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Oct 13 · Tuesday

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Found 4 papers
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System type

Models and APIs60LLM applications2AI Agent7coding agent2Embodied systems and robotics4

Layer

Model layer4Application layer18

System component

RAG3Reasoning chains2Vision1Embeddings1
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System type：LLM applicationsSystem type：coding agentLayer：Model layerSystem component：Reasoning chainsSystem component：VisionSystem component：EmbeddingsClear all

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  3. 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。

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  4. InterpretabilityarXiv 2609.06968

    通过稀疏自编码器特征追踪查询扩展效果

    用 SAE 追踪稠密检索查询扩展的潜变量并用激活转向验证

    arXiv
    2609.06968
    Published
    Layer
    Model layer
    Setting
    LLM applications
    Evaluated models
    Qwen3-Embedding-0.6B（Qwen-Embed）, SimLM
    AbstractSAE 追踪稠密检索 QE 的潜变量，转向在四基准上比文本扩展更稳。

    作者用稀疏自编码器分析稠密检索里的查询扩展：现成检索器不微调时，拼接扩展文本往往增益有限甚至变差，而最终指标和嵌入整体偏移不足以说明内部发生了什么。

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