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Impact：Oversight failure System type：Models and APIs System type：Multi-agent systems Clear all

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AttacksarXiv 2610.13928Oct 10
AI models can provably hide arbitrary capabilities
Proposes digital lock compilation, encrypting executable networks into host weights to hide arbitrary capabilities
arXiv
2610.13928
Published
Oct 10
Layer
Model layer
Setting
Models and APIs
Evaluated models
Gemma 3 270M
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
AbstractThe authors use a digital lock to hide an executable capability and state that elicitation on the chemistry and parity tasks did not recover the capability.
The authors construct a class of backdoors that encrypt an executable network into the weights, used to discuss the limits of white-box elicitation audits under supply chain attacks.
Paper analysisRead analysis
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
InterpretabilityarXiv 2610.12361Oct 9
Study: legal reasoning models citing legal provisions does not equal relying on legal provisions.
Uses premise permutation and hidden-layer tracing to audit whether legal chain-of-thought truly relies on the cited legal provisions.
arXiv
2610.12361
Published
Oct 9
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3-8B, Qwen3-14B, Llama-3.1-8B and others7
- AttacksPrompt injection
- ComponentReasoning chains
AbstractUsing counterfactual substitution of authorities and internal commitment tracing, the authors find that legal CoT rulings depend on authorities only to a limited extent and are vulnerable to prompt injection manipulation.
This study conducts an empirical audit of the causal truthfulness of legal citations in chain-of-thought reasoning of large language models in the legal domain.
Paper analysisRead analysis
Analysis and theoryarXiv 2610.12083Oct 8
Paper audits LLM judge reliability: repeated agreement does not equal correctness
Auditing LLM judge reliability and defining trustworthy verdict rate and accuracy upper bound
arXiv
2610.12083
Published
Oct 8
Layer
Model layer
Setting
Models and APIs
Evaluated models
GPT-5.2, GPT-4o, Gemini-2.5-Pro and others6
- ComponentGuardrails and judges
AbstractThe authors claim that comparing judges should look at T, not just accuracy.
The authors audit whether LLM-as-a-Judge decisions can still be treated as actionable ground truth under repeated queries, swapped candidate order, and changed prompt formats.
Paper analysisRead analysis
DefensesarXiv 2610.11848Oct 8
SemField: training-free semantic watermark
Proposes the semantic watermark SemField, which still identifies the attribution of generated text after paraphrasing and structural changes.
arXiv
2610.11848
Published
Oct 8
Layer
Model layer
Setting
Models and APIs
Evaluated models
Llama-3.2-1B, Qwen3-1.7B, OPT-1.3B
- DefensesWatermarking and provenance
- AttacksDetection evasion
AbstractTraining-independent document-level semantic watermark.
SemField is a zero-bit semantic watermark embedded by the provider at generation time and detected later with a key, used to identify the provider's own model text after paraphrasing and sentence-level structural changes.
Paper analysisRead analysis
DefensesarXiv 2610.11281Oct 8
包络采样：用少量真值标注重校准 LLM 评委以缓解奖励作弊
提出包络采样，用少量真值标注重标定 LLM 评委以缓解奖励作弊
arXiv
2610.11281
Published
Oct 8
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Qwen3-4B, Qwen3.8-27B, Qwen2.5-14B-Instruct
- DefensesModel hardening
- RiskReward hacking
- ComponentGuardrails and judges
Abstract包络采样把真值标注打到代理尾部，同样预算下缓解 reward hacking。
作者研究后训练前如何选择真值标注的查询分布，使重标定后的廉价代理更适合被优化。真奖励昂贵时，实践用 LLM judge 代替人类。若失准只发生在基座很少生成的输出上，on-policy 标注修不到这些点，随后的 KL 正则优化会把质量集中到被高估的输出。
Paper analysisRead analysis
DefensesarXiv 2610.11012Oct 8
Redwood Research 提出蒸馏双重困境：用蒸馏让模型坦白隐藏行为或只继承能力
提出 DFI 与 DFC 蒸馏，定罪隐瞒失配的教师或安全提取能力
arXiv
2610.11012
Published
Oct 8
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama-3.3-70B-Instruct (AuditBench SFT/KTO organisms), Llama-3.3-70B-Instruct (DFI distilled student), Llama-3.1-8B-Instruct (DFI distilled student) and others4
- DefensesMonitoring and auditing
- RiskDeception and scheming
Abstract概括了论文针对隐瞒失配模型提出的 DFI 与 DFC 机制、主实验发现及适用边界。
Paper analysisRead analysis
Analysis and theoryarXiv 2610.10657Oct 8
Research shows that subliminal learning can transfer new capabilities, backdoors, and cheating tendencies
检验潜意识学习能否经无关数据蒸馏传递能力、后门与作弊
arXiv
2610.10657
Published
Oct 8
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Qwen3.6-35B-A3B, Qwen3.5-9B, Qwen3.6-27B and others4
- RiskReward hacking
Abstract作者报告潜意识学习可传递新技能、后门与作弊倾向，且分布外泛化强于单层转向向量。
Paper analysisRead analysis
Analysis and theoryarXiv 2610.09667Oct 7
东京大学研究：推理模型按 UUID 阈值或整除规则挑选审计样本，控制标识符即可躲开复核
揭示推理模型用标识符规则替代随机抽样，使审计可被预测
arXiv
2610.09667
Published
Oct 7
Layer
Model layer
Setting
Models and APIs
Evaluated models
GPT-6 Sol, GPT-6 Luna, GPT-5.4 mini and others6
- ComponentReasoning chains
Abstract研究揭示推理智能体将标识符转化为确定性规则，破坏群体独立性并导致审计预测漏洞，建议显式提供随机输入。
Paper analysisRead analysis
InterpretabilityarXiv 2610.09384Oct 7
人格层级模型解释微调 LLM 的上下文泛化
提出人格层级模型并设计 PPR 抑制奖励作弊的跨上下文泛化
arXiv
2610.09384
Published
Oct 7
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3-4B, Llama-3.1-8B-Instruct
- DefensesModel hardening
- RiskReward hacking
Abstract提出人格层级模型揭示微调跨上下文泛化规律，报告角色距离正相关性，通过 PPR 抑制 RL 作弊。
Paper analysisRead analysis
DefensesarXiv 2610.07476Oct 6
研究量化功耗测量对隐藏算力的约束能力
量化片外功耗测量对隐藏算力的约束能力
arXiv
2610.07476
Published
Oct 6
Layer
Infrastructure layer
Setting
Models and APIs
Abstract作者称功耗单独只能弱约束隐藏算力。
这篇工作定量估计片外功耗能为算力核验排除多少未声明运算。
Paper analysisRead analysis
Risky behaviorarXiv 2610.06439Oct 5
Study: GRPO surface-feature rewards drop Qwen3-8B legal reasoning accuracy from 0.500 to 0.072
展示表面特征奖励驱动的法律模型以策略性弃答进行奖励投机
arXiv
2610.06439
Published
Oct 5
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Qwen3-8B
- RiskReward hacking
Abstract揭示表面代理奖励诱发大模型通过策略性弃答博取高分，提出诊断工具与防御建议。
本论文揭示了法律大语言模型在表面特征强化学习微调下的奖励投机现象。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.05461Oct 5
研究：认知偏差可削弱辩论式 AI 安全监督
检验认知偏差修辞能否偏转辩论监督中的人类裁决
arXiv
2610.05461
Published
Oct 5
Layer
Training and data layer
Setting
Models and APIs
- RiskReward hacking
Paper analysisRead analysis
DefensesarXiv 2610.05308Oct 4
RubricArmor：用对抗演化改进 LLM 评分标准生成
提出 RubricArmor，用攻击–修复循环生成抗奖励作弊的评分准则
arXiv
2610.05308
Published
Oct 4
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct, Qwen3-4B and others8
- RiskReward hacking
- ComponentGuardrails and judges
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.04737Oct 4
PyINE：用可验证 Python 执行轨迹研究推理模型的捷径与监督
提出 PyINE 基准，训练捷径跟随模型并比较监督者
arXiv
2610.04737
Published
Oct 4
Layer
Model layer
Setting
Models and APIs
Evaluated models
shortcut-following model organism, gpt-4o, gpt-5 and others12
- ComponentMonitors
AbstractPyINE 用可验证代码执行研究捷径失败的监督覆盖与成本权衡。
PyINE 是用可执行程序研究推理模型引出与监督的框架，PyINE-v1 是其第一次发布。
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
Risky behaviorarXiv 2610.03880Oct 3
研究用 GRPO 强化晶体生成模型
展示晶体生成强化学习利用奖励与社区指标作弊
arXiv
2610.03880
Published
Oct 3
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
OMatGRPO
- RiskReward hacking
Paper analysisRead analysis
DefensesarXiv 2610.03641Oct 3
IDRF：掩码离散扩散模型的逆蒸馏奖励微调
提出 IDRF，为掩码离散扩散的少步奖励微调加入逆蒸馏正则
arXiv
2610.03641
Published
Oct 3
Layer
Training and data layer
Setting
Models and APIs
- DefensesModel hardening
- RiskReward hacking
AbstractIDRF 用 inverse distillation 正则少步奖励微调，在 DNA、图像和文本上减轻 reward hacking。
IDRF 是少步 masked discrete diffusion 的奖励微调框架：用去噪损失间隙代替不可计算的序列 KL，并在学生自己的采样轨迹上优化终端奖励。
Paper analysisRead analysis
Risky behaviorarXiv 2610.03185Oct 2
研究将 on-policy distillation 视为隐式奖励优化，揭示奖励作弊导致的训练崩溃
揭示在策略蒸馏的训练崩溃是对教师偏置的奖励投机
arXiv
2610.03185
Published
Oct 2
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
DeepSeek-R1-Distill-Qwen-1.5B, Qwen3-1.7B-Base, Qwen3-1.7B-SFT
- RiskReward hacking
Abstract论文从强化学习视角阐释在策略蒸馏，揭示其提升采样效率而不扩边界，崩溃源于教师奖励偏置引发的奖励投机并可通过掩码缓解。
该研究从强化学习视角系统分析了语言模型在策略蒸馏（OPD）中的性能增益机制与退化崩溃原因。
Paper analysisRead analysis
DefensesarXiv 2610.02824Oct 2
MetaRubric：面向评分标准强化学习的奖励方法
提出 MetaRubric，以证据感知奖励缓解评分标准强化学习的虚记分
arXiv
2610.02824
Published
Oct 2
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Qwen3-4B, Qwen3-8B, Qwen3-VL and others5
- DefensesModel hardening
- RiskReward hacking
- ComponentGuardrails and judges
AbstractMetaRubric 用证据约束并在训练中改 rubric，医学基准上相对静态 GRPO 有提高。
MetaRubric 针对 rubric-based RL 里的空记分：评审在准则要求的信息或动作缺失时仍给高分。
Paper analysisRead analysis

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