Skip to content

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

Oct 13 · Tuesday

All papers

Found 2 papers
Filter5

Model risks

Deception and scheming2Reward hacking1

System type

Models and APIs29AI Agent5coding agent1Multi-agent systems1

Layer

Model layer2Application layer30
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 201papers have no tags yet and are not included in filtered results.
Model risks：Deception and schemingModel risks：Reward hackingSystem type：coding agentSystem type：Multi-agent systemsLayer：Model layerClear all

Another 201papers do not have category tags yet and are not included in filtered results.

  1. InterpretabilityarXiv 2610.02702

    Study: LLM agents retain their original premises internally even when conforming to the majority

    用 J-lens 检测辩论中屈从多数的智能体是否仍内部表征原前提

    arXiv
    2610.02702
    Published
    Layer
    Model layer
    Setting
    Multi-agent systems
    Evaluated models
    Qwen3.5-4B, Qwen3.6-27B, Gemma-4-E4B-it and others4

    Abstract揭示 LLM 智能体屈从多数时内部仍表征原推理前提，指出辩论表态共识可能夸大真实认同。

    Paper analysisRead analysis
  2. Evaluation and benchmarksarXiv 2609.39533

    CATCH: A Controllable Analysis Testbed for Coding RL Reward Hacking

    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
You have reached the end