没有外部监督的在线自蒸馏方案，为LLM后训练省去人工标注与奖励模型依赖。
arXiv:2608.06296v1 Announce Type: new Abstract: On-policy (Self-)Distillation (OPD / OPSD) has shown strong potential for post-training large language…
提出Panda无监督实时盆腔MR异常检测方法，无需标注数据即可高效筛查病变，为医学影像AI落地提供新思路。
arXiv:2607.24703v1 Announce Type: new Abstract: Female pelvic diseases remain an under researched area characterized by often delayed diagnosis. While…
无监督域对齐新方法，跨模态迁移助力医学影像分析。
arXiv:2607.21546v1 Announce Type: new Abstract: Multimodal based approaches often outperform single modality approaches in downstream tasks as the dif…
利用激活几何进行无监督特征挖掘，无需人类标注即可揭示大模型内部表征
arXiv:2607.04222v1 Announce Type: new Abstract: Interpretability methods aim to reveal the features represented inside large language models (LLMs). M…
不用人工标注，模型也能学会自然图像的左右语义，像素级理解新思路。
arXiv:2607.05006v1 Announce Type: new Abstract: While various works address reflective symmetry understanding in 3D data and images, pixel-level seman…
无需人工标注，通过神经元激活模式筛选数据，实现LLM高效自蒸馏训练。
arXiv:2607.02460v1 Announce Type: cross Abstract: Post-training large language models (LLMs) without real-world interaction feedback or human-labeled …
AI写代码？别被“100%由AI生成”忽悠了，关键在于有没有人盯着。
Article URL: https://www.tommyjepsen.com/blog/supervised-vs-unsupervised-ai-code Comments URL: https://news.ycombinator.com/item?id=48743411 Points: 1…
不用标注数据也能精准识别漏洞？ANVIL利用LLM的异常检测新范式，突破监督学习局限
arXiv:2408.16028v4 Announce Type: replace-cross Abstract: Supervised-learning-based vulnerability detectors often fall short due to limited labelled t…
首次验证无标准答案的强化学习也能提升LLM，颠覆传统监督范式。
arXiv:2606.27369v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) for training LLMs typically rely on ground-truth…
混合LLM代理实现通用指南驱动的图像聚类，突破传统方法对特定数据的依赖。
arXiv:2606.24094v1 Announce Type: new Abstract: Unifying image clustering across different clustering scenarios remains challenging due to fundamental…
无需真实标签，通过配对轨迹审计实现技能演化，为智能体能力进化提供新范式
arXiv:2606.14239v1 Announce Type: new Abstract: Agent skills are structured procedural packages that guide frozen LLM agents in specialized workflows.…
挑战无监督异常检测中任务特定训练的必要性，揭示分布偏移下重建残差评分的局限性
arXiv:2601.22763v3 Announce Type: replace Abstract: Current state-of-the-art multi-class unsupervised anomaly detection (MUAD) methods rely on trainin…
用改写反转来无监督学习文本风格，精准识别AI生成内容，方法新颖且实用。
arXiv:2606.10099v1 Announce Type: cross Abstract: The rapid development of large language models (LLMs) has raised concerns about misuse such as plagi…
用无监督学习揭开亨廷顿病分期之谜，模型表征与聚类分析解读疾病进展规律
arXiv:2606.07135v1 Announce Type: new Abstract: Huntington's disease (HD) is a progressive neurodegenerative disorder that affects motor, cognitive, a…
挑战无监督单词发现中传统评价指标，提出新视角下的词典评估方法。
arXiv:2606.06183v1 Announce Type: cross Abstract: Building a lexicon from discovered word-like units is a central goal in zero-resource speech process…
新方法让AI智能体通过自我偏好优化轨迹，无需人工标注数据即可持续提升能力，为Agent自我进化开辟新思路。
arXiv:2606.05922v1 Announce Type: new Abstract: AI agents rely on a harness of skills, tools, and workflows to solve complex problems. Continually imp…
利用掩码扩散模型实现高效异常检测，创新结合生成与判别任务，适用于图像异常定位。
arXiv:2605.30046v1 Announce Type: cross Abstract: Anomaly detection aims to identify samples that deviate from the nominal data distribution and is ce…
自监督验证机制让大模型推测解码无需额外训练，实现2-3倍推理速度提升，架构简洁高效。
arXiv:2510.02329v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates LLM inference by verifying candidate tokens from a draft mo…
LLM在图数据标注中的失败模式，揭示无标签学习新路径
arXiv:2605.27913v1 Announce Type: new Abstract: Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is exp…