Turn a paper into an in-depth reading card in Chinese—and decide whether it’s worth reading
Give it a paper PDF, arXiv link, or title and abstract, and get a structured in-depth reading card in Chinese: research question, method, experiments, strengths, limitations, reproduction challenges, and a reading-priority assessment.
What can this paper deep-reading skill do for you?
The paper deep-reading skill turns a single academic paper into a structured in-depth reading card in Chinese. It is designed for researchers in AI, NLP, and LLMs who need to quickly assess a paper’s value. You can provide a paper PDF, an arXiv link, a title and abstract, excerpts from the main text, or your own reading notes. It first assesses the level of evidence you provided—full text, partial main text, abstract only, or title only—and states the limits of its assessment at the top of the card. It then follows a fixed structure to cover the research question, core method, key model or algorithm details, experimental setup, main results, strengths, limitations, and reproduction challenges.
Where evidence is insufficient, it will not fill in the gaps: if the experimental setup, datasets, metrics, or results are not present in the input, they will be marked as unknown. Finally, it offers “implications for your current research” and assigns a reading-priority label—worth an in-depth read, worth a quick read, or can be deferred—with an explanation.
The result is a Markdown reading card with fixed fields that you can save directly to your literature library or excerpt into your related work notes. For quick screening, the same fields are condensed into a shorter version.
Who it’s for: researchers screening papers, writing related work, planning reproductions, or conducting evaluations. If the input is an English paper, the output is still Chinese by default.
Not for: it handles only one paper at a time. It does not produce multi-paper surveys or retrieve or download full-text papers.
How is this different from other ways of reading papers in depth?
Reading a paper yourself is not always the slow part; often, the challenge is first deciding whether it’s worth taking up half a day. General-purpose AI chat can answer all kinds of questions about a paper, but unless you ask, it usually won’t proactively tell you which conclusions lack evidence.
This paper deep-reading skill puts the “evidence boundary” front and center: it first states whether it saw the full text, partial main text, only the abstract, or only the title. Anything not included in the input—experimental setup, datasets, results, or open-source status—is marked unknown, rather than filled in to match your expectations. So when the full text is available, it assesses the paper based on the full text; with only an abstract, it deliberately qualifies claims about method details and experimental design.
It also turns “is this worth reading?” into a consistent label. It uses only three options—worth an in-depth read, worth a quick read, or can be deferred—and gives reasons based on fixed criteria, rather than changing its wording each time. This makes it easy to compare and rank reading cards for multiple papers.
Choose this when you need reusable, structured notes on a specific paper or need to decide whether to invest time in reading it in depth. General-purpose AI chat is better when you want to ask open-ended questions across concepts or don’t need a complete card for one paper.
Frequently asked questions about reading papers in depth
10 questions
What formats can I provide?
The paper deep-reading skill accepts PDFs, arXiv links, titles with abstracts, excerpts from the main text, and reading notes you have prepared yourself. You don’t need to put the paper into a fixed format first; provide any of the above directly. If you provide multiple sources, it will prioritize the one with the most complete coverage.
Can it do an in-depth read with only the abstract?
Yes, but its conclusions will be more cautious. With only the abstract, the paper deep-reading skill states “assessment based on the abstract only” at the top of the card and qualifies claims about method details and experimental design. It keeps the complete field structure, marking anything it cannot verify as unknown rather than making it up.
What does the result look like?
The output is a Markdown reading card in Chinese with fixed fields, covering, in order, input coverage, research question, core method, key model or algorithm details, experimental setup, main results, strengths, limitations, reproduction challenges, implications for your current research, and whether it is worth an in-depth read. You can save it directly to your literature library or excerpt parts into your related work.
Will it make up experimental results that aren’t in the paper?
No. The paper deep-reading skill explicitly requires that experimental setup, datasets, metrics, results, ablation conclusions, and open-source status be marked unknown if they are not explicitly provided in the input. It will not invent details to fill gaps. Benefits claimed by the paper itself are described as “claimed by the paper,” not treated as established facts.
How does it decide whether a paper is worth an in-depth read?
The paper deep-reading skill uses only one of three labels: worth an in-depth read, worth a quick read, or can be deferred. It bases the decision on fixed criteria, such as whether the research question is relevant to your work, whether the method is novel, and whether the experiments sufficiently support the claims. It explains the reasons alongside the label. If the input provides too little information, it tends toward a more cautious conclusion.
Can I use it with papers in English?
Yes, the language of the original paper does not matter. The paper deep-reading skill outputs a reading card in Chinese by default, so an English paper still results in Chinese notes. It does not translate the entire paper; it provides Chinese content focused on the research question, methods, and evidence needed for the reading card.
Can it process multiple papers at once?
No. The paper deep-reading skill handles one paper at a time and explains that paper within the limits of the evidence you provide. For comparisons or a survey of multiple papers, it is better to generate reading cards one paper at a time and compare them yourself, or use a dedicated survey tool.
What if I only want to screen papers quickly and don’t want a long card?
Just say that you only want a quick screening. The paper deep-reading skill will keep the same field structure but shorten each section so you can decide faster whether to keep reading. Unless you specify otherwise, it provides the full-length Chinese reading card, suitable when you need complete notes.
How is this different from asking a general-purpose AI directly?
General-purpose AI chat can answer all kinds of questions about a paper and is suited to open-ended questions about concepts. The paper deep-reading skill focuses on in-depth reading of a single paper and states whether each assessment is based on the full text, partial main text, or only the abstract. Content absent from the input is marked unknown. It is therefore better suited to creating structured, reusable notes.
What research areas is it suited to?
The paper deep-reading skill prioritizes assessing strengths, limitations, and implications directly relevant to research in AI, NLP, LLMs, Agents, RAG, and Safety. It is best for researchers screening papers, writing related work, planning reproductions, or conducting evaluations who want to produce a first-pass in-depth reading of a single paper.