diff --git a/muh/gen_patch.py b/muh/gen_patch.py
index 082f9957..98c91343 100644
--- a/muh/gen_patch.py
+++ b/muh/gen_patch.py
@@ -268,29 +268,55 @@ def generate_patches(header_dir):
                 summary.append(f"SKIP {algo}: no bi100_* structs and no inline values found")
                 continue
 
-        # Select the most relevant struct for vllm's primary data path.
-        # vllm's paged_attention score accumulator is always float32 (4 bytes),
-        # so we prefer bi100_*float32* or bi100_*accum4* structs.
-        # Fallback priority: float32 > accum2 (fp16 KV) > first non-default > first.
-        primary = None
-        preference_order = ['float32', 'accum4', 'accum2', 'int32']
-        for pref in preference_order:
-            for name, fields in structs:
-                if pref in name and 'det' not in name and 'default' not in name:
-                    primary = (name, fields)
-                    break
-            if primary:
-                break
-        if primary is None:
-            for name, fields in structs:
-                if 'default' not in name and 'det' not in name:
-                    primary = (name, fields)
-                    break
-        if primary is None:
-            primary = structs[0]
+        # Select the struct that matches each vllm kernel's data type.
+        #
+        # CCCL's policy_selector dispatches by (accum_size, type_t, offset_size).
+        # gen_patch must do the same: when injecting into paged_attention
+        # (float32 scores), use bi100_plus_float32_o4, not bi100_plus_accum1_o4.
+        #
+        # The VLLM_KERNEL_MAP in muh_kernel_map.py defines each kernel's
+        # data_types. This mapping encodes the primary data type per algorithm:
+        ALGO_PRIMARY_TYPE = {
+            'reduce':       ('float32', 4),   # paged_attention scores
+            'scan':         ('float32', 4),   # softmax denominator
+            'topk':         ('float32', 4),   # logits
+            'transform':    ('float16', 2),   # activations (SiLU, RMSNorm input)
+            'batch_memcpy': ('float16', 2),   # KV cache blocks
+            'for':          ('float16', 2),   # RoPE
+        }
 
-        pname, pfields = primary
-        summary.append(f"READ {algo}: {pname} → {pfields}")
+        target_type, target_size = ALGO_PRIMARY_TYPE.get(algo, ('float32', 4))
+
+        # Score each struct by match quality
+        def struct_score(name, fields):
+            score = 0
+            name_lower = name.lower()
+            # Exact type name match (best)
+            if target_type.replace('float', 'f') in name_lower or target_type in name_lower:
+                score += 100
+            # Accum/type size match in name (e.g. "_4B_", "_accum4_", "float32")
+            size_tags = [f'_{target_size}B', f'_accum{target_size}', f'float{target_size*8}']
+            for tag in size_tags:
+                if tag.lower() in name_lower:
+                    score += 50
+            # Offset size 4 preferred (most common in vllm)
+            if '_o4' in name_lower:
+                score += 10
+            # Penalize 'default' and 'det' (deterministic) structs
+            if 'default' in name_lower:
+                score -= 200
+            if 'det' in name_lower:
+                score -= 50
+            # Penalize 1-byte type structs for float32 targets
+            if target_size >= 4 and ('_1B' in name or 'accum1' in name_lower):
+                score -= 100
+            return score
+
+        scored = [(struct_score(n, f), n, f) for n, f in structs]
+        scored.sort(key=lambda x: -x[0])
+        _, pname, pfields = scored[0]
+
+        summary.append(f"READ {algo}: {pname} → {pfields} (target: {target_type})")
 
         for field_name, value in pfields.items():
             key = (algo, field_name)