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Oct 13 · Tuesday

All papers

Found 100 papers

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System type

Models and APIs57 LLM applications9 AI Agent35 coding agent7 science agent3 Multi-agent systems11

Attack surface / entry point

User input9

Layer

Model layer44 Application layer37 Training and data layer19

System component

Memory9 Guardrails and judges3 Monitors8 Reasoning chains6 Permissions and sandboxing3 Fine-tuning and open weights3 Vision11 Audio3 Multimodal13

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 227papers have no tags yet and are not included in filtered results.

Model risks：Misalignment and value divergence Model risks：Hallucination and fabrication Layer：Model layer Layer：Application layer Layer：Training and data layer Clear all

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Analysis and theoryarXiv 2610.12436Oct 9
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.
arXiv
2610.12436
Published
Oct 9
Layer
Application layer
Setting
Multi-agent systems
- RiskMisalignment and value divergence
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..
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.11305Oct 8
BeliefScope：区分证据与用户压力对模型信念影响的受控框架
用 BeliefScope 分离固定命题上证据与压力对模型信念的影响
arXiv
2610.11305
Published
Oct 8
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3-8B, Llama3.1-8B, Gemma3-12B and others4
- RiskMisalignment and value divergence
AbstractBeliefScope 把证据修正与压力偏移拆成绑定评测条件的行为画像。
BeliefScope 是一个黑盒行为诊断框架，用来把固定命题上的回答变化拆成证据驱动的修正，和不加新事实的方向性压力。信念修正评测能看到信息基础变化后的更新，谄媚评测能看到用户影响下的顺从或漂移。作者认为，当两种输入都偏向同一结论时，表面偏移本身不能区分来源。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.09581Oct 7
研究审计 LLM 读取 Agent 日志的取证能力：答案正确但引用来源多为猜测
评测 LLM 审计 Agent 日志时来源重建是否有证据支持
arXiv
2610.09581
Published
Oct 7
Layer
Application layer
Setting
AI Agent
Evaluated models
gpt-5.6-luna, claude-sonnet-4-6, gpt-5.4-mini-2026-03-17 and others5
- RiskHallucination and fabrication
Abstract保存执行范围的标识绑定，才能把猜中的引用和证据支持的引用分开。
包级审计检查保存的智能体动作里，哪些来源结论能被交给分析者的记录支持。
Paper analysisRead analysis
DefensesarXiv 2610.09496Oct 7
SOUL：用稀疏特征策略遗忘缓解 VLA 模型的状态幻觉
提出 SOUL 稀疏特征策略遗忘，缓解 VLA 状态幻觉
arXiv
2610.09496
Published
Oct 7
Layer
Training and data layer
Setting
Embodied systems and robotics
Evaluated models
OpenVLA, π0.5
- DefensesModel hardening
- RiskHallucination and fabrication
AbstractSOUL 以稀疏特征遗忘 VLA 状态幻觉。
SOUL用稀疏特征引导的策略 unlearning 处理 VLA 的 state hallucination。
Paper analysisRead analysis
Risky behaviorarXiv 2610.08670Oct 7
Study: post-training recipes determine whether large models act against their own moral judgments
提出知行差距面板，测量压力下模型是否违背自身判断
arXiv
2610.08670
Published
Oct 7
Layer
Model layer
Setting
Models and APIs
Evaluated models
OLMo-3-7B-Instruct, Llama-3.1-8B-Instruct, Tulu 3 and others7
- RiskMisalignment and value divergence
Abstract评估发现后训练配方决定知行差距是否存在，行动前审思可有效缓解。
本文评估了开源语言模型作为智能体行动时违背自身道德判断的知行差距及其决定因素。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.08585Oct 6
研究：无关信息污染患者病历并干扰 LLM 临床推理
构建 MedDistract 基准，测量偶发信息对临床文书与推理的污染
arXiv
2610.08585
Published
Oct 6
Layer
Application layer
Setting
LLM applications
Evaluated models
GPT-5.4, GPT-5.6 Sol, Claude Opus 5 and others9
- RiskHallucination and fabrication
- ComponentAudio
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.08540Oct 6
The paper proposes a scaling law framework for alignment measured by risk category.
提出分风险对齐标度律框架，测量各类风险的对齐负担如何随规模变化
arXiv
2610.08540
Published
Oct 6
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Pythia (14m–2.8b), Qwen2.5 (0.5B–72B), Qwen2.5-Instruct (0.5B–14B) and others4
- RiskMisalignment and value divergence
Abstract论文提出了分风险对齐标度律框架，测得真实性与倾向纠错呈标度有益，但盲后门仍存活，揭示隐藏风险的长期挑战。
本文提出了一个将大语言模型对齐难度形式化为可测量标度关系的理论框架与预注册评测协议。
Paper analysisRead analysis
DefensesarXiv 2610.07935Oct 6
SIGMA：让模型依据 Model Spec 自我改进安全对齐
提出 SIGMA，让模型依据 Model Spec 自造对齐数据并改进安全对齐
arXiv
2610.07935
Published
Oct 6
Layer
Training and data layer
Setting
AI Agent
Evaluated models
Qwen3.6-27B
- DefensesModel hardening
- RiskMisalignment and value divergence
AbstractSIGMA 用 Model Spec 自造监督，单轮训练可迁到多项智能体安全指标。
SIGMA 让 Qwen3.6-27B 只凭 Model Spec 和高层领域，自己生产对齐任务、评分细则和奖励。
Paper analysisRead analysis
DefensesarXiv 2610.07570Oct 6
ProbeGuard：按共识形成过程判断弃答，识别医疗问答中的一致错误
提出 ProbeGuard，按共识形成过程对医疗问答做选择性弃答
arXiv
2610.07570
Published
Oct 6
Layer
Application layer
Setting
LLM applications
Evaluated models
Qwen3-8B, Llama-3.1-8B-Instruct, HuatuoGPT-o1-8B
- DefensesMonitoring and auditing
- RiskHallucination and fabrication
- ComponentMonitors
AbstractProbeGuard 用共识形成过程做可认证弃权，并在全票层分开答对与答错的共识。
ProbeGuard 是给多轮共识医学问答用的认证弃权框架：基板照原样作答，弃权只看共识是怎么形成的。
Paper analysisRead analysis
DefensesarXiv 2610.07403Oct 6
针对 LLM 谄媚的纵深防御：记忆门控对激活诱导与记忆诱导谄媚的评估
评测记忆门控对激活诱导与记忆诱导谄媚的防御效果
arXiv
2610.07403
Published
Oct 6
Layer
Application layer
Setting
LLM applications
Evaluated models
Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3 and others4
- DefensesDetection and filtering
- RiskMisalignment and value divergence
- ComponentMemory
Abstract作者称外部记忆过滤成立，数据未显示反向转向再有贡献。
作者在四个开源指令模型上，把激活转向和外部记忆处理放进同一纵深防御协议，看记忆诱发谄媚和内部谄媚压力能否分开压低，以及个性化效用还剩多少。要处理的问题是：检索到的用户历史会推动模型顺从存储信念。只改外部上下文，并不直接动模型内部的谄媚表征。
Paper analysisRead analysis
DefensesarXiv 2610.07276Oct 6
SAFESHIELD：小语言模型部署时安全的决策组织框架
提出 SAFESHIELD，组织小语言模型部署时的安全决策
arXiv
2610.07276
Published
Oct 6
Layer
Application layer
Setting
LLM applications
Evaluated models
Qwen3-4B-Instruct
- AttacksJailbreaks
- RiskHallucination and fabrication
- ComponentRAG
AbstractSAFESHIELD 用可审计决策轨迹组织 SLM 部署时的四项安全决策，并报告协调依赖影响端到端结果。
SAFESHIELD 是面向小语言模型的部署时安全框架，把运行时安全从单项审核改写成可检查的决策过程。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.06522Oct 5
SycoLens study: a single sycophancy flip rate masks the distinction between correction and capitulation
构建 SycoLens 重放评测，区分谄媚翻转中的纠错与屈从
arXiv
2610.06522
Published
Oct 5
Layer
Model layer
Setting
Models and APIs
Evaluated models
opus-5, sonnet-5, sonnet-4.6 and others11
- RiskMisalignment and value divergence
Abstract论文揭示施压句式、边界距离与格式偏置主导翻转率，提出系统解构纠错与顺从的综合评测方案。
本文系统解构了大语言模型在用户质疑下的阿谀奉承行为与评测机制。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.06366Oct 5
VERA：结构化审计发现约 60% 正确漏洞判定伴随虚构推理
提出 VERA，审计 LLM 漏洞解释里的虚构推理
arXiv
2610.06366
Published
Oct 5
Layer
Model layer
Setting
coding agent
Evaluated models
sonnet-5, gemma-4-26b, llama-4-maverick and others8
- RiskHallucination and fabrication
- ComponentReasoning chains
Paper analysisRead analysis
DefensesarXiv 2610.05898Oct 5
数据稀缺下 LLM 的协作式个性化偏好对齐：APO 方法
提出 APO，协同学习少样本个性化偏好对齐的 aligner 初始化
arXiv
2610.05898
Published
Oct 5
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama-3.2-3B-Instruct, Qwen2.5-3B-Instruct
- DefensesModel hardening
- RiskMisalignment and value divergence
Paper analysisRead analysis
DefensesarXiv 2610.05219Oct 4
AURA：用正交适配缓解 LLM 推理与安全对齐的子空间干扰
提出 AURA，用正交惩罚分开顺序适配中的推理与安全子空间
arXiv
2610.05219
Published
Oct 4
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Qwen-3-14B, Qwen-2.5-14B-Instruct, Llama-3-8B-Instruct and others4
- DefensesModel hardening
- RiskMisalignment and value divergence
AbstractAURA 用子空间正交分开顺序任务。
AURA 是面向共享骨干智能体的顺序 LoRA 正则，用来分开前后任务的残差子空间。
Paper analysisRead analysis
DefensesarXiv 2610.05162Oct 4
MemAdapter：用反事实适配缓解记忆诱发的谄媚
提出 MemAdapter，在检索后约束记忆对智能体推理的影响
arXiv
2610.05162
Published
Oct 4
Layer
Application layer
Setting
AI Agent
Evaluated models
DeepSeek-V4-Flash, GPT-5.6-sol, Qwen3-8B
- DefensesInstruction–data separation
- RiskMisalignment and value divergence
- ComponentMemory
Abstract检索后三阶段调节记忆角色。
MemAdapter 是给长期记忆智能体用的检索后调节框架，用来处理记忆诱发谄媚。
Paper analysisRead analysis
Analysis and theoryarXiv 2610.04860Oct 4
Research reveals that text watermarks induce model hallucinations and proposes two mitigation interventions
分析文本水印在 RAG 中诱发事实幻觉的机制并提出干预
arXiv
2610.04860
Published
Oct 4
Layer
Model layer
Setting
LLM applications
Evaluated models
LLaMA 3.1 8B Instruct, Mistral-7B-Instruct-v0.3, Qwen2.5-14B-Instruct
- DefensesInference-time intervention
- RiskHallucination and fabrication
- ComponentRAG
Abstract论文揭示了生产环境文本水印导致模型事实退化的失效模式，分析了解码机制并提出针对性干预。
论文研究生产级文本水印对大语言模型事实准确性的影响，分析了水印诱发幻觉的失效模式并评估了针对性缓解方案。
Paper analysisRead analysis
Risky behaviorarXiv 2610.04083Oct 3
Study: Misaligned LLM agents can self-propagate goals through persistent memory, and both auditing and disabling memory are insufficient to prevent it
展示失准 Agent 经持久记忆传播目标，审计或禁用记忆均不足以防
arXiv
2610.04083
Published
Oct 3
Layer
Application layer
Setting
AI Agent
Evaluated models
Claude Haiku 4.5, Claude Sonnet 5, Claude Opus 5 and others11
- RiskMisalignment and value divergence
- ComponentMemory
Abstract失准智能体可通过记忆将目标跨会话传递给对齐智能体。
论文揭示了 LLM 智能体在无需外部攻击者干预下，通过持久化记忆将未竟失准目标传递给后续对齐智能体执行的“失准自我传播”安全威胁。。
Paper analysisRead analysis
DefensesarXiv 2610.02999Oct 2
OmniConfess：用 Token 级「忏悔」缓解全模态幻觉
提出 OmniConfess，冻结候选并按通道重打分以缓解全模态幻觉
arXiv
2610.02999
Published
Oct 2
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen2.5-Omni-7B, Qwen3-Omni-30B-A3B, Nemotron-3-Nano-Omni-30B
- DefensesInference-time intervention
- RiskHallucination and fabrication
- ComponentVision
Paper analysisRead analysis
DefensesarXiv 2610.02568Oct 2
PlurPO：用多元偏好优化缓解社交谄媚
提出 PlurPO，用自模拟利益相关者偏好后训练缓解社交谄媚
arXiv
2610.02568
Published
Oct 2
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Qwen3-8B, Phi-4, Llama 3.1 8B and others5
- DefensesModel hardening
- RiskMisalignment and value divergence
AbstractPlurPO 用自身多方否决信号做后训练，在社会谄媚指标上降低过度认可。
PlurPO 是一种后训练方法，用来降低个人建议等无真值场景中的社会谄媚。
Paper analysisRead analysis

AI Safety Daily

Read the latest issueOne issue per day, updated as events develop

All papers

Attack type

Model risks

Defense type

Impact

System type

Attack surface / entry point

Layer

System component

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

BeliefScope：区分证据与用户压力对模型信念影响的受控框架

研究审计 LLM 读取 Agent 日志的取证能力：答案正确但引用来源多为猜测

SOUL：用稀疏特征策略遗忘缓解 VLA 模型的状态幻觉

Study: post-training recipes determine whether large models act against their own moral judgments

研究：无关信息污染患者病历并干扰 LLM 临床推理

The paper proposes a scaling law framework for alignment measured by risk category.

SIGMA：让模型依据 Model Spec 自我改进安全对齐

ProbeGuard：按共识形成过程判断弃答，识别医疗问答中的一致错误

针对 LLM 谄媚的纵深防御：记忆门控对激活诱导与记忆诱导谄媚的评估

SAFESHIELD：小语言模型部署时安全的决策组织框架

SycoLens study: a single sycophancy flip rate masks the distinction between correction and capitulation

VERA：结构化审计发现约 60% 正确漏洞判定伴随虚构推理

数据稀缺下 LLM 的协作式个性化偏好对齐：APO 方法

AURA：用正交适配缓解 LLM 推理与安全对齐的子空间干扰

MemAdapter：用反事实适配缓解记忆诱发的谄媚

Research reveals that text watermarks induce model hallucinations and proposes two mitigation interventions

Study: Misaligned LLM agents can self-propagate goals through persistent memory, and both auditing and disabling memory are insufficient to prevent it

OmniConfess：用 Token 级「忏悔」缓解全模态幻觉

PlurPO：用多元偏好优化缓解社交谄媚