7月16日，小米发布面向真实移动操作的具身基底模型Xiaomi-Robotics-1。该模型基于10万小时真实世界数据预训练，结合跨本体后训练，突破传统机器人策略的硬件依赖与数据规模瓶颈；预训练阶段引入UMI（通用操作接口），标志着小米在具身智能领域系统化推进规模法则（Scaling Law）。
谷歌“AI代码突击队”成立不足两月即重组，DeepMind团队职责从编码工具与智能代理优化扩展至模型“中期训练”，即预训练后、指令对齐与微调前的关键阶段，旨在缩小与Anthropic在生成式AI编程领域的差距。
MiniMax M2系列模型在输出“马嘉祺”时出现异常，原因是后训练阶段导致低频Token退化。排查发现，分词器将“马嘉祺”切分为[马，嘉祺]，“嘉祺”因预训练高频被合并为独立Token（编号190467），但在后续训练中该Token被“挤压”，引发输出错误。官方已发布技术报告，揭示了这一机制。
Anthropic推出"个性向量"新技术，可监测和调节AI模型的特定个性特征。该技术通过分析神经活动模式，能识别并控制如"邪恶"、"阿谀奉承"等不良特征，既可在训练阶段预防问题，也能在部署后修正模型行为。研究人员将这一过程比作"为模型接种疫苗"，通过适量接触不良特征增强抵抗力。技术还能筛查训练数据中的潜在问题，但可能轻微影响模型智能水平。目前已在真实数据集测试中展现效果。
Chatglm
$2
Input tokens/M
$8
Output tokens/M
128
Context Length
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