Autonomous Research Intelligence
Omploy reads the papers you import — and nothing else. No training-data recall, no confident invention, no citation you have to go and verify at two in the morning and obviously no hallucinations.
Do any of these papers evaluate across more than one dataset?
Two of the twelve do. Ahmed et al. evaluate on both benchmarks and report a six-point macro-F1 drop on the held-out test split1. Novak and Lindqvist report a second-dataset result, but their full text is paywalled — that claim rests on the abstract alone2. The remaining ten report a single dataset each.
“Transferred to the held-out test split, macro-F1 falls from 0.81 to 0.75.”
“We replicate the effect on a second, independently collected corpus.”
What does the literature say about cost at production scale?
The sources do not cover this. None of the twelve papers reports inference cost, hardware, or latency at deployment. Import something that does and ask again.
Illustration · papers and quotations are examples
Different deadlines, same moment: someone reads what you wrote and asks where it came from.
Your first literature review, and no idea how to hold twenty papers in your head at once.
Every paper read into the same shape, so you can see where they agree and where they don’t before writing a word.
A dissertation chapter due, a reading pile that keeps growing, and a supervisor who checks sources.
Ask the whole pile a question and get the passage back alongside the answer — ready for the moment they ask.
Sixty papers, a gap you have to prove is real, and prose that has to survive a viva.
A gap you can evidence, and a related-work section where every sentence traces to something you imported.
Situating a contribution against recent work, quickly, without re-verifying a single claim.
A grounded draft in your own register, in an afternoon rather than a fortnight.
That is the wrong objective for a literature review. A fabricated citation costs more than no answer at all — it costs the hours spent finding out it was fabricated, and the credibility of everything standing next to it.
Scope is fixed by architecture, not by asking a model to behave. The draft assistant sees the papers linked to that draft — not the rest of your project, not the open web, not its own training data.
Where the sources do not support a claim, it says so and names what is missing. An absent result comes back as “not stated in the available text”, not as a plausible sentence you have to catch.
Analyses store verbatim quotes from the paper beside each section. You open the passage and check it yourself. The citation is a span of text, not a claim about one.
Search by DOI, title or keyword across public directories — or drop in a PDF. Title, authors and abstract are read off the document.
Each paper is analyzed into distinct fields — method, datasets, results, limitations. Consistent enough to compare sixty of them side by side.
Ask about one paper, about the sources behind one draft, or across the whole project. Answers arrive with the passages they rest on.
Draft in an editor. Generate a grounded section, rewrite a passage, compare versions — and watch it learn to write in your own voice.
A literature review is roughly twenty-five papers and eighty questions — the plans are sized against that.
$0/mo
See it read your library.
$8/mo
A full literature review.
$15/mo
A 60-paper thesis chapter.
$45/mo
Multiple projects, all year.