Research is not just reading papers. It is holding a dozen of them in your head at once: what each one contributed, how they build on each other, where they agree and disagree, and how to write that up with citations a reviewer will trust. For a student entering a field or a researcher scoping a project, that synthesis is the slow, hard part.
Sharper turns a stack of papers into a knowledge base you can question. Upload the PDFs and you can summarize any paper, explore the concepts that connect them, trace how an idea evolved across the literature, compare methods side by side, and generate a related-work section - with every claim grounded in the source papers and linked to the passage it came from.
What is knowledge base question answering for research? It lets you upload a set of papers as one knowledge base, then ask natural-language questions across them - for summaries, comparisons, and synthesis - and get answers grounded in the papers, with source passages cited so you can verify and cite them yourself.
See it for yourself. Watch the full session replay to follow every question and answer end to end. Want to try it? Download the sample papers - all ten used here - and build the knowledge base yourself.
The setup: ten foundational papers, one field
The knowledge base holds ten landmark papers that trace the modern history of transformers, language models, and neural retrieval - the kind of reading list a new researcher in the field has to work through:
- Architecture and language models: Attention Is All You Need, BERT, RoBERTa, DistilBERT, GPT-3, LLaMA
- Dense retrieval and embeddings: Sentence-BERT, DPR, ColBERT, ANCE
Two connected research threads, ten dense PDFs. Read cover to cover, that is days of work before you can even start synthesizing.
Step 1 - Upload the papers to build the knowledge base
The researcher uploads all ten PDFs into a single knowledge base. Sharper parses and indexes each paper - equations, tables, and all - and prepares the set for question answering.
One knowledge base holds the whole reading list, indexed and ready to question.
How Sharper works, in two moves. First, you build a knowledge base: upload your documents once and Sharper parses and indexes them into a reusable, searchable collection. Second, you ask: start a new task, select that knowledge base, and ask any question in plain English. Sharper searches only the selected knowledge base and answers from it, so every response is grounded in your papers - and cites the passage behind each claim.
Step 2 - Understand any paper fast
Start where every researcher starts: getting the gist of a single paper without reading all thirty pages first.
The question: "Summarize the key contribution, method, and main results of Attention Is All You Need."
A structured summary - contribution, method, results - with a citation on every point. The Transformer's encoder-decoder, scaled dot-product and multi-head attention, and the 28.4 / 41.8 BLEU WMT 2014 results, each linked back to the paper.
This is the difference from a generic summary: every claim carries a citation chip that opens the exact passage in the source PDF, so you can trust it and quote it.
Step 3 - See the whole landscape
Papers do not exist in isolation, and neither should your understanding of them. Without tagging anything, Sharper extracts the concepts that run across the corpus and maps how they connect.
Sharper auto-extracts the concepts across all ten papers - here, 40 concepts drawn from the 10 sources - each with a summary and the papers that mention it.
The knowledge graph turns those connections into a map: which papers share a concept, where a method originates, and how the field fits together.
The knowledge graph links papers and concepts - a fast way to see how ColBERT, late interaction, and the surrounding ideas connect before you read a word.
For a newcomer, this is the fastest way to build a mental map of a field before diving into any single paper.
Step 4 - Synthesize across papers
This is where Sharper earns its place in a research workflow. The hard questions are not about one paper - they are about the arc across many.
Trace how an idea evolved
The question: "Trace how language-model pretraining evolved from BERT to RoBERTa to GPT-3 to LLaMA - what changed at each step?"
The arc across four papers: BERT's bidirectional MLM, RoBERTa's better optimization, GPT-3's scale and in-context few-shot learning, and LLaMA's efficient, open, data-over-parameter scaling - synthesized into one throughline.
Compare methods side by side
The question: "Compare the dense-retrieval methods in DPR, ColBERT, and ANCE - how does each represent and match passages, and what benchmark results does each report?"
A cross-paper comparison: DPR's single-vector dual encoder, ColBERT's token-level late interaction, and ANCE's contrastive negatives - with cited benchmark numbers like ANCE's 0.330 MRR@10 on MS MARCO versus DPR's 0.311.
Answering either question by hand means holding several papers open at once and reconciling their notation and numbers. Sharper does it in one response, and cites every figure.
Step 5 - Read critically
Good research reading is not just extraction - it is knowing what a paper admits about itself. Ask Sharper for the caveats and it pulls them straight from the text.
The question: "What limitations does the GPT-3 paper acknowledge?"
Sharper surfaces the paper's own stated limitations - capability gaps, architectural and objective constraints, bias (Section 6.2), and data contamination (Section 4) - grouped and cited, so you can weigh the work honestly.
Step 6 - Generate a cited related-work section
The payoff for a research project: turning all of that into writing you can actually use.
The request: "Write a related-work section on neural information retrieval covering Sentence-BERT, DPR, ColBERT, and ANCE, with citations to these papers."
A structured, cited related-work section - an overview that frames the trajectory, then a paragraph per method with the numbers and claims cited to each paper - saved as a document you can drop into your draft.
The section is not a generic overview. It frames the four works around a throughline, pulls the specific figures from each paper (BERT's cross-encoder cost, Sentence-BERT's siamese pooling), and cites them - saved as a file you can export into your own draft and refine.
What makes Sharper different for research
- Grounded and citable. Every claim links to the exact passage in the source paper, so you can verify it and cite it - not trust a black box.
- Structure without tagging. Upload the papers and Sharper builds the concepts and knowledge graph automatically, so you can see the field before you read it.
- Synthesis, not just lookup. Sharper traces how ideas evolved and compares methods across papers, which is the actual work of a literature review.
- Reads critically. It surfaces a paper's own stated limitations, so your reading is honest, not just favorable.
- Produces the deliverable. It writes a cited related-work section you can export into your paper.
The time saved
Reading ten papers, mapping how they connect, comparing their methods, and drafting a related-work section is a week of a researcher's time. With Sharper it is an afternoon of questions - and the output is grounded in the papers, cited, and ready to fold into your draft. You spend your time on the judgment that matters: what to include, what to challenge, and what to build next.
Try it yourself
Watch the full session replay to see every question answered end to end, or download the ten sample papers, build the knowledge base in Sharper, and ask the questions above yourself.
FAQ
What kinds of documents can Sharper use for research?
Research papers, technical reports, theses, textbooks, and other PDFs. Sharper parses tables, figures, and structured content - not just plain text - and builds one knowledge base you can question across.
Does Sharper cite its sources?
Yes. Every claim in an answer links to the exact passage in the source paper, so you can verify it and cite it in your own work.
Can Sharper compare and synthesize across multiple papers?
Yes. Beyond summarizing one paper, Sharper traces how an idea evolved across the literature and compares methods side by side, with each figure cited to its source.
Can Sharper write a related-work or literature-review section?
Yes. Ask for a related-work section on a set of papers and Sharper drafts a structured, cited section grounded in those papers, saved as a document you can export into your draft.
How many papers can a knowledge base hold?
Sharper is built for large collections - from a focused reading list of ten papers to knowledge bases with thousands of documents - while still returning grounded, cited answers.