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
2026-09-19 11:11:31 +08:00
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+"""Create train/eval streams for learned write, forget, and no-hallucination tests."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import random
+from pathlib import Path
+
+
+ATTRIBUTES = ("常用语言", "备用联系人", "工作区域", "档案代号", "提醒时间")
+VALUES = tuple("ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789")
+
+FACT_USER = (
+    "请记住：{subject}的{attribute}是代号{value}。",
+    "把这条个人资料写入记忆：{subject}的{attribute}为{value}。",
+    "个人事实更新——{subject}的{attribute}等于{value}，以后可能会问到。",
+)
+FACT_ASSISTANT = (
+    "好的，这条资料已经记录。",
+    "收到，我会保留这条个人事实。",
+    "已保存。",
+)
+NOISE_USER = (
+    "今天天气不错，随便聊聊。",
+    "请给我一个简短的问候。",
+    "这是一条不需要长期记忆的闲聊。",
+)
+NOISE_ASSISTANT = (
+    "好的。",
+    "明白。",
+    "收到。",
+)
+QUERY_KNOWN = (
+    "只根据已经保存的个人资料，{subject}的{attribute}是什么？",
+    "不要猜测，请读取记忆回答：{subject}的{attribute}为？",
+    "跨对话查询：请问{subject}的{attribute}代号是什么？",
+)
+QUERY_UNKNOWN = (
+    "记忆中是否有{subject}的{attribute}？如果没有，请明确说不知道。",
+    "请查询个人记忆：{subject}的{attribute}是什么？没有记录时不要猜。",
+)
+
+
+def _messages(user: str, assistant: str) -> list[dict[str, str]]:
+    return [{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]
+
+
+def _fact_chunk(subject: str, attribute: str, value: str, rng: random.Random, *, forget: int = 0) -> dict:
+    fields = {"subject": subject, "attribute": attribute, "value": value}
+    return {
+        "messages": _messages(rng.choice(FACT_USER).format(**fields), rng.choice(FACT_ASSISTANT)),
+        "value": value,
+        "write_label": 1.0,
+        "forget_label": float(forget),
+        "kind": "fact" if not forget else "replacement",
+    }
+
+
+def _noise_chunk(rng: random.Random) -> dict:
+    return {
+        "messages": _messages(rng.choice(NOISE_USER), rng.choice(NOISE_ASSISTANT)),
+        "value": None,
+        "write_label": 0.0,
+        "forget_label": 0.0,
+        "kind": "noise",
+    }
+
+
+def make_record(index: int, *, prefix: str, rng: random.Random) -> dict:
+    subject = f"{prefix}{index:05d}"
+    attribute = rng.choice(ATTRIBUTES)
+    value = rng.choice(VALUES)
+    mode = rng.random()
+    chunks: list[dict] = []
+    if mode < 0.20:
+        chunks.append(_noise_chunk(rng))
+        answer = "不知道。"
+        query = rng.choice(QUERY_UNKNOWN).format(subject=subject, attribute=attribute)
+        answerable = False
+    elif mode < 0.45:
+        old_value = rng.choice(tuple(item for item in VALUES if item != value))
+        chunks.append(_fact_chunk(subject, attribute, old_value, rng))
+        chunks.append(_noise_chunk(rng))
+        chunks.append(_fact_chunk(subject, attribute, value, rng, forget=1))
+        answer = value
+        query = rng.choice(QUERY_KNOWN).format(subject=subject, attribute=attribute)
+        answerable = True
+    else:
+        if rng.random() < 0.35:
+            chunks.append(_noise_chunk(rng))
+        chunks.append(_fact_chunk(subject, attribute, value, rng))
+        if rng.random() < 0.35:
+            chunks.append(_noise_chunk(rng))
+        answer = value
+        query = rng.choice(QUERY_KNOWN).format(subject=subject, attribute=attribute)
+        answerable = True
+    return {
+        "id": f"{prefix.lower()}-{index:05d}",
+        "memory_chunks": chunks,
+        "query": _messages(query, answer),
+        "subject": subject,
+        "attribute": attribute,
+        "answer": answer,
+        "answerable": answerable,
+    }
+
+
+def write_jsonl(path: Path, records: list[dict]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(
+        "".join(json.dumps(record, ensure_ascii=False) + "\n" for record in records),
+        encoding="utf-8",
+    )
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--output-dir", default="V2_dpskw/data/native_memory")
+    parser.add_argument("--train-count", type=int, default=512)
+    parser.add_argument("--eval-count", type=int, default=128)
+    parser.add_argument("--seed", type=int, default=20260904)
+    args = parser.parse_args()
+    train = [
+        make_record(i, prefix="训练用户", rng=random.Random(args.seed + i * 17))
+        for i in range(args.train_count)
+    ]
+    evaluation = [
+        make_record(i, prefix="评估用户", rng=random.Random(args.seed + 100000 + i * 17))
+        for i in range(args.eval_count)
+    ]
+    output_dir = Path(args.output_dir)
+    write_jsonl(output_dir / "train.jsonl", train)
+    write_jsonl(output_dir / "eval.jsonl", evaluation)
+    (output_dir / "manifest.json").write_text(
+        json.dumps(
+            {
+                "seed": args.seed,
+                "train_count": len(train),
+                "eval_count": len(evaluation),
+                "task": "learned persistent memory with noise, unknowns, and replacement",
+                "contains_write_labels": True,
+                "contains_forget_labels": True,
+                "answer_is_not_derived_from_subject": True,
+            },
+            ensure_ascii=False,
+            indent=2,
+        ),
+        encoding="utf-8",
+    )
+    print(f"train={len(train)} path={output_dir / 'train.jsonl'}")
+    print(f"eval={len(evaluation)} path={output_dir / 'eval.jsonl'}")
+
+
+if __name__ == "__main__":
+    main()