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Oct 12 · Monday

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Found 2 papers
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Model risks

Reward hacking2Misalignment and value divergence7Dangerous agent actions6

System component

Memory2Guardrails and judges19Monitors12Reasoning chains6Permissions and sandboxing7Embeddings0Multimodal3
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  1. Risky behaviorarXiv 2610.12822

    Reward hacking as a transmissible strategy in multi-agent coding systems

    Show that reward hacking can propagate through code, memory, and plans in multi-agent coding systems

    arXiv
    2610.12822
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems, coding agent
    Evaluated models
    Qwen2.5-Coder-14B, Gemma-3-12B, Qwen2.5-Coder-7B and others16
    AbstractThe authors state that reward hacking can spread across models and affect routing and aggregation, and that review can reduce delivery.

    Reward hacking induced by coding RL can leave the source agent and enter subsequent collaboration and system coordination.

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  2. DefensesarXiv 2602.03224

    TAME: Trustworthy Agent Memory Test-time Evolution and Systematic Benchmarking

    提出 TAME 调节 Agent 测试时记忆演化，并用 Trust-Memevo 评测可信度

    arXiv
    2602.03224
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    Qwen3-32B, GPT-5.2
    AbstractTAME 在共享记忆上同时调节效用与信任。

    TAME 针对测试时记忆演化中的 Agent Memory Misevolution：良性任务只按成败保留经验时，任务表现可以上升，安全、隐私、公平等可信度下降或随领域波动。

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