AI Paper Explainer: Read the Paper, Not a Summary of It
Open any research paper and read it with an expert sitting next to you. Nothing scrolls away — every answer stays on the line that raised it, in your own language. Free to try, no credit card needed.
The sentence that stopped you, explained in place
This is the product itself: the paper on the left, the explanation beside it. Select anything you can't follow — the answer takes the surrounding page into account, and stays anchored to the line that raised it.
1. Introduction
Deep convolutional neural networks [22, 21] have led to a series of breakthroughs for image classification [21, 50, 40]. Deep networks naturally integrate low/mid/high-level features [50] and classifiers in an end-to-end multi-layer fashion, and the “levels” of features can be enriched by the number of stacked layers (depth). Recent evidence [41, 44] reveals that network depth is of crucial importance, and the leading results [41, 44, 13, 16] on the challenging ImageNet dataset [36] all exploit “very deep” [41] models, with a depth of sixteen [41] to thirty [16]. Many other non-trivial visual recognition tasks [8, 12, 7, 32, 27] have also greatly benefited from very deep models. Driven by the significance of depth, a question arises: Is learning better networks as easy as stacking more layers? An obstacle to answering this question was the notorious problem of vanishing/exploding gradients [1, 9], which hamper convergence from the beginning.
1
This is the authors' rhetorical setup for the core problem they are solving: naively adding more layers does not improve networks, and they want to explain why.
The intuition that "deeper = better" was well-supported at the time (VGG, GoogleNet, etc. all gained accuracy by going deeper). But in practice, simply stacking more layers caused networks to become harder to optimize — not because of overfitting, but because of degradation: as shown in Figure 1 on that same page, the 56-layer plain network has higher training error than the 20-layer one. The question primes the reader to recognize that depth alone isn't the answer, motivating the residual (shortcut connection) framework they then introduce as the actual solution.
Real, unedited output — from “Deep Residual Learning for Image Recognition” (He et al., CVPR 2016). Note that it cites Figure 1 on the same page: the explanation reads the surrounding context, not just the sentence you selected.
And a summary card writes itself
You never have to ask for it. While you read, a one-page card is built in the background — TL;DR, method, key findings with the real numbers, and the limitation. This one came from “Attention Is All You Need” (Vaswani et al., NeurIPS 2017), with no manual edits.
Export it in 16:9 for your journal club slides. It's a by-product of reading, not a replacement for it.
Understanding first, figures second
Most AI reading tools hand you a summary and leave you to face the methods section alone. This one stays with you inside the PDF, sentence by sentence. And once you do understand a paper — or once you're writing your own — our graphical abstract maker turns it into a journal-ready submission figure. One place for reading papers and for publishing them.
How the paper explainer works
No prompting, no setup. The paper opens in seconds and you read normally — until something stops you.
Open a paper PDF
Drop in any journal article, conference paper, or preprint up to 100MB — yours or anyone's, in any language. It renders immediately; you don't wait for an AI pass before you can start reading.
Highlight what stops you
Select the sentence, the equation, or the term you can't follow. The explanation appears beside it, written from the surrounding context of this paper — at the depth you pick, in the language you read in.
Ask the paper anything
Why that control group? Does the discussion match Table 3? Questions are answered from the full text of this paper, with the section named so you can go check it yourself.
Built for how researchers actually read
Reading a paper is not one action. It's two hundred small moments of being stuck.
In-context explanations
The explanation comes from the passage you highlighted plus the text around it — not from what a model happens to know about the topic. Notation is explained as this paper defines it.
Three depths, one click
The same sentence explained for someone outside the field, for a grad student new to the subarea, or for a peer who just wants the technical substance. Switch mid-paper as the sections get harder.
Read in English, understand in your language
Explanations come back in your own language while gene names, datasets, and math notation stay verbatim. You keep reading the original — you just stop losing time to jargon.
Every highlight stays anchored
Answers don't vanish into a chat scroll. Each one keeps the sentence that prompted it and its page number; click it and you jump straight back to that line in the PDF.
Your reading, saved
Close the tab and come back tomorrow: every highlight, explanation, and question is exactly where you left it. After a semester you have a marked-up library, not a folder of PDFs.
A summary card, for free
A one-page card with the TL;DR, method, findings, and limitation is generated while you read — export it as a PNG for your journal club without writing slides.
A summary you're handed vs. a paper you read
A summarizer answers the questions it decided to answer. Reading is about the questions the paper raises in you.
AI summarizers and chat-with-PDF
- You leave the paper and read a paraphrase of it
- The one sentence you were stuck on is the one that got compressed away
- Answers pile up in a scroll with no link back to the text
- Every question means re-describing the context you're looking at
- Nothing to show for it once the tab closes
With the paper explainer
- You stay in the original PDF, start to finish
- Highlight exactly the sentence that stopped you
- Every answer is anchored to its line and page
- Follow-ups keep the passage — no re-explaining yourself
- Highlights, questions, and a summary card all saved to your library
FAQ
Paper explainer AI, free usage, accuracy, and what you keep — answered.
Open the paper you're stuck on
Upload the PDF you have to present this week. Highlight the parts you can't follow, ask the questions you'd be embarrassed to ask, and leave with a summary card. Free to try.