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

inspect_locomo.py

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+"""Inspect the downloaded LoCoMo dataset: structure, categories, evidence format."""
+import collections
+import json
+import sys
+from pathlib import Path
+
+path = Path(sys.argv[1] if len(sys.argv) > 1 else r"H:\Memory\V2_dpskw\data\net_locomo\locomo10.json")
+data = json.loads(path.read_text(encoding="utf-8"))
+print("conversations:", len(data))
+first = data[0]
+print("top keys:", list(first.keys()))
+conv = first["conversation"]
+print("conversation keys:", list(conv.keys()))
+for key in list(conv.keys())[:5]:
+    value = conv[key]
+    size = len(value) if hasattr(value, "__len__") else "-"
+    print(f"  {key}: {type(value).__name__} len={size}")
+    if isinstance(value, list) and value:
+        print("     first:", json.dumps(value[0], ensure_ascii=False)[:300])
+
+categories = collections.Counter()
+n_qa = 0
+turns = 0
+sessions = 0
+for item in data:
+    c = item["conversation"]
+    keys = [k for k in c if k.startswith("session_") and not k.endswith("date_time")]
+    sessions += len(keys)
+    turns += sum(len(c[k]) for k in keys)
+    for q in item["qa"]:
+        n_qa += 1
+        categories[q.get("category")] += 1
+
+print()
+print(f"total QA: {n_qa}   sessions: {sessions}   turns: {turns}")
+print("category distribution:", dict(sorted(categories.items())))
+
+print()
+print("=== one example per category ===")
+seen = set()
+for item in data:
+    for q in item["qa"]:
+        cat = q.get("category")
+        if cat in seen:
+            continue
+        seen.add(cat)
+        ev = q.get("evidence")
+        print(f"[cat {cat}] Q={q['question']!r}")
+        print(f"          answer={q['answer']!r}  evidence={ev}  adversarial={q.get('adversarial_answer')!r}")
+
+print()
+print("=== a conversation turn, verbatim ===")
+sample = data[0]["conversation"]["session_1"][:3]
+print(json.dumps(sample, ensure_ascii=False, indent=1)[:900])