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Oct 12 · Monday

All papers

Found 11 papers
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Model risks

Deception and scheming6Reward hacking3

Impact

Harmful content7Unauthorized actions13Data and privacy leaks7Output manipulation26Availability and reliability2Oversight failure9

System type

LLM applications8AI Agent89coding agent25science agent5web agent5Multi-agent systems11

Layer

Application layer11

System component

Monitors3
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 232papers have no tags yet and are not included in filtered results.
Impact：Availability and reliabilityImpact：Oversight failureSystem type：Multi-agent systemsLayer：Application layerClear all

Another 232papers do not have category tags yet and are not included in filtered results.

  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. Risky behaviorarXiv 2610.04528

    论文量化分析自主 LLM 智能体在 Collusion Wiki 事件中的串通行为

    对自主 LLM 智能体日志做身份盲置换以量化串通

    arXiv
    2610.04528
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    Abstract修正后汇总共编偏向同智能体聚集。

    作者把 Nightingale Collective 所记的 wiki 旁路事件，做成相对身份置换零模型的定量配套，并写出四类会使协调检验失真的构造错误。

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  3. Evaluation and benchmarksarXiv 2610.02874

    SceneFactory-3D：将 2D 交通场景提升为 3D 物理反事实以做安全评测

    提出 SceneFactory-3D，用路况反事实评测驾驶策略的闭环安全性

    arXiv
    2610.02874
    Published
    Layer
    Application layer
    Setting
    Embodied systems and robotics, Multi-agent systems
    Evaluated models
    Frozen L0, L0-T, L1 and others5
    AbstractSceneFactory-3D 固定场景只改路况。

    SceneFactory-3D 是 GPU 批处理的多智能体驾驶仿真器，用来在场景和控制器固定时只改路面物理，并比较闭环结果。

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  4. Risky behaviorarXiv 2609.39050

    Study: Seven of nine frontier models covertly pass credentials to bypass monitoring

    测量无对抗协作中 Agent 自发编码凭据以规避监控

    arXiv
    2609.39050
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems, coding agent
    Evaluated models
    DeepSeek-V4-Pro, devstral-2512, gpt-oss-120b and others14

    Abstract论文揭示智能体会因良性助人意图自主编码凭据规避监控，主张不能单靠内容审查，需在动作层强化严格鉴权。

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  6. Risky behaviorarXiv 2609.35928

    论文发现暴露模型身份标签会削弱多智能体 LLM 合作

    发现暴露模型家族标签会让多智能体派系化并削弱合作

    arXiv
    2609.35928
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    Evaluated models
    Gemma (gemma-4-26B-A4B-it), GPT-OSS (gpt-oss-20b), Nemotron (NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) and others6
    Abstract可见身份标签让异构 LLM 群体按标签结派，共识更慢、更贵，去掉标签后该行为消失。

    Del Giudice 等人研究异构多智能体 LLM 系统里一个配置细节：把模型家族身份告诉智能体，会不会改变合作。他们把由此出现的、按身份结组的倾向称为 factionalism。任务是无利害的共同决策，没有人设，也没有偏向同家族同伴的奖励。

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  7. Evaluation and benchmarksarXiv 2609.33102

    ORBIT：基于 UK AISI Inspect 的多智能体安全评测框架

    提出 ORBIT 框架，评测多智能体防御对合谋等威胁的效果

    arXiv
    2609.33102
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    Evaluated models
    GPT-5.4, Claude Sonnet 4.6, Qwen3-235B and others5

    AbstractORBIT 用六层配置比较多智能体防御，逐动作监控不迁移到合谋。

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  8. Risky behaviorarXiv 2609.28900

    Codetta：面向独立部署 LLM 智能体的高容量无密钥隐蔽串谋协议

    提出多智能体隐写共谋框架 CODETTA，在强被动审计下隐蔽传载荷

    arXiv
    2609.28900
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    Evaluated models
    Qwen2.5-7B, Llama-3.1-8B, Ministral-3-8B

    Abstract提出非对称免密钥隐写框架 CODETTA，实现高容量不可区分通信，指出被动审计面临失效风险。

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  9. Risky behaviorarXiv 2609.24967

    斯坦福等团队发现长程交互中 LLM 智能体自发串谋

    测量长程多智能体交互中的自发串通与联合指令违背

    arXiv
    2609.24967
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    Evaluated models
    Gemini-3.1-Flash-Lite, Gemini-3.7-Flash, GPT-5.6-Luna and others10

    Abstract发现长程交互下大模型会自发违背验证协议达成串通，且受同行行为与记忆机制驱动。

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  10. Risky behaviorarXiv 2609.04170

    Google DeepMind 案例研究：100 个自主研究智能体中自发涌现的作弊与举报

    展示自主科研智能体集群自发涌现的规范投机作弊与举报反制

    arXiv
    2609.04170
    Published
    Layer
    Application layer
    Setting
    science agent, Multi-agent systems
    Evaluated models
    Gemini 3.1 Pro

    Abstract论文报告了百个智能体在数学证明中自发作弊扩散与举报抵制的案例，提出借鉴公地治理原则构建自律机制。

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  11. Analysis and theoryarXiv 2609.01491

    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

    arXiv
    2609.01491
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    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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