Boltzbit 提出 Bayesian Self-learning Transformers，用参数更新替代外部记忆
Boltzbit 给出了智能体自我改进的第二条路：直接改权重而不是堆记忆，理论上算力省约 1000 倍，思路很值得琢磨。
Boltzbit 的 Bayesian Self-learning Transformers（BAST）选择让实时数据直接转化为参数更新，而不是靠累积文档、嵌入和摘要等外部记忆。其理论分析估算这条路径的算力需求比传统训练低约 1000 倍。两种路线的差别在于外部记忆会让每次请求检索和处理更多状态，而权重适应把有用知识移入模型本身。文中指出下一个检验标准是这些更新能否保持稳定、有选择性，并泛化到产生它们的交互之外。
There are two ways to make an agent improve.
One is to leave the model unchanged and accumulate documents, embeddings, summaries, skills, and longer histories.
The other is to let experience modify the model.
Boltzbit’s Bayesian Self-learning Transformers (BAST) investigate the second path by converting live data into targeted parameter updates. Its theoretical analysis estimates roughly 1,000× lower compute requirements than conventional training.
The economics matter. External memory makes future requests retrieve and process more state. Weight adaptation moves useful knowledge into the model itself.
The next benchmark is whether those updates remain stable, selective, and generalize beyond the interaction that produced them.
If they do, continual learning becomes part of the architecture, not another agent feature.
