Consider a recommendation platform that interacts with a finite set of users in an online fashion. Users arrive at the platform and receive a recommendation.

We show how techniques from the theory of error-correcting codes can be applied to achieve scalability and hence improved performance. As an exemplar, we focus on how polar codes can be used to efficiently simulate i.i.d.

Although freely accessible, it is provided in individual files for each year, and is difficult to combine in order to analyze spatial or temporal trends.

To evaluate VLMs' fine-grained understanding, existing benchmarks primarily focus on evaluating VLMs' capability