git a/transformer_lens/tools/model_registry/data/supported_models.json b/transformer_lens/tools/model_registry/data/supported_models.json
index c037dedfa..fdce49a70 100644
--- a/transformer_lens/tools/model_registry/data/supported_models.json
+++ b/transformer_lens/tools/model_registry/data/supported_models.json
@@ -1,22 +1,22 @@
 {
-  "generated_at": "2026-04-10",
+  "generated_at": "2026-04-14",
   "scan_info": {
-    "total_scanned": 5436,
+    "total_scanned": 5633,
     "task_filter": "text-generation",
     "min_downloads": 500,
-    "scan_duration_seconds": 3.9
+    "scan_duration_seconds": 4.2
   },
-  "total_architectures": 43,
-  "total_models": 7006,
-  "total_verified": 704,
+  "total_architectures": 47,
+  "total_models": 7426,
+  "total_verified": 706,
   "models": [
     {
       "architecture_id": "Qwen3NextForCausalLM",
       "model_id": "Qwen/Qwen3-Next-80B-A3B-Instruct",
       "status": 2,
-      "verified_date": "2026-04-10",
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": "Estimated 6929.6 GB exceeds 96.0 GB limit",
+      "note": "Estimated 708.8 GB exceeds 96.0 GB limit",
       "phase1_score": null,
       "phase2_score": null,
       "phase3_score": null,
@@ -28,9 +28,9 @@
       "architecture_id": "Qwen3NextForCausalLM",
       "model_id": "unsloth/Qwen3-Coder-Next",
       "status": 2,
-      "verified_date": "2026-04-10",
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": "Estimated 6929.6 GB exceeds 96.0 GB limit",
+      "note": "Estimated 708.8 GB exceeds 96.0 GB limit",
       "phase1_score": null,
       "phase2_score": null,
       "phase3_score": null,
@@ -42,9 +42,9 @@
       "architecture_id": "Qwen3NextForCausalLM",
       "model_id": "Qwen/Qwen3-Next-80B-A3B-Thinking",
       "status": 2,
-      "verified_date": "2026-04-10",
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": "Estimated 6929.6 GB exceeds 96.0 GB limit",
+      "note": "Estimated 708.8 GB exceeds 96.0 GB limit",
       "phase1_score": null,
       "phase2_score": null,
       "phase3_score": null,
@@ -56,7 +56,7 @@
       "architecture_id": "Qwen3NextForCausalLM",
       "model_id": "tiny-random/qwen3-next-moe",
       "status": 1,
-      "verified_date": "2026-04-10",
+      "verified_date": "2026-04-15",
       "metadata": null,
       "note": "Full verification completed",
       "phase1_score": 100.0,
@@ -70,7 +70,7 @@
       "architecture_id": "Qwen3NextForCausalLM",
       "model_id": "optimum-intel-internal-testing/tiny-random-qwen3-next",
       "status": 1,
-      "verified_date": "2026-04-10",
+      "verified_date": "2026-04-15",
       "metadata": null,
       "note": "Full verification completed",
       "phase1_score": 100.0,
@@ -84,7 +84,7 @@
       "architecture_id": "Qwen3NextForCausalLM",
       "model_id": "yujiepan/qwen3-next-moe-tiny-random",
       "status": 1,
-      "verified_date": "2026-04-10",
+      "verified_date": "2026-04-15",
       "metadata": null,
       "note": "Full verification completed",
       "phase1_score": 100.0,
@@ -98,9 +98,9 @@
       "architecture_id": "Qwen3NextForCausalLM",
       "model_id": "huihui-ai/Huihui-Qwen3-Coder-Next-abliterated",
       "status": 2,
-      "verified_date": "2026-04-10",
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": "Estimated 6929.6 GB exceeds 96.0 GB limit",
+      "note": "Estimated 708.8 GB exceeds 96.0 GB limit",
       "phase1_score": null,
       "phase2_score": null,
       "phase3_score": null,
@@ -112,9 +112,9 @@
       "architecture_id": "Qwen3NextForCausalLM",
       "model_id": "Qwen/Qwen3-Coder-Next-Base",
       "status": 2,
-      "verified_date": "2026-04-10",
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": "Estimated 6929.6 GB exceeds 96.0 GB limit",
+      "note": "Estimated 708.8 GB exceeds 96.0 GB limit",
       "phase1_score": null,
       "phase2_score": null,
       "phase3_score": null,
@@ -126,9 +126,9 @@
       "architecture_id": "Qwen3NextForCausalLM",
       "model_id": "bknyaz/Qwen3-Coder-Next-REAM",
       "status": 2,
-      "verified_date": "2026-04-10",
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": "Estimated 5201.5 GB exceeds 96.0 GB limit",
+      "note": "Estimated 535.9 GB exceeds 96.0 GB limit",
       "phase1_score": null,
       "phase2_score": null,
       "phase3_score": null,
@@ -140,7 +140,7 @@
       "architecture_id": "Qwen3NextForCausalLM",
       "model_id": "Qwen/Qwen3-Coder-Next",
       "status": 2,
-      "verified_date": "2026-04-10",
+      "verified_date": "2026-04-15",
       "metadata": {
         "downloads": 664116,
         "likes": 0,
@@ -153,7 +153,7 @@
         ],
         "parameter_count": 79674391296
       },
-      "note": "Estimated 6929.6 GB exceeds 96.0 GB limit",
+      "note": "Estimated 708.8 GB exceeds 96.0 GB limit",
       "phase1_score": null,
       "phase2_score": null,
       "phase3_score": null,
@@ -1999,9 +1999,9 @@
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "ibm-granite/granite-4.0-h-small",
       "status": 2,
-      "verified_date": "2026-03-17",
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": "Estimated 135.9 GB exceeds 75.2 GB limit",
+      "note": "Estimated 270.8 GB exceeds 96.0 GB limit",
       "phase1_score": null,
       "phase2_score": null,
       "phase3_score": null,
@@ -2362,14 +2362,14 @@
     {
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "ibm-granite/granite-4.0-micro-base",
-      "status": 0,
-      "verified_date": null,
+      "status": 1,
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": null,
-      "phase1_score": null,
-      "phase2_score": null,
-      "phase3_score": null,
-      "phase4_score": null,
+      "note": "Full verification completed",
+      "phase1_score": 100.0,
+      "phase2_score": 100.0,
+      "phase3_score": 100.0,
+      "phase4_score": 88.7,
       "phase7_score": null,
       "phase8_score": null
     },
@@ -2573,13 +2573,13 @@
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "ibm-granite/granite-4.0-micro",
       "status": 1,
-      "verified_date": "2026-03-17",
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": "Core verification completed",
+      "note": "Full verification completed",
       "phase1_score": 100.0,
-      "phase2_score": null,
-      "phase3_score": null,
-      "phase4_score": 72.2,
+      "phase2_score": 100.0,
+      "phase3_score": 100.0,
+      "phase4_score": 98.9,
       "phase7_score": null,
       "phase8_score": null
     },
@@ -3342,14 +3342,14 @@
     {
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "ibm-granite/granite-4.0-h-tiny",
-      "status": 1,
-      "verified_date": "2026-03-17",
+      "status": 3,
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": "Core verification completed",
-      "phase1_score": 100.0,
-      "phase2_score": null,
-      "phase3_score": null,
-      "phase4_score": 77.5,
+      "note": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 72/347 components failed (72 critical)",
+      "phase1_score": 50.0,
+      "phase2_score": 100.0,
+      "phase3_score": 100.0,
+      "phase4_score": 96.6,
       "phase7_score": null,
       "phase8_score": null
     },
@@ -4140,13 +4140,13 @@
     {
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "ibm-granite/granite-4.0-h-micro",
-      "status": 1,
-      "verified_date": "2026-03-17",
+      "status": 3,
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": "Core verification completed",
-      "phase1_score": 100.0,
-      "phase2_score": null,
-      "phase3_score": null,
+      "note": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 72/307 components failed (72 critical)",
+      "phase1_score": 50.0,
+      "phase2_score": 100.0,
+      "phase3_score": 100.0,
       "phase4_score": 98.2,
       "phase7_score": null,
       "phase8_score": null
@@ -4182,14 +4182,14 @@
     {
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "ibm-granite/granite-4.0-tiny-preview",
-      "status": 1,
-      "verified_date": "2026-03-17",
+      "status": 3,
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": "Core verification completed",
-      "phase1_score": 100.0,
-      "phase2_score": null,
-      "phase3_score": null,
-      "phase4_score": 97.4,
+      "note": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 72/347 components failed (72 critical)",
+      "phase1_score": 50.0,
+      "phase2_score": 100.0,
+      "phase3_score": 100.0,
+      "phase4_score": 98.7,
       "phase7_score": null,
       "phase8_score": null
     },
@@ -4280,14 +4280,14 @@
     {
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "ibm-granite/granite-4.0-350m",
-      "status": 0,
-      "verified_date": null,
+      "status": 1,
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": null,
-      "phase1_score": null,
-      "phase2_score": null,
-      "phase3_score": null,
-      "phase4_score": null,
+      "note": "Full verification completed with issues: P2=91.7% (failed: generation)",
+      "phase1_score": 100.0,
+      "phase2_score": 91.7,
+      "phase3_score": 100.0,
+      "phase4_score": 94.7,
       "phase7_score": null,
       "phase8_score": null
     },
@@ -5344,14 +5344,14 @@
     {
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "ibm-granite/granite-4.0-h-1b",
-      "status": 0,
-      "verified_date": null,
+      "status": 3,
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": null,
-      "phase1_score": null,
-      "phase2_score": null,
-      "phase3_score": null,
-      "phase4_score": null,
+      "note": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 72/307 components failed (72 critical)",
+      "phase1_score": 50.0,
+      "phase2_score": 100.0,
+      "phase3_score": 100.0,
+      "phase4_score": 72.2,
       "phase7_score": null,
       "phase8_score": null
     },
@@ -5540,14 +5540,14 @@
     {
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "ibm-granite/granite-4.0-h-350m",
-      "status": 0,
-      "verified_date": null,
+      "status": 3,
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": null,
-      "phase1_score": null,
-      "phase2_score": null,
-      "phase3_score": null,
-      "phase4_score": null,
+      "note": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 56/243 components failed (56 critical)",
+      "phase1_score": 50.0,
+      "phase2_score": 100.0,
+      "phase3_score": 100.0,
+      "phase4_score": 94.8,
       "phase7_score": null,
       "phase8_score": null
     },
@@ -5946,14 +5946,14 @@
     {
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "ibm-granite/granite-4.0-1b",
-      "status": 0,
-      "verified_date": null,
+      "status": 1,
+      "verified_date": "2026-04-15",
       "metadata": null,
-      "note": null,
-      "phase1_score": null,
-      "phase2_score": null,
-      "phase3_score": null,
-      "phase4_score": null,
+      "note": "Full verification completed",
+      "phase1_score": 100.0,
+      "phase2_score": 100.0,
+      "phase3_score": 100.0,
+      "phase4_score": 100.0,
       "phase7_score": null,
       "phase8_score": null
     },
@@ -8186,11 +8186,11 @@
     {
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "onnx-community/granite-4.0-350m-ONNX-web",
-      "status": 0,
-      "verified_date": null,
+      "status": 3,
+      "verified_date": "2026-04-14",
       "metadata": null,
-      "note": null,
-      "phase1_score": null,
+      "note": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: onnx-community/granite-4.0-350m-ONNX-web does not appear to have a file named pytorch_model.bin or model",
+      "phase1_score": 0.0,
       "phase2_score": null,
       "phase3_score": null,
       "phase4_score": null,
@@ -8746,11 +8746,11 @@
     {
       "architecture_id": "GraniteMoeHybridForCausalLM",
       "model_id": "ibm-granite/granite-4.0-350m-base",
-      "status": 0,
-      "verified_date": null,
+      "status": 3,
+      "verified_date": "2026-04-14",
       "metadata": null,
-      "note": null,
-      "phase1_score": null,
+      "note": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: 'NoneType' object has no attribute 'in_proj'",
+      "phase1_score": 0.0,
       "phase2_score": null,
       "phase3_score": null,
       "phase4_score": null,
@@ -99556,15 +99556,15 @@
       "architecture_id": "Qwen3_5ForCausalLM",
       "model_id": "Qwen/Qwen3.5-0.8B",
       "status": 1,
-      "verified_date": "2026-04-14",
+      "verified_date": "2026-04-15",
       "metadata": {
         "downloads": 2577198,
         "total_params": 950000000
       },
-      "note": "Full verification completed with issues: P3=94.1% (failed: attention_output_centering)",
+      "note": "Full verification completed",
       "phase1_score": 100.0,
       "phase2_score": 100.0,
-      "phase3_score": 94.1,
+      "phase3_score": 100.0,
       "phase4_score": 91.5,
       "phase7_score": null,
       "phase8_score": null
@@ -99602,6 +99602,4934 @@
       "phase4_score": null,
       "phase7_score": null,
       "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3_5ForCausalLM",
+      "model_id": "llmfan46/Darkidol-Ballad-27B-ultra-uncensored-heretic-v1",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "contextboxai/Qwen3-1.7B-FC",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "tomg-group-umd/DynaGuard-1.7B",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3_5ForCausalLM",
+      "model_id": "principled-intelligence/Qwen3.5-9B-text-only",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3_5ForCausalLM",
+      "model_id": "kai-os/Carnice-9b",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "activeDap/Qwen3-1.7B_hh_harmful",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3_5ForCausalLM",
+      "model_id": "GoodStartLabs/gin-rummy-hbc-qwen3.5-0.8b",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "bigatuna/Qwen3-1.7B-Sushi-Coder",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "prefeitura-rio/Rio-3.0-Open-Mini",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Gemma3ForCausalLM",
+      "model_id": "pthinc/Cicikus_v4_0.3B_Pitircik",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen2ForCausalLM",
+      "model_id": "joekarim/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-foxy_peckish_pigeon",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
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+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "MistralForCausalLM",
+      "model_id": "misterJB/atlas-field-528hz",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "ChuGyouk/F_R5_T2",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "LlamaForCausalLM",
+      "model_id": "idopinto/llama3-8b-full-gen-inv-sft-v2-g2-e3",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "LlamaForCausalLM",
+      "model_id": "stsirtsis/llama-3.1-8b-DA-SynthDolly-1A",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
     }
   ]
 }
diff --git a/transformer_lens/tools/model_registry/data/verification_history.json b/transformer_lens/tools/model_registry/data/verification_history.json
index d9910bfc2..dc48d675e 100644
--- a/transformer_lens/tools/model_registry/data/verification_history.json
+++ b/transformer_lens/tools/model_registry/data/verification_history.json
@@ -1,5 +1,5 @@
 {
-  "last_updated": "2026-04-14T13:03:57.367589",
+  "last_updated": "2026-04-15T09:15:26.792099",
   "records": [
     {
       "model_id": "Macropodus/macbert4mdcspell_v1",
@@ -11290,6 +11290,226 @@
       "notes": "Full verification completed with issues: P3=94.1% (failed: attention_output_centering)",
       "invalidated": false,
       "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-micro-base",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-14",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: PositionEmbeddingsAttentionBridge.__init__() got an unexpected keyword argument 'requires_attention_mask",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-350m",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-14",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: PositionEmbeddingsAttentionBridge.__init__() got an unexpected keyword argument 'requires_attention_mask",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-h-1b",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-14",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: 'NoneType' object has no attribute 'q_proj'",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-h-350m",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-14",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: 'NoneType' object has no attribute 'q_proj'",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-1b",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-14",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: 'NoneType' object has no attribute 'in_proj'",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "onnx-community/granite-4.0-350m-ONNX-web",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-14",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: onnx-community/granite-4.0-350m-ONNX-web does not appear to have a file named pytorch_model.bin or model",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-350m-base",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-14",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: 'NoneType' object has no attribute 'in_proj'",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-micro-base",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P3=88.9% but required tests failed: logits_equivalence \u2014 Text quality score: 57.8/100 (avg perplexity: 17.8) \u2014 generated text may be incoherent",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-micro-base",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-micro",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-h-tiny",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 72/347 components failed (72 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-h-micro",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 72/307 components failed (72 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-tiny-preview",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 72/347 components failed (72 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-350m",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed with issues: P2=91.7% (failed: generation)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-h-1b",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 72/307 components failed (72 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-h-350m",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 56/243 components failed (56 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "ibm-granite/granite-4.0-1b",
+      "architecture_id": "GraniteMoeHybridForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "tiny-random/qwen3-next-moe",
+      "architecture_id": "Qwen3NextForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "optimum-intel-internal-testing/tiny-random-qwen3-next",
+      "architecture_id": "Qwen3NextForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/qwen3-next-moe-tiny-random",
+      "architecture_id": "Qwen3NextForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "Qwen/Qwen3.5-0.8B",
+      "architecture_id": "Qwen3_5ForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 18/142 components failed (18 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "Qwen/Qwen3.5-0.8B",
+      "architecture_id": "Qwen3_5ForCausalLM",
+      "verified_date": "2026-04-15",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
     }
   ]
 }