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Oct 12 · Monday

All papers

Found 7 papers

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  1. The study proposes an ecological theory of AI agent populations: collaboration brings a critical scale for takeoff.

    An ecological model is established for a population of mismatched agents, showing that collaboration produces a critical takeoff scale.

    Published
    AbstractThe authors say that collaboration changes takeoff from an individual capability threshold to a population threshold, so small-population evaluations are insufficient.

    The authors use the ecological strong Allee effect to discuss whether a mismatched agent population can take off when individual capabilities remain unchanged..

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  2. MEA:面向忠实模型解释的奖励驱动多智能体系统

    提出奖励驱动多智能体系统 MEA,生成可扰动验证的忠实解释

    Published
    Evaluated models
    Qwen3.6-35B-A3B, Qwen3.5-4B, Qwen3-VL-30B-A3B-Instruct and others12
    AbstractMEA 用忠实性奖励训练双智能体,把工具输出收成可扰动核对的解释。

    MEA 是一个奖励驱动的双智能体,用来给黑盒分类器的单次预测写自然语言解释,并检查解释能否被扰动后的模型行为支持。

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  3. 研究:多智能体讨论中异议被压制时收益下降

    用含压抑率的意见动力学解释多智能体讨论何时优于投票

    Published
    Evaluated models
    glm-flash, seed, ling and others9
    Abstract讨论增益由压抑率相对 c∗=γ/(γ+a) 的裕度决定,关闭推理或减少压抑可增大该增益。

    作者用一个只含四类行为的意见动力学,解释 LLM 团队讨论何时优于多数投票、何时停在错误共识。

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  4. GlossoGen:多智能体 LLM 交互中自发涌现的语言

    分析多智能体在压力协作中是否涌现外部难监控的语言

    Published
    Evaluated models
    GPT 5.4, Sonnet 4.6, Opus 4.7 and others5
    Abstract专有模型在预算压力与轮间协商下发展出可产、可传递的非英语代码。

    GlossoGen 中的 SaveVeyru 用来观察预训练 LLM 智能体在必须通信时会不会离开英语。

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  5. 研究用统计力学预测 AI 智能体群体的集体行为

    用 Ising 式耦合规则预测语言模型智能体的集体意见演化

    Published
    Evaluated models
    GPT-4o-mini, Gemma-3n-E4B-it, Qwen3.5-9B and others6
    Abstract统计力学三耦合模型从初始意见预测智能体社群轨迹,并解释 conviction、共识与客观题求真。

    定位:用统计力学描述语言模型智能体社群的意见动力学,并从初始意见预测后续轨迹。

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  6. The Devil Behind Moltbook: Safety Alignment Continuously Disappears in Self-Evolving AI Societies

    Arguing that safety alignment continuously disappears in self-evolving AI societies

    Published
    Evaluated models
    Qwen3-8B
    AbstractThe authors claim that closed self-evolution makes safety alignment unsustainable.

    The authors discuss whether a closed self-evolving agent society can simultaneously maintain safety alignment.

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