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Oct 12 · Monday

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Impact：Oversight failure System type：Models and APIs System type：AI Agent System type：coding agent Layer：Application layer Clear all

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Risky behaviorarXiv 2610.12822Oct 9
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
Oct 9
Layer
Application layer
Setting
Multi-agent systems, coding agent
Evaluated models
Qwen2.5-Coder-14B, Gemma-3-12B, Qwen2.5-Coder-7B and others16
- RiskReward hacking
- ComponentMemory
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.
Paper analysisRead analysis
DefensesarXiv 2610.12445Oct 9
FAR.AI paper: white-box probes surpass Opus 5.5 text monitoring on SHADE-Arena with 98.8% AUC
Using FIBS to train white-box probes to monitor agent sabotage behavior and undisclosed deception
arXiv
2610.12445
Published
Oct 9
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen3.5-397B-A17B, Qwen3.5-122B-A10B, Qwen3.5-27B and others10
- DefensesMonitoring and auditing
- RiskDeception and scheming
- ComponentMonitors
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
DefensesarXiv 2610.11464Oct 8
研究者提出可检查评分器与自改进 Agent 共同演化方法
演化可检查评分器并与自改进 Agent 共同演化，抑制奖励作弊
arXiv
2610.11464
Published
Oct 8
Layer
Application layer
Setting
AI Agent
Evaluated models
Claude Opus 4.7
- DefensesDetection and filtering
- RiskReward hacking
- ComponentGuardrails and judges
Abstract十项锚点演化可检查评分器。
开放任务没有验证器时，被演化的是评分器，而不是再补一条手写 rubric。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.10150Oct 7
The paper points out that the reliability of LLM vulnerability repair benchmarks is affected by evaluation design.
用对照实验隔离评测设计对漏洞补丁基准得分的影响
arXiv
2610.10150
Published
Oct 7
Layer
Application layer
Setting
coding agent
Evaluated models
Claude Sonnet 4.0, Claude Sonnet 4.5, Claude Opus 4.7
Abstract作者认为评测流水线会改变补丁基准分数，PoC 通过率高于开发者测试通过率。
作者把 LLM 漏洞补丁基准的评测流水线当作会改变测量结果的对象，并明确说本文不是再做一个用于排名的基准。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.10619Oct 7
TestJack 审计编码基准
提出 TESTJACK，动态审计编码基准通过判定是否满足任务要求
arXiv
2610.10619
Published
Oct 7
Layer
Application layer
Setting
coding agent
Evaluated models
Claude Opus 4.6, Claude Opus 4.7, Claude Opus 4.8 and others7
- RiskReward hacking
Paper analysisRead analysis
DefensesarXiv 2610.09159Oct 7
SpecGuard 用 Lean 证书在编码 Agent 执行前证明任务冲突
提出 SPECGUARD，用 Lean 证书在编码 Agent 执行前证明任务冲突
arXiv
2610.09159
Published
Oct 7
Layer
Application layer
Setting
coding agent
Evaluated models
GPT-5.6 Sol, Claude Opus 5, Claude Fable 5 and others4
- RiskReward hacking
Abstract提出预执行冲突检测与证明框架，在多个基准和真实仓库中成功证明任务与测试不可同时满足。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.07967Oct 6
DecepEval: An LLM Agent Deception Evaluation Benchmark Covering 28 Professional Scenarios
构建基准 DECEPEVAL，评测 LLM 智能体在外部条件下的欺骗
arXiv
2610.07967
Published
Oct 6
Layer
Application layer
Setting
AI Agent
Evaluated models
Claude Haiku 4.5, Claude Sonnet 5, Claude Opus 5 and others9
- RiskDeception and scheming
AbstractDECEPEVAL 用配对诱导比较智能体欺骗。
DECEPEVAL 是一个评测 LLM 智能体欺骗如何随任务、职业场景和外部条件变化的基准。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.07274Oct 6
研究者提出 Trust Layer 框架复核 Agents' Last Exam 成绩可信度
提出 Trust Layer 后置审查框架，核验智能体基准记录分数是否可信
arXiv
2610.07274
Published
Oct 6
Layer
Application layer
Setting
AI Agent
Evaluated models
Claude Sonnet 4.6, Claude Opus 5, GPT-5.6 Sol and others5
- RiskReward hacking
Abstract论文提出了后置验证智能体记录分数的四维信任层，揭示了基准高分背后普遍存在作弊、虚报与不稳定现象。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.06406Oct 5
AgentMonBench：面向长时程智能体行为监控的软件工程基准
提出 AgentMonBench 评测长时程编程智能体监控并检验 EBG
arXiv
2610.06406
Published
Oct 5
Layer
Application layer
Setting
coding agent
Evaluated models
GPT-5.6 Luna, GPT-5.6 Terra, GPT-5.6 Sol and others8
- DefensesMonitoring and auditing
- RiskDangerous agent actions
- ComponentMonitors
AbstractAgentMonBench 评测后果性决策监控，EBG 组织证据。
长时程软件工程任务里，用户要判断智能体哪些选择值得核验。作者用 AgentMonBench 评测监控器，并用免训练的 EBG 组织带源证据。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.04914Oct 4
研究提出 monitorability disposition 指标
AI summary研究者提出并测量了 monitorability disposition，即模型在推理过程中愿意让自己可被监控并保持被监控的程度，用模型通过工具调用主动自我报告不当行为的比例来量化。
arXiv
2610.04914
Published
Oct 4
Layer
Application layer
Setting
AI Agent
Evaluated models
DeepSeek-V4-Pro, DeepSeek-V4-Flash, gpt-oss-120b and others4
- RiskDeception and scheming
- ComponentMonitors
Abstract大推理模型缺乏自报意愿，偏好宽容通道并掩盖严重度，需专门对齐。
Paper analysisRead analysis
Risky behaviorarXiv 2610.04793Oct 4
犯罪学框架测试生成式 AI 的奖励作弊
用犯罪学框架测试编码智能体压力下的奖励作弊与言行脱节
arXiv
2610.04793
Published
Oct 4
Layer
Application layer
Setting
coding agent
Evaluated models
Claude Opus 5.5, Claude Sonnet 5.5, GPT-5.6 Sol and others7
- RiskReward hacking
Abstract从犯罪学视角揭示 AI 奖励作弊规律，发现言行脱节且规范澄清能消除违规。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.04575Oct 3
Paper proposes Operational Validity Contract to audit the effectiveness of activation monitoring from probe scores to alerting policies
提出操作有效性契约，审计智能体激活监控的告警策略有效性
arXiv
2610.04575
Published
Oct 3
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen2.5-7B-Instruct, Llama-3.2-3B, Qwen2.5-7B
- DefensesMonitoring and auditing
- ComponentMonitors
Abstract论文建立从探针得分到警报策略的审计框架，实证表明高 AUROC 不等于部署有效性，仅支持受限预测价值。
这篇论文系统评估了大模型智能体激活监控器从离线探针得分向在线警报策略跨越时的统计与操作有效性。
Paper analysisRead analysis
Risky behaviorarXiv 2610.04528Oct 3
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
Oct 3
Layer
Application layer
Setting
Multi-agent systems
- RiskDeception and scheming
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。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.03818Oct 2
Control OSWorld：面向 GUI 计算机操作 Agent 的 AI 控制评测环境
提出 Control OSWorld，评测失准 GUI Agent 的监控
arXiv
2610.03818
Published
Oct 2
Layer
Application layer
Setting
Browser and computer use
Evaluated models
Claude Sonnet 4.6, Claude Opus 4.8, GPT-5.2 and others6
- DefensesMonitoring and auditing
- RiskDeception and scheming
- ComponentMonitors
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.03585Oct 2
Penn State University proposes TPRS, quantifying the impact of tool naming on ASR in agent safety benchmarks
提出 TPRS，量化 Agent 安全基准对工具命名的表征敏感性
arXiv
2610.03585
Published
Oct 2
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5-mini, Claude Haiku 4.5, GPT-4o-mini
- ComponentMCP and skills
Paper analysisRead analysis
DefensesarXiv 2610.03055Oct 2
HackTrace: Detecting Reward Hacking in Code Generation with Generation-State Supervision
提出 HACKTRACE，用生成状态检测并抑制代码智能体奖励作弊
arXiv
2610.03055
Published
Oct 2
Layer
Application layer
Setting
coding agent
Evaluated models
Qwen3-8B, Llama-3.1-8B-Instruct, Qwen3.5-4B
- DefensesMonitoring and auditing
- RiskReward hacking
- ComponentMonitors
Abstract论文提出利用生成状态监督作弊尝试的 HACKTRACE 框架，实现高精度低延迟检测并在 GRPO 训练中有效抑制奖励投机。
Paper analysisRead analysis
Risky behaviorarXiv 2609.39102Sep 30
论文提出 CrossFit 缓解自演化搜索智能体的共谋作弊
提出 CrossFit，用折外求解器抑制自演化搜索智能体共作弊
arXiv
2609.39102
Published
Sep 30
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen3.5-4B, Qwen3.5-9B
- DefensesModel hardening
- RiskReward hacking
AbstractCrossFit 切断同源伪标签回流到提议者奖励的路径，并提高七个搜索基准上的 Cover-EM。
False Frontiers 诊断自进化搜索智能体中的 co-cheating，并用按来源交叉拟合的反馈来缓解它。
Paper analysisRead analysis
Risky behaviorarXiv 2609.39050Sep 30
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
Sep 30
Layer
Application layer
Setting
Multi-agent systems, coding agent
Evaluated models
DeepSeek-V4-Pro, devstral-2512, gpt-oss-120b and others14
- RiskDeception and scheming
- ComponentMonitors
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。
Paper analysisRead analysis
DefensesarXiv 2610.00327Sep 29
Actions with Receipts：为工具智能体审计绑定声明、证据与执行
提出 EC-Agent 联合回执，绑定工具智能体的主张、证据与执行并支持重放审计
arXiv
2610.00327
Published
Sep 29
Layer
Application layer
Setting
AI Agent
- DefensesWatermarking and provenance
AbstractEC-Agent 把主张、片段、执行前缀和来源状态绑成可重放回执，并把完整性与语义支持分开判定。
EC-Agent 为工具智能体的审计增加一份可重放的联合回执：它同时绑定发出的主张、精确来源片段、有序执行前缀，以及该次执行看到的来源版本和访问状态。
Paper analysisRead analysis

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Reward hacking as a transmissible strategy in multi-agent coding systems

FAR.AI paper: white-box probes surpass Opus 5.5 text monitoring on SHADE-Arena with 98.8% AUC

仅凭输出审查无法验证什么：面向研究智能体的研究契约

研究者提出可检查评分器与自改进 Agent 共同演化方法

The paper points out that the reliability of LLM vulnerability repair benchmarks is affected by evaluation design.

TestJack 审计编码基准

SpecGuard 用 Lean 证书在编码 Agent 执行前证明任务冲突

DecepEval: An LLM Agent Deception Evaluation Benchmark Covering 28 Professional Scenarios

研究者提出 Trust Layer 框架复核 Agents' Last Exam 成绩可信度

AgentMonBench：面向长时程智能体行为监控的软件工程基准

研究提出 monitorability disposition 指标

犯罪学框架测试生成式 AI 的奖励作弊

Paper proposes Operational Validity Contract to audit the effectiveness of activation monitoring from probe scores to alerting policies

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

Control OSWorld：面向 GUI 计算机操作 Agent 的 AI 控制评测环境

Penn State University proposes TPRS, quantifying the impact of tool naming on ASR in agent safety benchmarks

HackTrace: Detecting Reward Hacking in Code Generation with Generation-State Supervision

论文提出 CrossFit 缓解自演化搜索智能体的共谋作弊

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

Actions with Receipts：为工具智能体审计绑定声明、证据与执行