• Explored the options for external search engines. Most of the options can't be used because our use case might not comply with the law or Terms of Service.
• Created and initial pipeline to search for candidates using Wikipedia's internal search. The results will be served in the format of:

{
    "title": <"title">,
    "snippet": <"snippet">,
    "pageid": <"pageid">,
}

• Explored the possible logic for paragraph reranking for the selected candidates' pages. I recommend proceeding with pretrained crossencoder models. In particular, I tested the pipeline based on jina-reranker (pipeline notebook with usage example). Potentially, we can improve performance with better models, such as Qwen3-Reranker, but I currently face infrastructure constraints.

Implemented the search results selection logic with the following workflow:

• Collect search results from the search engine at the page level (currently limited to Wikipedia; this should be extended to additional sources to avoid selection bias).
• Compute ranking scores for all paragraphs within the retrieved pages using a cross-encoder reranker model.
• Select the top five ranked paragraphs, with a maximum of two paragraphs per source page.