diff --git a/代码/code/bench.py b/代码/code/bench.py
index 0c4cb4f..27308dd 100644
--- a/代码/code/bench.py
+++ b/代码/code/bench.py
@@ -324,6 +324,7 @@ def _parse_args():
     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("--emb-bag", action="store_true", help="F.embedding_bag 融合查表+池化")
     ap.add_argument("--sparse-pool", action="store_true", help="稀疏矩阵乘做池化(段内高重复时省)")
     ap.add_argument("--precompute-rep", action="store_true",
                     help="预计算RepEncoder缓存,model(batch)跳过embedding层(从batches自建)")
@@ -370,6 +371,8 @@ if __name__ == "__main__":
         cfg["emb_fp16"] = True
     if a.dedup_emb:
         cfg["dedup_embedding"] = True
+    if a.emb_bag:
+        cfg["use_embedding_bag"] = True
     if a.sparse_pool:
         cfg["sparse_pool"] = True
     if a.precompute_rep:
diff --git a/代码/code/infer.py b/代码/code/infer.py
index 5854344..f66540f 100644
--- a/代码/code/infer.py
+++ b/代码/code/infer.py
@@ -145,6 +145,7 @@ 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)
+    "use_embedding_bag": False,  # True=用 F.embedding_bag 融合查表+池化(单kernel,免[M,512]中间),攻最大块
     "dedup_embedding": True,  # True=查表前对sign去重(只查唯一值再展开),本地7.80->6.49s,AUC逐位等价
     "sparse_pool": False,     # True=用(段×唯一)稀疏矩阵乘做池化,避免materialize整个[M,512](段内高重复时省)
     "compile": False,         # 是否 torch.compile（实测慢5×，勿开）
@@ -520,7 +521,12 @@ 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]
-        if CONFIG.get("sparse_pool", False):
+        if CONFIG.get("use_embedding_bag", False):
+            # F.embedding_bag 融合"查表+按段求和"，单 kernel，免 [M,emb] 中间。
+            pooled = F.embedding_bag(
+                cat_values, self.emb.weight,
+                offsets=seg[:-1].contiguous(), mode="sum").to(target_dtype)
+        elif CONFIG.get("sparse_pool", False):
             # 稀疏池化：pooled = W @ emb_unique，W[段,唯一]=该段内该唯一sign出现次数。
             # 段内高重复(slot19)塌缩成单个带权项，避免 materialize 整个 [M,emb]。
             uniq, inv = torch.unique(cat_values, return_inverse=True)
diff --git a/代码/code/tests/test_equiv.py b/代码/code/tests/test_equiv.py
index a4fa482..49a81b8 100644
--- a/代码/code/tests/test_equiv.py
+++ b/代码/code/tests/test_equiv.py
@@ -86,6 +86,30 @@ def test_chunked_matches_dense_attention():
     print(f"[PASS] chunked SDPA == 稠密SDPA (max err={err:.2e}, dev={dev})")
 
 
+def test_embedding_bag_matches():
+    torch.manual_seed(0)
+    dev = "cuda" if torch.cuda.is_available() else "cpu"
+    slot_num, emb_dim, d_model = 28, 512, 512
+    enc = infer.RepEncoder(vocab_size=200, emb_dim=emb_dim, slot_num=slot_num,
+                           d_model=d_model).to(dev).eval()
+    N = 6
+    batch = {}
+    for s in range(1, slot_num + 1):
+        counts = torch.randint(0, 8, (N,))
+        vals = torch.randint(0, 200, (int(counts.sum()),), device=dev)
+        offs = torch.cat([torch.zeros(1, dtype=torch.long), counts.cumsum(0)]).to(dev)
+        batch[s] = (vals, offs)
+    with torch.no_grad():
+        infer.CONFIG["use_embedding_bag"] = False
+        ref = enc(batch)
+        infer.CONFIG["use_embedding_bag"] = True
+        new = enc(batch)
+    infer.CONFIG["use_embedding_bag"] = False
+    err = (ref - new).abs().max().item()
+    assert torch.allclose(ref, new, atol=1e-3, rtol=1e-3), f"embedding_bag 不等价 max err={err:.3e}"
+    print(f"[PASS] embedding_bag == segment_reduce (max err={err:.2e}, dev={dev})")
+
+
 def test_sparse_pool_matches():
     torch.manual_seed(0)
     dev = "cuda" if torch.cuda.is_available() else "cpu"
@@ -217,6 +241,7 @@ def test_flex_matches_dense_attention():
 if __name__ == "__main__":
     test_moe_dense_matches_loop()
     test_fused_embedding_matches_perslot()
+    test_embedding_bag_matches()
     test_sparse_pool_matches()
     test_syncfree_mask_matches()
     test_triton_varlen_matches_dense()