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Automating daily paper recommendations involves searching arXiv and Semantic Scholar, applying multi-criteria scoring to rank candidates, and generating a structured dataset of papers with metadata for downstream note generation.

Generating Obsidian notes from research papers entails creating a daily file containing a prioritized list, an overview, detailed notes for the top 3 papers, integrated images, and wikilinks to existing reports.

Multi-criteria scoring for academic papers evaluates candidates based on relevance, recency, popularity, and quality metrics to produce a ranked set of research papers matching your specified interests.

Configurable language output supports multi-language research reports in both zh and en, allowing you to generate daily paper recommendations and structured notes in your preferred language.

Linking new research papers to an Obsidian knowledge graph uses automatic keyword linking to connect new papers with existing notes, weaving them into your current knowledge structure via wikilinks.

Searching Semantic Scholar hot papers and arXiv latest papers together requires a two-source search workflow that queries arXiv for the latest month and Semantic Scholar for the past year, then ranks the combined results.