CLArXivR

In some fields the arXiv now sees hundreds of new preprints every day. No working researcher can read all of them, and most existing tools stop at recommendation — surfacing a ranked list of papers that look superficially related to what you’ve read before. That helps with discovery, but it doesn’t solve the harder problem: actually reading enough of each paper to know whether and how it matters for your work.

CLArXivR adds a comprehension layer on top of recommendation. For each new preprint, the system reads the paper in the context of the researcher’s own work and produces a contextual summary that answers questions an expert actually cares about — does this overlap with what I’m doing? does it build on it, contradict it, or extend it in a direction worth following up? The goal is to skip past generic abstracts and deliver the kind of read you’d want from a thoughtful colleague who knows your work.

Once you commit to summarising at that level of detail, the load-bearing properties shift. Faithfulness (the system doesn’t say things the paper didn’t) and relevance (the summary highlights what this researcher needs, not generic findings) become the things to design and engineer around — and most of the technical effort goes into the evaluation frameworks needed to measure both, and the iteration loops that let them improve together rather than at each other’s expense.

Coming soon — currently in private alpha. If you work in a field with a high preprint volume and would like early access, get in touch.