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Oct 12 · Monday

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  1. Jev 被用于零样本检测十类对齐失败,中位 AUROC 达 0.886

    提出 RLCDAlignBench,零样本检测多种对齐失败

    Published
    Evaluated models
    Jev (jev-1.13.0)
    Abstract一次通用问题已能给多数对齐失败排序,大的误差来自状态和标签。

    RLCDAlignBench 测量用强化学习做校准决策的 Jev,能否当作对齐失败的零样本检测器。

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  2. DefensesarXiv 2609.01046

    HiveTraceGuard-Pro:面向提示注入、越狱与对抗混淆的紧凑生成式护栏

    提出生成式护栏 HiveTraceGuard-Pro,检测提示注入、越狱与混淆

    Published
    Evaluated models
    HiveTraceGuard-Pro (0.6B)
    Abstract0.6B 俄英护栏在自建俄语干净集和 PI-RU 上得分最高,聚合 key 居第三,过阻断和回复精度仍是作者标出的短板。

    HiveTraceGuard-Pro 是面向请求与回复审核的 0.6B 生成式护栏,由 Qwen3-0.6B 经 LoRA 微调,在俄语和英语上训练,只输出一个 safe/unsafe 裁决。

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