diff --git a/代码/code/bench.py b/代码/code/bench.py
index c0c4f67..ea890b1 100644
--- a/代码/code/bench.py
+++ b/代码/code/bench.py
@@ -298,6 +298,7 @@ def _parse_args():
                     help="MoE实现：dense=向量化(新), loop=逐expert循环(原)")
     ap.add_argument("--compile", action="store_true", help="开启 torch.compile")
     ap.add_argument("--emb-fp16", action="store_true", help="Embedding表转FP16(查表带宽减半,测AUC)")
+    ap.add_argument("--dedup-emb", action="store_true", help="查表前对sign去重(减少大表随机访存)")
     ap.add_argument("--profile", type=int, default=None, metavar="N",
                     help="剖析前 N 个 batch，打印按 CUDA 耗时排序的算子表（定位瓶颈）")
     ap.add_argument("--rebuild", action="store_true", help="强制重建过滤缓存")
@@ -331,6 +332,8 @@ if __name__ == "__main__":
         cfg["vectorize_moe"] = (a.moe == "dense")
     if a.emb_fp16:
         cfg["emb_fp16"] = True
+    if a.dedup_emb:
+        cfg["dedup_embedding"] = True
     if a.compile:
         cfg["compile"] = True
     if a.profile is not None:
diff --git a/代码/code/infer.py b/代码/code/infer.py
index 8397e07..ac078e6 100644
--- a/代码/code/infer.py
+++ b/代码/code/infer.py
@@ -52,6 +52,7 @@ CONFIG = {
     "fuse_embedding": True,   # True=28个slot的查表+池化融合为1次(减per-batch kernel启动)
     "syncfree_mask": True,    # True=用searchsorted构造因果mask(无同步)；False=repeat_interleave(同步)
     "emb_fp16": True,         # True=Embedding表转FP16(查表带宽减半,实测AUC 0.75932≈无损)
+    "dedup_embedding": False, # True=查表前对sign去重(只查唯一值再展开),减少大表随机访存。数学等价
     "compile": False,         # 是否 torch.compile（实测慢5×，勿开）
 }
 
@@ -380,7 +381,12 @@ class RepEncoder(nn.Module):
         cat_values = self._signid(torch.cat(parts), max_idx)
         seg = torch.cat([torch.zeros(1, dtype=torch.long, device=cat_values.device),
                          torch.cat(ends)])   # [28*N + 1]
-        emb = self.emb(cat_values).to(target_dtype)
+        if CONFIG.get("dedup_embedding", False):
+            # 去重：只对唯一 sign 查大表，再按逆索引展开（数学逐位等价，省随机访存）
+            uniq, inv = torch.unique(cat_values, return_inverse=True)
+            emb = self.emb(uniq).to(target_dtype)[inv]
+        else:
+            emb = self.emb(cat_values).to(target_dtype)
         pooled = torch.segment_reduce(emb, reduce='sum', offsets=seg, initial=0)  # [28*N, emb]
         pooled = pooled.view(self.slot_num, N, self.emb_dim).permute(1, 0, 2).reshape(
             N, self.slot_num * self.emb_dim)