Natural Memory NM2.1: 记忆路由器分叉、数据集缺陷修复与全轴评测证据

- 引入 MemoryRouterXL 与 v5/v6 流式多线程训练/编码管线
- 修复 prepare_memory_router_dataset 候选池重建缺陷（mega 家族 3568x 加速，输出逐字节相同）
- 修复 v5 被破坏的拒答与多跳标签（train 未知样本 319 -> 16319，multi_hop 平均正例 1.00 -> 2.00）
- 同存储预算下 V2-128 v6 逐轴 22/22 通过：Top-1 41.12% -> 94.62%，未知拒答 0.00% -> 100.00%
- 记录三条被实测推翻的显然优化（logits_to_keep=1 反而慢 55%、XL 容量未带来收益）
- 记忆手术跨架构可移植性 14/14，读写关闭时与原生模型逐位相同

audit_refusal_detection.py

@@ -0,0 +1,70 @@
+"""Audit the unknown-refusal axis: are the 0/25 refusals real, or misdetected?
+
+`ABSTENTION_MARKERS` is a fixed keyword list ("不知道", "没有记录", "无法确认",
+"未找到", "不清楚", "没有相关信息").  A reply that says
+"当前长期记忆中未包含相关信息，无法回答。" is a refusal in plain Chinese but
+contains none of those exact strings, so it is scored as a confident wrong answer.
+
+This prints every unknown_category reply so the true refusal rate can be counted
+by hand, and groups them by which abstention signal (if any) they carry.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+import sys
+from collections import Counter
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from V2_dpskw.eval_scoring import ABSTENTION_MARKERS
+from V2_dpskw.rescore_e2e import load_corpus, load_run
+
+# Refusal shapes actually observed in this project's replies, expressed as
+# patterns rather than exact strings.
+REFUSAL_PATTERNS = [
+    r"不知道",
+    r"没有(相关)?(记录|信息)",
+    r"未(包含|找到|记录|提及|提供)",
+    r"无法(回答|确认|确定|提供)",
+    r"不清楚",
+    r"缺乏(具体)?(上下文|信息)",
+    r"未(曾)?(在)?(长期)?记忆中(包含|出现|找到|记录)",
+    r"证据不足",
+    r"无法(从|根据)(记忆|证据)",
+    r"没有(足够)?(的)?证据",
+]
+REFUSAL_RE = re.compile("|".join(REFUSAL_PATTERNS))
+
+
+def looks_like_refusal(reply: str) -> bool:
+    return bool(REFUSAL_RE.search(reply))
+
+
+def main(corpus, run, category="unknown_attribute"):
+    cases = load_corpus(Path(corpus), 25)
+    rows = load_run(Path(run))
+    n = 0
+    markers_hit = 0
+    pattern_hit = 0
+    for case, row in zip(cases, rows):
+        if case["category"] != category:
+            continue
+        n += 1
+        reply = row.get("reply", "")
+        m = [x for x in ABSTENTION_MARKERS if x in reply]
+        p = bool(looks_like_refusal(reply))
+        markers_hit += int(bool(m))
+        pattern_hit += int(p)
+        print(f"[{n:02d}] markers={m or '-'} refusal_pattern={p}")
+        print(f"     Q: {case['query']}")
+        print(f"     R: {reply}")
+    print(f"\n{category}: {n} cases")
+    print(f"  detected by keyword list : {markers_hit}/{n}")
+    print(f"  detected by pattern set  : {pattern_hit}/{n}")
+
+
+if __name__ == "__main__":
+    main(sys.argv[1], sys.argv[2])