Rohan Paul@rohanpaul_ai · XAI公司Trajectory推出了一个持续学习平台，旨在解决AI模型部署后无法从实际使用中改进的核心问题。该平台的核心是“轨迹”概念，它将智能体（Agent）的行为与用户后续的接受、拒绝、编辑、重试或修复反馈结合，形成完整的交互链条。公司可借此对大规模智能体模型进行持续的后训练，以同步提升模型权重、配置和提示词。该平台已与Harvey、Decagon等多家AI公司合作，部分已投入生产。其团队由来自DeepMind、OpenAI、Meta Superintelligence等机构的研究人员组成。项目获得了1500万美元融资，投资方包括Conviction、Bessemer等。
Cracking continual learning would make AI far more capable， because models could improve from real usage after deployment.
Trajectory just launched a continual learning platform， backed by a $15M round， to turn every agent trace and user correction into a system that keeps improving after deployment.
A neolab with ex-DeepMind， OpenAI， and Meta Superintelligence researchers that also has paying customers， totally normal.
AI products are still frozen software， because users correct them every day but those corrections rarely update the model， the prompts， or the surrounding agent workflow.