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Oct 12 · Monday

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OtherarXiv 2610.13479Oct 9
A local agentic AI framework generates edge-case scenarios for safety assurance
Proposes a local dual-model closed loop to generate edge scenarios for autonomous system safety assurance
arXiv
2610.13479
Published
Oct 9
Layer
Application layer
Setting
LLM applications, Embodied systems and robotics
Evaluated models
PPO
AbstractLocal closed-loop generation of JSON edge cases.
The authors built a local agentic loop that runs on a laptop to generate edge cases for safety arguments of autonomous systems, rather than releasing a new public test set.
Paper analysisRead analysis
OtherarXiv 2610.13376Oct 9
EndoRSI: Recursive Self-Improvement of Endoscopic Robot Policies Driven by Physical Experiment Evidence
Proposes EndoRSI, recursively improving endoscopic robot policies with physical evidence
arXiv
2610.13376
Published
Oct 9
Layer
Application layer
Setting
Embodied systems and robotics
Evaluated models
Qwen3-VL-4B
Paper analysisRead analysis
OtherarXiv 2610.13372Oct 9
Toward Safe World Models: Risks, Defenses, and Evaluation
A survey of risks, defenses, and evaluation in the six-stage lifecycle of world models
arXiv
2610.13372
Published
Oct 9
Layer
Application layer
Setting
Embodied systems and robotics
AbstractSafety is written as a property of the prediction-decision closed loop; claims must be linked to observed consequences and versions.
World model safety is organized by the nature of the prediction-decision loop, not by isolated prediction scores.
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.13116Oct 9
DRYLAB-Bench: Evaluating biosecurity risks of agentic LLM mutation design
Proposing DRYLAB-Bench to evaluate agentic LLMs' high-risk mutation design capability
arXiv
2610.13116
Published
Oct 9
Layer
Application layer
Setting
science agent
Evaluated models
Claude Opus 5, Claude Opus 4.8, Claude Fable 5 and others15
- RiskDangerous capabilities
Paper analysisRead analysis
OtherarXiv 2610.12369Oct 9
Embodied Turing Machine: Code-Only-as-Policy for Recursive Self-Improvement in Robots with Stateful Code
Propose COAP, which uses stateful code to control a robot at test time without a model
arXiv
2610.12369
Published
Oct 9
Layer
Application layer
Setting
Embodied systems and robotics
Paper analysisRead analysis
DefensesarXiv 2610.11754Oct 8
仅凭输出审查无法验证什么：面向研究智能体的研究契约
提出研究合同，把已批准选择绑定到执行证据并由检查器核验
arXiv
2610.11754
Published
Oct 8
Layer
Application layer
Setting
science agent
Evaluated models
OpenAI gpt-5.6-sol, OpenAI gpt-5.6-terra, OpenAI gpt-5.6-luna and others18
- DefensesPermissions and approval
- RiskReward hacking
- ComponentPermissions and sandboxing
Abstract研究合同暴露四类输出单独建不立的关系。
研究合同用来核验计算科学研究智能体是否仍在已批准研究之内，不判断科学是否为真。。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.10310Oct 8
RSIGym：预算约束下的 AI 研究智能体评测环境与 RSI-Index 指标
提出 RSIGym 环境，评测研究智能体的递归自改进
arXiv
2610.10310
Published
Oct 8
Layer
Application layer
Setting
science agent
Evaluated models
Claude Opus 5, Claude Opus 5.5, GPT-6 Astra and others9
- RiskDangerous capabilities
Paper analysisRead analysis
Analysis and theoryarXiv 2610.09892Oct 7
AI 安全 Stackelberg 模型中的防御充分性研究
用 Stackelberg 博弈分析发现与修复投入的防御充分性
arXiv
2610.09892
Published
Oct 7
Layer
Application layer
Setting
Models and APIs
Abstract覆盖定理给出充分条件。
作者用攻击集合上的发现—修补过程，加上防守方领先的 Stackelberg 博弈，讨论反馈式防御何时够用，以及为威慑自适应攻击者该买多少能力。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.09082Oct 7
研究团队用 LLM 分析 570 万条 Reddit 帖，构建 AI 危害分类体系
用 LLM 流水线从社交媒体构建自下而上的 AI 伤害分类
arXiv
2610.09082
Published
Oct 7
Layer
Application layer
Setting
Models and APIs
Abstract自下而上的 Reddit 分类补上专家框架的细类，并用来追踪智能体、职场采用和伴侣停用相关叙事。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.08927Oct 7
Station 在开放式科研任务中平均复现 62.7% 的预设子发现
评测多智能体在开放式科研任务中找回预设子发现的能力
arXiv
2610.08927
Published
Oct 7
Layer
Application layer
Setting
science agent, Multi-agent systems
Evaluated models
Gemini 3.1 Pro, GPT-5.5, Claude Opus 4.8 and others7
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.06824Oct 6
TasteVal 基准：用算力效率衡量 AI 的实验研究品味
提出 TasteVal 基准，衡量模型设计实验并解释结果的研究品味
arXiv
2610.06824
Published
Oct 6
Layer
Application layer
Setting
science agent
Evaluated models
Opus 5.5, Fable 5.1, GPT-6 Astra and others20
- RiskDangerous capabilities
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.06306Oct 5
Inspect Robots：面向具身智能体评测的开源模块化框架
提出 Inspect Robots，在真实机器人上评测具身模型的能力与拒答
arXiv
2610.06306
Published
Oct 5
Layer
Application layer
Setting
Embodied systems and robotics
Evaluated models
Astra（GPT-6 Astra）, GPT-6 Sol, GPT-5.6 Sol and others7
- RiskInappropriate refusal
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.05140Oct 4
AutoSciBench：自动生成科学智能体评测基准的框架
提出 AutoSciBench，循环生成科学智能体评测任务
arXiv
2610.05140
Published
Oct 4
Layer
Application layer
Setting
science agent
Evaluated models
Claude Opus 5, Claude Opus 4.8, Claude Opus 4.7 and others10
AbstractAutoSciBench 用概念、配方和求解反馈自动构造并加难科学智能体基准。
AutoSciBench 研究科学智能体基准能否自动构造，并随 solver 行为迭代改编。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.04366Oct 3
CrashSim 用真实事故先验生成碰撞场景并构建 nuCrash 数据集
提出 CrashSim 用真实事故先验引导扩散仿真并构建 nuCrash 评测自动驾驶规划器
arXiv
2610.04366
Published
Oct 3
Layer
Application layer
Setting
Embodied systems and robotics
Evaluated models
Lane-graph planner, PDM-closed planner, IL planner and others5
AbstractCrashSim 迁入真实事故先验生成场景。
CrashSim 用检索到的真实事故先验引导扩散式多车仿真，为自动驾驶安全评测生成碰撞与近碰撞场景。。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.04184Oct 3
EvalResearchBench：AI 智能体自主设计评测基准
提出 EvalResearchBench，评测智能体自建评测对目标基准的排序一致性
arXiv
2610.04184
Published
Oct 3
Layer
Application layer
Setting
science agent
Evaluated models
GPT-6 Astra, GPT-5.6 Sol, GLM-5.3 and others9
AbstractERB 比较有预算的评测设计，分数复现与排序并不由同一设计同时领先。
ERB 用来测一件事：智能体在固定预算内做出的可执行评测，能否复现既有基准对候选模型的分数和排序。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.02874Oct 2
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
Oct 2
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。
Paper analysisRead analysis
OtherarXiv 2610.02662Oct 2
论文发现语言驱动机器人安全监控的细化缺口：安全高层计划可能产生不安全执行
审计机器人安全监控的细化缺口，比较表面计划与图细化轨迹的裁决
arXiv
2610.02662
Published
Oct 2
Layer
Application layer
Setting
Embodied systems and robotics
- ComponentMonitors
Abstract表面轨迹可漏掉图诱导的禁区进入。
作者审计 RoboGuard 的一个假设：对高层动作轨迹的安全裁决，在补上语义图诱导的导航和已记录隐式效应之后是否仍然成立。做法是不改验证器，并固定同一条 LTL。
Paper analysisRead analysis
OtherarXiv 2610.01716Oct 1
无架构师的架构？分裂世界中的全球 AI 治理
评述全球 AI 治理，认为问题是行动者权力与激励分歧
arXiv
2610.01716
Published
Oct 1
Layer
Application layer
Setting
Models and APIs
Abstract书评认为形成中的架构由多类行动者塑造，但没有共同计划。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.38948Sep 30
DrivingBench 发布：让视觉语言模型驾驶丰田 Corolla 绕桩
提出 DrivingBench，评测通用视觉语言模型能否闭环驾驶真车
arXiv
2609.38948
Published
Sep 30
Layer
Application layer
Setting
Embodied systems and robotics
Evaluated models
GPT-6 Astra, Claude Fable 5.1, Grok 4.6 and others4
- ComponentVision
Abstract仅 Astra 第二次跑完低速锥桶路线。
DrivingBench 评测通用 VLM 能否自己闭环开真车，推理延迟也算进任务。
Paper analysisRead analysis
DefensesarXiv 2609.35561Sep 29
RSI-Master：用结构化实验引导模型自主改进
提出 RSI-Master，约束自主后训练实验动作并结构化探索以防奖励作弊
arXiv
2609.35561
Published
Sep 29
Layer
Application layer
Setting
science agent, Multi-agent systems
- DefensesPermissions and approval
- RiskReward hacking
- ComponentPermissions and sandboxing
Paper analysisRead analysis

AI Safety Daily

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Attack type

Model risks

Defense type

Impact

System type

Attack surface / entry point

Layer

System component

A local agentic AI framework generates edge-case scenarios for safety assurance

EndoRSI: Recursive Self-Improvement of Endoscopic Robot Policies Driven by Physical Experiment Evidence

Toward Safe World Models: Risks, Defenses, and Evaluation

DRYLAB-Bench: Evaluating biosecurity risks of agentic LLM mutation design

Embodied Turing Machine: Code-Only-as-Policy for Recursive Self-Improvement in Robots with Stateful Code

仅凭输出审查无法验证什么：面向研究智能体的研究契约

RSIGym：预算约束下的 AI 研究智能体评测环境与 RSI-Index 指标

AI 安全 Stackelberg 模型中的防御充分性研究

研究团队用 LLM 分析 570 万条 Reddit 帖，构建 AI 危害分类体系

Station 在开放式科研任务中平均复现 62.7% 的预设子发现

TasteVal 基准：用算力效率衡量 AI 的实验研究品味

Inspect Robots：面向具身智能体评测的开源模块化框架

AutoSciBench：自动生成科学智能体评测基准的框架

CrashSim 用真实事故先验生成碰撞场景并构建 nuCrash 数据集

EvalResearchBench：AI 智能体自主设计评测基准

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

论文发现语言驱动机器人安全监控的细化缺口：安全高层计划可能产生不安全执行

无架构师的架构？分裂世界中的全球 AI 治理

DrivingBench 发布：让视觉语言模型驾驶丰田 Corolla 绕桩

RSI-Master：用结构化实验引导模型自主改进