Skip to content

Translation in progress Some entries still include text in the original language.

Oct 12 · Monday

All papers

Found 6 papers
Filter4

Model risks

Deception and scheming3

Impact

Oversight failure5

System type

Models and APIs4

Layer

Prompt layer3Model layer6Application layer51

System component

Memory1Guardrails and judges5Monitors5Reasoning chains6Fine-tuning and open weights0Vision8Audio4Video5Image generation3Multimodal16
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 232papers have no tags yet and are not included in filtered results.
Layer：Model layerSystem component：MemorySystem component：MonitorsSystem component：Fine-tuning and open weightsClear all

Another 232papers do not have category tags yet and are not included in filtered results.

  2. Evaluation and benchmarksarXiv 2610.04737

    PyINE：用可验证 Python 执行轨迹研究推理模型的捷径与监督

    提出 PyINE 基准，训练捷径跟随模型并比较监督者

    arXiv
    2610.04737
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    shortcut-following model organism, gpt-4o, gpt-5 and others12
    AbstractPyINE 用可验证代码执行研究捷径失败的监督覆盖与成本权衡。

    PyINE 是用可执行程序研究推理模型引出与监督的框架，PyINE-v1 是其第一次发布。

    Paper analysisRead analysis
  3. Evaluation and benchmarksarXiv 2610.02741

    研究系统评估循环语言模型的思维链可监控性

    系统评估循环语言模型加深 loop 后的 CoT 可监控性

    arXiv
    2610.02741
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Ouro-1.4B-Thinking, Ouro-2.6B-Thinking, Nanbeige4.2-3B and others4
    Abstract加深 loop 可在部分压力测试任务降低可监测性，循环架构本身并不必然更难监测。

    这项工作评估 LoopLM 的 CoT 可监测性：加深共享层的重复次数，以及“有循环结构”本身，会不会让决策关键因素更少出现在可监测文本里。

    Paper analysisRead analysis
  4. Evaluation and benchmarksarXiv 2609.39533

    CATCH：面向编码 RL 奖励作弊的可控分析测试台

    构建编码 RL 奖励作弊测试台 CATCH 并检验 CoT 监控失效

    arXiv
    2609.39533
    Published
    Layer
    Model layer
    Setting
    coding agent
    Evaluated models
    Qwen3-4B, Qwen3.5-27B
    AbstractCATCH 用双运行金标签研究编码 RL 的奖励黑客，并观察到监控会被注释适应削弱。

    CATCH 是面向编码 RL 奖励黑客的可控分析测试床，用可利用环境、可调初始倾向和基于执行的金标签，在训练全程比较黑客动态与干预。

    Paper analysisRead analysis
  5. Evaluation and benchmarksarXiv 2609.39473

    FAME：用反事实推理评测自主智能体的虚假记忆

    提出 FAME，用反事实概念漂移评测智能体虚假记忆

    arXiv
    2609.39473
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3B
    AbstractFAME 用反事实引起的隐状态概念漂移，在出答案前区分忠实记忆与虚假记忆。

    FAME 是一个免训练的评测框架，用来判断自主智能体的记忆在虚假相关、环境偏移或知识冲突下是否已变成虚假记忆。。

    Paper analysisRead analysis
  6. Evaluation and benchmarksarXiv 2602.08877

    Automated red-teaming pipeline stress-tests alignment audits, finds prompt-level strategic deception can render audits ineffective

    提出自动化红队流水线，用系统提示词压测对齐审计能否抵御策略性欺骗

    arXiv
    2602.08877
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Gemma 2 9B Instruct, Llama 3.3 70B Instruct

    Abstract通过红队提示词揭示现有审计方法易受策略欺骗，并在激活层面诱导高置信度错误推断。

    Paper analysisRead analysis
You have reached the end