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
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"""Spot-check bilingual refusal detection on real reply wordings."""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from V2_dpskw.eval_scoring import is_refusal
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CASES = [
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("zh", "关于您的 weekly 会议安排,当前长期记忆中未包含相关信息,无法回答。", True),
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("zh", "您的协议编号未知。", True),
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("zh", "您的培训周期是 32GB。", False),
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("zh", "我固定每周三 22:00 做上线。", False),
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("en", "I don't know when that happened.", True),
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("en", "There is no mention of a charity race in the conversation.", True),
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("en", "The date was not mentioned.", True),
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("en", "I cannot determine that from the dialogue.", True),
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("en", "She was unable to answer that question about her research.", True),
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("en", "The conversation does not mention any support group.", True),
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("en", "I'm not sure about that.", True),
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("en", "She went to the LGBTQ support group on 7 May 2023.", False),
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("en", "2022", False),
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("en", "Caroline researched adoption agencies.", False),
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("en", "Melanie painted a sunrise in 2022.", False),
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]
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bad = 0
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for lang, reply, expected in CASES:
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got = is_refusal(reply)
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flag = "ok " if got == expected else "BAD"
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if got != expected:
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bad += 1
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print(f"{flag} [{lang}] expected={'REFUSE' if expected else 'assert'} got={'REFUSE' if got else 'assert'} {reply[:64]}")
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print(f"\nmismatches: {bad}/{len(CASES)}")
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