The text discusses the implementation of fix actions in a new streaming architecture designed to reduce latency and cost in validation processes. Fix actions allow for automatic corrections of faulty language model outputs, such as detecting personal information or adjusting text case, and are particularly useful in ensuring compliant responses. However, in a streaming context where validators accumulate data independently based on different thresholds, running them sequentially poses challenges. The solution involves accumulating enough data for each validator to output a fix value, followed by a merging algorithm that combines these outputs into a cohesive final result using a modified version of Google's diff-match-patch algorithm. Although effective, this approach can sometimes create issues when replacement ranges overlap, and users are encouraged to report bugs. The text also highlights related resources and advancements in AI safety frameworks, including new open-source validators for personal information detection and jailbreak prevention.