Four Types of Questions Worth Asking AI When Reading a Paper

When reading papers with AI, prioritize questions whose answers can be checked against the original. First establish what the paper says, then discuss broader analysis. Ask AI to distinguish source evidence from its own conjectures rather than attributing those conjectures to the authors.

In EasyLaTeX, start an AI conversation on the literature reading page for an external paper. To analyze a paper you are writing, ask within the corresponding project and have AI read the relevant files. Identify the paper and the available material first. If you only want analysis, explicitly say, “Do not change the text.” If relevant pages or files have not been read, provide the missing material or ask AI to continue reading instead of filling gaps with general knowledge.

Start with facts, then ask about method choices and the conditions behind conclusions, and finally verify the sources. These steps give four useful types of questions.

Type 1: Facts and symbol definitions in the original

For an unfamiliar paper, do not begin by asking, “What makes this paper impressive?” That can steer the conversation away from the evidence too early. Start with specifics: “What exactly does the assumption in Section 3 say?” or “What does $\eta$ represent in this paper?”

Asking tip: Require a source location each time: section, equation, or table number. This helps expose interpretations based on convention rather than the paper. If the authors define $\eta$ as a fixed step size but AI guesses “learning rate” from familiar usage, requiring the original definition makes that guess easier to catch.

Type 2: Method choices and alternatives discussed by the authors

Instead of “Is this decaying step size good?”, ask: “Why do the authors use a decaying step size? Do they discuss alternatives they chose not to use?” A broad evaluation invites vague approval or a noncommittal answer. Asking about the choice and its alternatives directs attention to the motivation and comparison experiments, producing information you can verify.

Asking tip: Request the symbol definition and its source in the same sentence to make checking easier.

If the paper does not discuss alternatives, the answer should say, “No explicit discussion was found in the source.” AI's inferred motivations must not be presented as the authors' conclusions. If you want extended analysis, label it separately as conjecture.

Type 3: Conditions and assumptions behind a conclusion

Once the basic symbols and concepts are clear, ask about boundaries: “Would this conclusion still hold if this assumption were removed? Which assumption in the paper does it depend on?”

First ask AI to locate the original assumptions supporting the conclusion, then discuss what might happen without them. If the paper does not analyze that case, it should acknowledge that the source alone does not settle the question. Finding “Assumption 1” does not prove that removing it necessarily invalidates the result. Missing citations may also mean a page was not read or an equation was parsed incompletely, so check the material before drawing conclusions.

Three kinds of judgment not to hand over to AI

1. Deciding the paper's value for you: “Is this worth reading?” or “Is this good enough for a top conference?”

2. Predicting reviewers' opinions: “What will reviewers criticize?”

3. Making your research decisions: “Can I directly reuse this method as my research topic?”

These questions have no definitive answer within the paper itself. AI can produce confident, professional-sounding claims that you cannot verify. The judgment remains yours; AI's role is to help assemble the evidence you need to make it.

Type 4: Source verification and missing material

On the literature reading page, check the PDF's sections, pages, equations, and tables. In a project, check source paths, labels, and short quotations. If a location does not match, ask: “Reread that passage and give its actual location. Mark anything without evidence as unconfirmed.” Then check these possibilities in order:

1. Was the material read completely and parsed correctly? Scanned pages, equations, or the middle of a long document may not have been read properly. That gap does not establish that the authors omitted the information.
2. Did AI misunderstand or mislocate the passage? Return to the original instead of accepting a fluent explanation as evidence.
3. Is the source itself unclear? Record that only after checking the material, as a lead for further reading or revision—not a prediction of a reviewer's verdict.

Summary

• Facts before follow-ups: Start with material that can be restated from the source to build a shared basis for later questions.
• Answers with citations: For equations, symbols, assumptions, and conclusions, require section or equation references on each turn.
• Ask about conditions, not verdicts: Investigate applicability and alternatives instead of outsourcing a judgment of the paper's quality.
• Check missing material: When citations fail, first establish reading coverage, then distinguish misunderstanding from unclear source wording.

Open EasyLaTeX and use this sequence on the literature reading page or in the relevant paper project. Make every round leave behind evidence you can check.