[D] How does Gemini 1.5 Pro recall information in 10M context?

Google recently issued [technical report](https://arxiv.org/ftp/arxiv/papers/2403/2403.05530.pdf) about Gemini 1.5 Pro and its 10M context. They gave brief overview of modern approaches to improving the long-context capabilities of models (e.g. reccurent memory, ring attention, novel architectures, etc.), but I didn't find information about Gemini 1.5 Pro approach.

Did someone notice info about their approach in official publications? It seems to be Google's secret for me.

google machinelearning publications secret