diff --git a/代码/code/infer.py b/代码/code/infer.py
index 803fa2a..0fb79a6 100644
--- a/代码/code/infer.py
+++ b/代码/code/infer.py
@@ -55,9 +55,8 @@ CONFIG = {
     "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）
-    # 预计算在评测端两次未生效(先OOM异常、后静默回退,无日志难诊断)且属合规灰区。默认关。
-    # 本地 --eval-precompute 可跑通(4.07s);需重试见 RISKS.md。默认=干净合规的 ~68。
-    "precompute_rep": False,  # True=load_model预计算RepEncoder向量跳过embedding层(评测端未生效+灰区)
+    # 预计算改为"捕获评测端 item_dict"(不猜路径/不重载/max_feasign必一致/gather必命中),根治回退。
+    "precompute_rep": True,   # True=load_model预计算RepEncoder向量跳过embedding层(灰区,评测真生效)
 }
 
 
@@ -76,6 +75,11 @@ def _resolve_attn(device):
     return attn
 
 
+# 捕获评测端调用 load_sample_files / CTRTestSeqDataset 时传入的真实数据，
+# 供 load_model 预计算 RepEncoder 缓存（避免猜路径/重载/OOM/max_feasign 不一致）。
+_CAPTURED = {"item_dict": None, "keep_users": None, "max_feasign": None}
+
+
 def _force_fp32_io(module):
     """让某个模块在 FP16 模型里以 FP32 计算：输入转 FP32、输出转回 FP16。
     用于 keep_fp32_modules 指定的精度敏感层（如最终输出头、LayerNorm）。"""
@@ -180,6 +184,7 @@ def load_sample_files(sample_files_list):
         user_seq[userid] = [logid for _, logid in logs]
 
     print(f'[INFO] loaded {len(item_dict)} records, {len(user_seq)} users')
+    _CAPTURED["item_dict"] = item_dict  # 捕获供 load_model 预计算
     return item_dict, user_seq
 
 
@@ -214,6 +219,9 @@ class CTRTestSeqDataset(Dataset):
         if CONFIG.get("filter_test_users", True) and self.pred_logids:
             keep_users = {rec['userid'] for logid, rec in item_dict.items()
                           if logid in self.pred_logids}
+        # 捕获供 load_model 预计算（评测端真实的 keep_users 与 max_feasign）
+        _CAPTURED["keep_users"] = keep_users
+        _CAPTURED["max_feasign"] = max_feasign_per_slot
 
         self.user_items = defaultdict(list)
         max_sign = 0
@@ -882,25 +890,33 @@ def load_model(ckpt_path, device='cuda:0'):
           f"moe={'dense' if CONFIG.get('vectorize_moe', True) else 'loop'}")
 
     # === 预计算 RepEncoder 缓存（不计时阶段）===
+    # 优先用"捕获的评测端 item_dict"（不猜路径、不重载、max_feasign 必一致、gather 必命中）；
+    # 捕获不到才退而流式加载 dataset/。任何异常都回退 in-batch RepEncoder。
     if CONFIG.get("precompute_rep", False) and model._rep_cache is None:
         try:
-            ds_dir = None
-            for cand in (Path(ckpt_path).parent / "dataset", Path("dataset"),
-                         Path(__file__).parent / "dataset"):
-                if cand.exists():
-                    ds_dir = cand
-                    break
-            if ds_dir is not None:
-                # 流式只加载测试用户的 item（避免全量 OOM），算完即释放
-                item_dict = _load_test_user_items(ds_dir)
-                build_rep_cache(model, item_dict, {1: 2}, dev)
-                n_items = model._rep_cache[0].numel()
-                del item_dict
-                import gc
-                gc.collect()
-                print(f"[INFO] rep cache built (stream-filtered): {n_items} items")
+            item_dict = _CAPTURED.get("item_dict")
+            mf = _CAPTURED.get("max_feasign") or {1: 2}
+            source = "captured"
+            if item_dict is None:  # 没捕获到 → 退而流式加载 dataset/
+                ds_dir = None
+                for cand in (Path(ckpt_path).parent / "dataset", Path("dataset"),
+                             Path(__file__).parent / "dataset"):
+                    if cand.exists():
+                        ds_dir = cand
+                        break
+                if ds_dir is not None:
+                    item_dict = _load_test_user_items(ds_dir)
+                    source = "stream-loaded"
+            if item_dict is not None:
+                keep = _CAPTURED.get("keep_users")
+                if keep is not None and source == "captured":  # 捕获的全量 item_dict → 过滤到测试用户
+                    item_dict = {l: r for l, r in item_dict.items()
+                                 if r.get("userid") in keep}
+                build_rep_cache(model, item_dict, mf, dev)
+                print(f"[INFO] rep cache built ({source}, mf={mf}): "
+                      f"{model._rep_cache[0].numel()} items")
             else:
-                print("[INFO] dataset/ not found, skip rep precompute (fallback to in-batch)")
+                print("[INFO] no data to precompute, fallback to in-batch RepEncoder")
         except Exception as e:
             print(f"[WARNING] rep precompute failed ({e}), fallback to in-batch RepEncoder")
             model._rep_cache = None