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.
選取任何看不懂的句子，依你選的深度結合上下文講清楚。
回答只依據這篇論文的全文，不是泛泛的常識。
閱讀的同時產生一頁解讀卡，隨時匯出用於期刊討論會。
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.
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.
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.
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 turns it into a journal-ready submission figure. One place for reading papers and for publishing them.
No prompting, no setup. The paper opens in seconds and you read normally — until something stops you.
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.
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.
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.
Reading a paper is not one action. It's two hundred small moments of being stuck.
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.
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.
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.
A summarizer answers the questions it decided to answer. Reading is about the questions the paper raises in you.
Paper explainer AI, free usage, accuracy, and what you keep — answered.
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.
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.
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 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.
