How can I understand an AI paper if my math is not advanced?

Pass one: decide whether the paper deserves more time

Read the title, abstract, first part of the introduction, main method figure, result-table headings, and conclusion. Write four sentences: the problem, the change, the strongest evidence, and the stated limitation.

If you cannot write them, do not immediately attack the equations. Check whether the paper assumes an earlier work you have not seen.

Pass two: turn the paper into a data flow

Trace input, transformations, output, training loss, and inference-time changes. Give each module a functional name such as select relevant information, compress a representation, or predict the next state.

Then read the setup, baselines, main result, and ablations. The experiments must isolate the change the authors claim matters.

Pass three: learn only the decisive mathematics first

Mark equations that recur, connect modules, or directly define the loss. Check tensor shape, input, output, and edge cases before attempting a derivation.

For the Transformer, understand where Q, K, and V come from and how attention selects information before studying the scaling factor and matrix derivation.

When you get stuck, identify whether you need a map, a local explanation, or an evidence check

If you still cannot state the central claim, the missing piece is the paper map: return to the abstract, main figure, conclusion, and decisive result table. If one term, paragraph, or equation blocks the flow, use a local explanation and require definitions, inputs, outputs, and the exact source location. One local gap is not a reason to summarize the whole paper again.

If the real concern is that the experiments do not establish the claim, stop looking for a smoother explanation. Inspect the dataset, comparison baseline, main table, ablations, and cited primary source directly. The finish line is not a fluent tool response; it is being able to close the tool and state the claim, strongest evidence, and main limitation yourself.

• Cannot state the claim: build the paper map first.
• Blocked by one passage or equation: use a local explanation.
• Missing a prerequisite: learn the smallest necessary concept, then return.
• Evidence feels weak: check tables, baselines, ablations, and primary citations.

Do not let a citation parking lot become a second reading list

When an appendix, term, or citation looks worth chasing, record why it might change your judgment of the current paper instead of opening it during the same pass. Cap the parking lot at three questions. After the pass, keep only questions whose answers could change the central claim, strongest evidence, or main limitation; delete the rest.

This stop rule is for completing a focused read of one paper. It is not a literature-search protocol for a systematic review, evidence synthesis, or citation due diligence. Those tasks need predefined database queries, inclusion criteria, and stopping rules rather than a three-question cap.

• Finish the current pass before opening a new citation.
• For every parked item, write which judgment it could change.
• Keep at most three; remove items that cannot change the claim, evidence, or limitation.
• Use a reproducible retrieval and screening protocol for a systematic review.

Use counterfactual questions to test understanding

What happens if the module is removed, replaced with the previous method, or trained with less data? Which experiment answers that?

If you can only repeat the abstract but cannot name a failure condition or evidence boundary, your model of the paper is still incomplete.