diff --git a/transformer_lens/benchmarks/component_outputs.py b/transformer_lens/benchmarks/component_outputs.py
index 1efe59f82..adbb76924 100644
--- a/transformer_lens/benchmarks/component_outputs.py
+++ b/transformer_lens/benchmarks/component_outputs.py
@@ -199,6 +199,17 @@ def print_detailed_analysis(self) -> None:
 class ComponentBenchmarker:
     """Benchmarking utility for testing TransformerBridge components against HuggingFace."""
 
+    def _is_delegated_block(self) -> bool:
+        """Return True if the blocks component uses DelegatedAttentionBlockBridge."""
+        blocks = (
+            getattr(self.adapter, "component_mapping", {}).get("blocks")
+            if self.adapter is not None
+            else None
+        )
+        return blocks is not None and (
+            "hook_q_input" not in getattr(blocks, "hook_aliases", {"hook_q_input": True})
+        )
+
     def __init__(
         self,
         bridge_model: nn.Module,
@@ -419,6 +430,23 @@ def _test_component_recursive(
         ):
             return
 
+        # Skip attention and PLE submodules when using DelegatedAttentionBlockBridge.
+        # These architectures delegate all math to HF; the benchmark can't call the HF
+        # attention in isolation (missing position_embeddings, attention_mask, etc.) and
+        # PLE submodules receive per-layer inputs at a different dimension than hidden_states.
+        _is_delegated = self._is_delegated_block()
+        if _is_delegated and "attn" in component_path:
+            return
+        if _is_delegated and any(
+            name in component_path
+            for name in (
+                "per_layer_input_gate",
+                "per_layer_projection",
+                "post_per_layer_input_norm",
+            )
+        ):
+            return
+
         # Skip models whose MLP/attn forward signatures require extra context from the block:
         # - BLOOM: MLP requires residual and alibi bias
         # - T5: requires cache_position for relative position embeddings
@@ -526,6 +554,12 @@ def _test_component(
             ComponentTestResult or None if the component cannot be tested
         """
         try:
+            # Skip rotary_emb for DelegatedAttentionBlockBridge architectures.
+            # Gemma4's RotaryEmbeddingBridge wraps a rotary that returns a set-like
+            # structure which the benchmark comparison can't subscript.
+            if self._is_delegated_block() and component_path == "rotary_emb":
+                return None
+
             # Get bridge component
             # The adapter returns nn.Module, but for bridge models it's actually GeneralizedComponent
             bridge_component = cast(
diff --git a/transformer_lens/benchmarks/main_benchmark.py b/transformer_lens/benchmarks/main_benchmark.py
index 05735095f..d2ccbfa70 100644
--- a/transformer_lens/benchmarks/main_benchmark.py
+++ b/transformer_lens/benchmarks/main_benchmark.py
@@ -935,7 +935,7 @@ def cleanup_model(model, model_name_str: str):
             # Use appropriate AutoModel class (e.g., AutoModelForSeq2SeqLM for T5)
             auto_model_class = get_auto_model_class(model_name, trust_remote_code=trust_remote_code)
             if verbose and auto_model_class != AutoModelForCausalLM:
-                print(f"Using {auto_model_class.__name__} for encoder-decoder model")
+                print(f"Using {auto_model_class.__name__}")
             # Ensure pad_token_id exists (some models crash without it during init).
             hf_config = AutoConfig.from_pretrained(
                 model_name, trust_remote_code=trust_remote_code, token=_hf_token()
@@ -1209,14 +1209,14 @@ def cleanup_model(model, model_name_str: str):
     # PHASE 2: Bridge (unprocessed) + HookedTransformer (unprocessed)
     # ========================================================================
     current_phase[0] = 2
-    if verbose:
-        print(f"\n{'='*80}")
-        print("PHASE 2: TransformerBridge (unprocessed) + HookedTransformer (unprocessed)")
-        print(f"{'='*80}\n")
 
     # OPTIMIZATION: Run generation benchmarks first (only bridge in memory)
     # Then cleanup bridge before loading HT to reduce peak memory
     if should_run_phase(2) and bridge_unprocessed:
+        if verbose:
+            print(f"\n{'='*80}")
+            print("PHASE 2: TransformerBridge (unprocessed) + HookedTransformer (unprocessed)")
+            print(f"{'='*80}\n")
         if verbose:
             print("Running Phase 2 benchmarks...\n")