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Oct 13 · Tuesday

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Models and APIs231LLM applications5AI Agent30coding agent8web agent0Embodied systems and robotics14

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Prompt layer5Model layer6Application layer154Training and data layer7Infrastructure layer1
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  1. OtherarXiv 2610.11959

    MiMo-V2.6: Toward Self-Improvement via Scaled Reinforcement Learning

    Extending reinforcement learning for full-modality foundation models to support agent self-improvement

    arXiv
    2610.11959
    Published
    Layer
    Training and data layer
    Setting
    coding agent
    Evaluated models
    MiMo-V2.6-Pro, MiMo-V2.6-Flash, MiMo-V2.6-Distill-Qwen-9B
    AbstractThe series uses three-dimensional RL and group scoring to improve agent scores; the authors claim the final performance is comparable to frontier models.

    MiMo-V2.6 is an all-modality MoE technical report, pursuing agent self-improvement by scaling up RL computation, environments, and scoring.

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  3. AttacksarXiv 2610.05793

    研究者训练出按多智能体拓扑触发的代码后门模型

    提出拓扑条件后门，使代码模型在推断多智能体部署时植入漏洞

    arXiv
    2610.05793
    Published
    Layer
    Training and data layer
    Setting
    coding agent
    Evaluated models
    Qwen2.5-7B-Instruct, Qwen2.5-Coder-7B-Instruct

    Abstract论文证实了拓扑条件后门的可训练性与泛化性，展示了常规评测盲区，并提出差异化拓扑审计作为检测防御方向。

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  4. Risky behaviorarXiv 2610.03458

    Paper: Low monitoring readings do not prove behavior is under control

    检验低监控读数能否证明代码生成中的奖励作弊已受控

    arXiv
    2610.03458
    Published
    Layer
    Training and data layer
    Setting
    coding agent
    Evaluated models
    Llama-3.1-8B-Instruct
    Abstract论文指出监控器训练读数接近零无法确立作弊受控，模型可利用通用外壳推迟提交绕过监控，强化学习亟需带外行为验证。

    本文是一篇针对强化学习可验证奖励后训练（RLVR）中监控器有效性与奖励作弊行为的实证分析研究。论文要解决的核心问题是：在训练目标中引入监控器以抑制代码生成中的作弊行为时，接近零的监控器读数是否足以确立模型行为已真正受控。

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  5. DefensesarXiv 2610.00850

    AuraForge：为训练安全编码 Agent 扩展安全监督

    提出 AuraForge，从漏洞修复合成安全测试并训练编码 Agent

    arXiv
    2610.00850
    Published
    Layer
    Training and data layer
    Setting
    coding agent
    Evaluated models
    Qwen3.5-4B, GLM 4.7 Flash, Qwen3-Coder-Next and others7
    AbstractAuraForge 用攻击效果测试和防泄露环境做成 AuraGym，合成监督的误拒更低，训出的 4B 模型增益高于人工测试。

    AuraForge 把真实仓库的历史漏洞修复变成可执行训练任务，用来监督编程智能体同时满足功能请求和隐含的安全要求。

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  6. Analysis and theoryarXiv 2609.38723

    面向智能体的高效 HPC 系统：挑战与机遇

    刻画 HPC 上编码智能体的使用差异并提出设施协同设计议程

    arXiv
    2609.38723
    Published
    Layer
    Infrastructure layer
    Setting
    coding agent
    AbstractFASRC 测量说明编码智能体改变 HPC 使用方式，作者据此提出四层协同设计议程。

    这是一篇对生产 HPC 上编码智能体的测量与系统议程论文，不是攻击实现或防御评测。。

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  8. OtherarXiv 2607.28568

    Frontis-MA1：面向机器学习工程递归自我改进的 AI4AI 模型

    提出 OpenMLE，后训练元进化智能体并做机器学习工程长程搜索

    arXiv
    2607.28568
    Published
    Layer
    Training and data layer
    Setting
    coding agent
    Evaluated models
    Frontis-MA1-35B, Frontis-MA1-30B
    AbstractOpenMLE 训练 Frontis-MA1-35B，用四个共享算子把 MLE 后训练和长程搜索接成元进化闭环。

    作者提出全栈 OpenMLE，在机器学习工程里研究从进化到元进化的一步，并把递归自我改进视为更远目标。智能体要在延迟、有噪声的执行反馈下反复修改程序。作者认为公开系统很少同时提供可扩展环境、基于执行的后训练，以及部署该模型的长程进化搜索。

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