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Oct 12 · Monday

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  1. 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

    Published
    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. OtherarXiv 2609.08832

    自进化智能体框架缩小 LLM 智能体的「一致性差距」

    提出离线两阶段框架,诊断易翻转步骤并生成记忆指南以稳定智能体

    Published
    Setting
    AI Agent
    Evaluated models
    GPT-4.1, GPT-OSS-120B
    Abstract用离线一致性诊断生成记忆指南,在 AppWorld 上提高重复全通过率。

    作者要缩小的是 LLM 智能体平均通过、重复不稳之间的 consistency gap,即 Mean@k 减 Passˆk。

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  3. Salesforce AI Research 重测自改进 Agent

    重测记忆型自改进 Agent 的运行方差与任务顺序敏感性

    Published
    Setting
    AI Agent
    Evaluated models
    GPT-5-mini, Gemini-2.5-Pro, GPT-OSS-120B and others4
    Abstract论文揭示了基于记忆的自改进智能体在多次运行和乱序下的脆弱性,并指出规约不完备是诱发退化的关键瓶颈。

    这篇论文是对基于文本记忆的自我改进智能体在复杂环境中评估可靠性的实证研究。

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  4. DefensesarXiv 2602.03224

    TAME:可信的 Agent 记忆测试时演化与系统化基准评测

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

    Published
    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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