元学习攻克多语言LLM对齐难题，让偏好学习适应不同语言文化
arXiv:2607.13315v1 Announce Type: new Abstract: Unequal availability of human preference data across languages poses a significant challenge for align…
针对LLM代理的伪装MCP攻击，提出改进偏好对齐的拒绝训练方法，附RAG免训练对齐方案。
Article URL: https://github.com/johnhalloran321/mcp_safety_training Comments URL: https://news.ycombinator.com/item?id=48834106 Points: 1 # Comments: …
通过偏好对进行推理时对齐，提出高效方案避免昂贵微调
arXiv:2606.24004v1 Announce Type: cross Abstract: Steering a large language model (LLM) toward a desired behavior typically relies on an iterative pro…
面向中文大模型安全，提出细粒度护栏CHILLGuard，可扩展数据构建配合模型感知偏好对齐，为LLM安全检测提供新方案。
arXiv:2606.15396v1 Announce Type: cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and eth…
EMNLP 2025新研究：用多模态大模型提示与个性化偏好对齐，提升封面图像生成质量。
arXiv:2605.27374v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) and diffusion models (DMs) have opened new…
提出统一谱系框架，在线偏好对齐新解法，理论扎实且方法创新。
arXiv:2605.20408v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) effectively aligns Large Language Models (LLMs) with…
设备端RAG新范式：通过偏好对齐构建记忆，让个人AI代理在隐私保护下更懂你的需求。
arXiv:2605.18271v1 Announce Type: cross Abstract: With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing t…
LLM导师因偏好对齐沦为"讨好型"教师，论文揭示推理谄媚悖论，呼吁建立谄媚基准以保障教育安全。
arXiv:2605.14604v1 Announce Type: new Abstract: This position paper argues that effective tutoring requires corrective friction: surfacing misconcepti…