黑盒LLM分类推理的不确定性估计，巧用层级感知监督提升准确性。
arXiv:2608.22839v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for scientific decision support, yet reliable con…
IT之家 8 月 22 日消息， 特斯拉官网 更新了 FSD 监督版全球可用地区清单。名单中包含了 12 个国家或地区， 而 中国不在此次公示的可用范围内。 据IT之家此前报道，今年 5 月，特斯拉官方在 X 发文，当时 提到监督版 FSD 支持的国家与地区中包含中国 。 相关阅读： 《 特斯拉在北…
OpenAI启动新倡议，用工具与培训赋能政府机构，探索国家安全中AI的民主监督路径。
OpenAI launches an initiative to strengthen democratic oversight of AI in national security, supporting government institutions with tools, training, …
本地AI预先筛选医学稿件，人工双盲复核兜底，透明可控的审稿新思路
arXiv:2608.14625v1 Announce Type: cross Abstract: Academic peer review is under mounting strain: NeurIPS 2025 received 21,575 submissions, ICLR 2025 r…
分片机制如何阻断大模型监督失效与对抗攻击？这篇论文给出了关键防线设计。
arXiv:2608.06422v1 Announce Type: new Abstract: Giving an LLM judge more compute does not necessarily make it check more requirements. When one call m…
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…
自监督世界模型与几何感知策略空间结合，让智能体在复杂环境中高效自主决策。
arXiv:2608.01397v1 Announce Type: cross Abstract: World Action Models (WAMs) couple action generation with prediction of future states. Their effectiv…
像素级多模态大模型新训练范式，掩码—文本一致性循环让模型精修细节，值得细读。
arXiv:2608.01354v1 Announce Type: new Abstract: Recent studies develop pixel-level multimodal large language models (MLLMs) that support both Region S…
把物理规律嵌入自监督增强，让科学成像模型学到的特征更可解释、更稳健，是AI4Science方向值得关注的新思路。
arXiv:2607.28868v1 Announce Type: new Abstract: Data augmentations define the invariances learned by self-supervised learning (SSL). Standard augmenta…
不再丢弃大模型输出语义，用自监督生成式嵌入让LLM直接产出向量，重新定义嵌入学习。
arXiv:2603.10913v3 Announce Type: replace Abstract: Fine-tuning LLM-based text embedders via contrastive learning maps inputs and outputs into a new r…
提出一种自监督语义扩散方法，让技能像参数一样被训练与迁移，为AI技能学习开辟新思路。
arXiv:2607.27557v1 Announce Type: new Abstract: While Large Language Models (LLMs) demonstrate remarkable general instruction-following capabilities, …
工信部突查车企样车与电池，生产一致性监管再升级，质量安全底线不容挑战。
IT之家 7 月 31 日消息，工信部今日发文称，7 月 30 日至 31 日，为进一步规范汽车产业竞争秩序、提升汽车产品生产一致性和质量安全水平，工业和信息化部装备工业一司赴 奇瑞汽车股份有限公司、蔚来汽车科技（安徽）有限公司、安徽江淮汽车集团股份有限公司 开展道路机动车辆生产企业产品安全保障能力…
重新定义用户对AI输出的验证行为，不再视作不信任，而是日常认知治理的新视角
arXiv:2607.24761v1 Announce Type: cross Abstract: Research on human-AI interaction has long framed verification of system outputs as a trust-contingen…
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…
图半监督蒸馏新方法被KDD2026录用，利用文本属性提升图表示学习效率。
arXiv:2607.20477v1 Announce Type: new Abstract: {\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph top…
工信部突击检查广汽埃安、小鹏，现场随机抽样动力电池，剑指汽车产品一致性与安全底线。
IT之家 7 月 24 日消息，工信部今日发文，为进一步规范汽车产业竞争秩序、提升汽车产品生产一致性和质量安全水平，工业和信息化部装备工业一司赴 广汽埃安新能源汽车股份有限公司、肇庆小鹏汽车有限公司 开展道路机动车辆生产企业产品安全保障能力和生产一致性监督检查，重点检查企业的智能网联汽车安全保障能力…
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…
利用未标注数据提升神经群体解码的泛化能力，打通稀疏标注与海量数据的桥梁。
arXiv:2607.14086v1 Announce Type: new Abstract: Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interface…
通过合成指令数据扩展预训练规模，突破传统监督训练数据瓶颈的新方法
arXiv:2601.22146v2 Announce Type: replace-cross Abstract: Due to limited supervised training data, large language models (LLMs) are typically pre-trai…
从粗到细监督实现高效高分辨率视觉表示，SigLIP-HD在处理多模态大模型时兼顾计算效率与细粒度特征。
arXiv:2607.09488v1 Announce Type: new Abstract: High-quality visual representation is a long-standing pursuit in computer vision. In the context of mu…