About

AI research now moves faster than most practitioners can realistically track. Thousands of new papers appear every week across arXiv, research labs, and conferences, making it increasingly difficult to separate genuinely important work from background noise.

Takara TLDR was built to turn that volume into a concise, practical briefing for engineers, founders, and researchers who need to stay current without spending hours reading papers every day.

The project began as a simple API that surfaced a small number of new papers daily. That helped, but abstracts still took too long to skim and rarely provided enough context to judge what truly mattered. TLDR evolved from that limitation into a full curation and summarisation pipeline designed not just to find new research, but to identify the work worth attention and explain it clearly.

The aim is straightforward: surface the small number of papers worth understanding, rather than asking people to sort through everything themselves.

Each day, TLDR scans thousands of newly released research papers and embeds them using DS1, Takara's high-performance embedding model.

Those embeddings are then compared against a curated reference set that reflects the kinds of research practitioners are most likely to care about. This makes it possible to identify the strongest candidates from the daily paper stream quickly and at scale.

A diversification stage then narrows the selection further, reducing duplicates, thematic overlap, and short-lived hype clusters. The result is a briefing that captures genuinely distinct ideas rather than multiple versions of the same trend.

This pipeline allows TLDR to review thousands of papers each day while surfacing only the research most likely to matter in practice.