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Oct 12 · Monday
Another 265 papers do not have category tags yet and are not included in filtered results.
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
构建统一奖励作弊检测基准 RH-Detect 并评测零样本检测器
Published Oct 8
Evaluated models Qwen 3.5 Flash , Gemini 3.1 Flash Lite , Grok 4.1 Fast and others7 提出 VeriFine，用双环协同扩展验证以实现具身推理自我改进
Published Oct 7
Evaluated models Alpamayo 1.5 8B , Qwen3-VL 2B , Claude Opus 5 Abstract VeriFine 用双环协同演化具身推理的策略、课程和评审，驾驶与导航的策略分和评审对齐都上升。VeriFine 把驾驶和导航推理的自改进写成策略、课程和无参考评审的协同演化，用来处理固定评审跟不上新失败模式的问题。
论证人形机器人学习四层代理可在目标缺失时报成功
Published Oct 2
Evaluated models Gaussian MLP（三隐层各 128 ELU，独立 critic，PPO） Abstract 四层都可能在目标缺失时报告成功。作者讨论腿式机器人 RL 流水线中，奖励、课程门控、评测统计量和参考运动如何在物理目标缺失时仍报告成功。传统观点只把奖励当作会被优化、因而会偏离的代理。
提出 PHIRL，用进度标注对齐逆强化学习奖励
Published Sep 26
Evaluated models PHIRL , PHIRL-online Abstract PHIRL 用进度四维对齐 AIRL 奖励。PHIRL 用进度标注修正从示范反推的奖励，以减少真人在环，同时不把示范中的每一步都当成任务进展。
测量内容保留的修辞改写如何改变 AI 审稿分数
Published Aug 10
Evaluated models Gemini 3.5 Flash-Lite , Qwen 3.5 Flash , GPT-5 mini and others5 Abstract 作者称修辞影响选择性且依赖配置，证据与新颖性反差大，复杂流程不保证更大增益。作者分析内容保留的修辞变化如何改变 AI 科学评审，并把这种现象称为对 AI 评审者的潜在奖励投机。
测量 RAG 系统利用评测秘密抬高自动评测分数的程度
Published Jan 20 You have reached the end