diff --git a/fp8_to_nvfp4_streaming.py b/fp8_to_nvfp4_streaming.py
index e364184..16a56c6 100644
--- a/fp8_to_nvfp4_streaming.py
+++ b/fp8_to_nvfp4_streaming.py
@@ -57,18 +57,24 @@ from tqdm import tqdm
 PRESERVE_REGEXES = [
     r".*lm_head.*",
     r".*embed_tokens.*",
-    r".*\.mlp\.gate(\.weight)?$",      # MoE router (NOT gate_proj)
+    r".*\.(mlp|ffn)\.gate(\.weight)?$",      # MoE router (NOT gate_proj)
     r".*norm.*",
     r".*indexer.*",                    # V3.2 DSA / V4 CSA indexer
     r".*hyper_conn.*",                 # V4 mHC
     r".*\.mhc.*",
+    r".*hc_attn.*",                    # V4 hyper-connection attn
+    r".*hc_ffn.*",                     # V4 hyper-connection ffn
+    r".*hc_head.*",                    # V4 hyper-connection head
     r".*scoring.*",
+    r".*attn_sink.*",                  # V4 attention sink
+    r".*compressor\.ape.*",            # V4 compressor absolute pos encoding
+    r".*tid2eid.*",                    # V4 MoE token-to-expert mapping
     r".*\.bias$",                      # any biases
 ]
 PRESERVE_RE = re.compile("|".join(f"(?:{p})" for p in PRESERVE_REGEXES))
 
 # Identify expert pairs that need joint global scale
-EXPERT_PAIR_RE = re.compile(r"(.*experts\.\d+)\.(gate_proj|up_proj)\.weight$")
+EXPERT_PAIR_RE = re.compile(r"(.*experts\.\d+)\.(w1|w3)\.weight$")
 
 
 def is_preserve(name: str) -> bool:
@@ -293,10 +299,10 @@ def build_plan(src_dir: Path):
     # Classify everything else
     solo_quantize = []
     preserve = []
-    scale_companions = []  # weight_scale_inv tensors that get consumed during dequant
+    scale_companions = []  # .scale tensors that get consumed during dequant
 
     for n in weight_map:
-        if n.endswith(".weight_scale_inv"):
+        if n.endswith(".scale") and n.replace(".scale", ".weight") in weight_map:
             scale_companions.append(n)
             continue
         if n in paired_names:
@@ -354,7 +360,7 @@ class ShardCache:
 def load_weight_and_scale(cache: ShardCache, weight_map, name):
     """Load an FP8 weight with its weight_scale_inv companion (if any)."""
     weight = cache.get(weight_map[name]).get_tensor(name)
-    scale_name = name.replace(".weight", ".weight_scale_inv")
+    scale_name = name.replace(".weight", ".scale")
     scale = None
     if scale_name in weight_map:
         scale = cache.get(weight_map[scale_name]).get_tensor(scale_name)